crime mapping principles & practice

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U.S. Department of Justice

Office of Justice Programs

National Institute of Justice

MAPPING CRIMEMAPPING CRIMEPRINCIPLE AND PRACTICE

U.S. Department of JusticeOffice of Justice Programs810 Seventh Street N.W.Washington, DC 20531

Janet RenoAttorney General

Daniel MarcusActing Associate Attorney General

Laurie RobinsonAssistant Attorney General

Noël BrennanDeputy Assistant Attorney General

Jeremy TravisDirector, National Institute of Justice

Office of Justice Programs National Institute of JusticeWorld Wide Web Site World Wide Web Site http://www.ojp.usdoj.gov http://www.ojp.usdoj.gov/nij

About the National Institute of Justice

The National Institute of Justice (NIJ), a component of the Office of Justice Programs, is the research agency of the U.S. Department of Justice. Created by the Omnibus Crime Control and Safe Streets Act of 1968, as amended,NIJ is authorized to support research, evaluation, and demonstration programs, development of technology, and both national and international information dissemination. Specific mandates of the Act direct NIJ to:

● Sponsor special projects, and research and development programs, that will improve and strengthen the criminal justice system and reduce or prevent crime.

● Conduct national demonstration projects that employ innovative or promising approaches for improving criminal justice.

● Develop new technologies to fight crime and improve criminal justice.

● Evaluate the effectiveness of criminal justice programs and identify programs that promise to be successful if continued or repeated.

● Recommend actions that can be taken by Federal, State, and local governments as well as by private organizationsto improve criminal justice.

● Carry out research on criminal behavior.

● Develop new methods of crime prevention and reduction of crime and delinquency.

In recent years, NIJ has greatly expanded its initiatives, the result of the Violent Crime Control and Law EnforcementAct of 1994 (the Crime Act), partnerships with other Federal agencies and private foundations, advances in technology, and a new international focus. Some examples of these new initiatives:

● New research and evaluation are exploring key issues in community policing, violence against women, sentencingreforms, and specialized courts such as drug courts.

● Dual-use technologies are being developed to support national defense and local law enforcement needs.

● The causes, treatment, and prevention of violence against women and violence within the family are being investigated in cooperation with several agencies of the U.S. Department of Health and Human Services.

● NIJ’s links with the international community are being strengthened through membership in the United Nations network of criminological institutes; participation in developing the U.N. Criminal Justice Information Network;initiation of UNOJUST (U.N. Online Justice Clearinghouse), which electronically links the institutes to the U.N. network; and establishment of an NIJ International Center.

● The NIJ-administered criminal justice information clearinghouse, the world’s largest, has improved its online capability.

● The Institute’s Drug Use Forecasting (DUF) program has been expanded and enhanced. Renamed ADAM (Arrestee Drug Abuse Monitoring), the program will increase the number of drug-testing sites, and its role as a “platform” for studying drug-related crime will grow.

● NIJ’s new Crime Mapping Research Center will provide training in computer mapping technology, collect andarchive geocoded crime data, and develop analytic software.

● The Institute’s program of intramural research has been expanded and enhanced.

The Institute Director, who is appointed by the President and confirmed by the Senate, establishes the Institute’s objectives, guided by the priorities of the Office of Justice Programs, the Department of Justice, and the needs of the criminal justice field. The Institute actively solicits the views of criminal justice professionals and researchers in the continuing search for answers that inform public policymaking in crime and justice.


Office of Justice Programs

National Institute of Justice

Washington, DC 20531

Official Business

Penalty for Private Use $300

SPECIAL STANDARD MAILPOSTAGE & FEES PAID

DOJ/NIJPERMIT NO. G–91

Mapping Crime: Principle and PracticeResearch Report

U.S. Department of JusticeOffice of Justice Programs810 Seventh Street N.W.Washington, DC 20531

Janet RenoAttorney General

Daniel MarcusActing Associate Attorney General

Laurie RobinsonAssistant Attorney General

Noël BrennanDeputy Assistant Attorney General

Jeremy TravisDirector, National Institute of Justice

Office of Justice Programs National Institute of JusticeWorld Wide Web Site World Wide Web Site http://www.ojp.usdoj.gov http://www.ojp.usdoj.gov/nij

Mapping Crime: Principle and Practice

Keith Harries, Ph.D.

December 1999NCJ 178919

Jeremy TravisDirector

Nancy La VigneProgram Monitor

This project was supported by Grant No. 98–LB–VX–0009 awarded by the National Institute ofJustice, Office of Justice Programs, U.S. Department of Justice. Points of view in this documentare those of the author and do not necessarily represent the official position or policies of the U.S.Department of Justice.

The National Institute of Justice is a component of the Office of Justice Programs, which also includes theBureau of Justice Assistance, the Bureau of Justice Statistics, the Office of Juvenile Justice and DelinquencyPrevention, and the Office for Victims of Crime.

Cover CreditsClockwise, starting with upper left corner:

Map by Lodha and Verma, 1999

Map by Jeff Stith and the Wilson, North Carolina, Police Department

Map by Wilpen Gorr, Heinz School of Public Policy andManagement, Carnegie Mellon University, Pittsburgh, Pennsylvania

Map by Susan Wernicke, Overland Park, Kansas, Police Department

Map by ASDI Technologies, Colorado Springs, Colorado

Map by Jeff Stith and the Wilson, North Carolina, Police Department

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AcknowledgmentsWithout the generous cooperation ofmany people in various settings, includingpolice departments and universities, thevariety of examples and particularly illus-trations presented in this guide would nothave been possible. Some individuals whoshould have been acknowledged belowmay have been inadvertently omitted, andI apologize if that is the case. Amongthose who must be singled out for specialrecognition are the following (in alphabet-ical order):

Al Ball, Office of Community OrientedPolicing Services (COPS) RegionalInstitute, Cincinnati, Ohio; Rachel Boba,Tempe Police Department, Tempe,Arizona; Chris Bruce, Cambridge PoliceDepartment, Cambridge, Massachusetts;Jim Bueermann, Redlands PoliceDepartment, Redlands, California; Philip Canter, Baltimore County PoliceDepartment, Towson, Maryland; PeterDana, University of Texas, Austin, Texas;Wilpen Gorr, Heinz School, CarnegieMellon University, Pittsburgh,Pennsylvania; Elizabeth Groff, CrimeMapping Research Center, NationalInstitute of Justice, Washington, D.C.; HalHoltzman, U.S. Department of Housingand Urban Development, Washington,D.C.; James LeBeau, Southern IllinoisUniversity, Carbondale, Illinois; Suresh K.Lodha, University of California, SantaCruz, California; Mark Mattson, Temple

University, Philadelphia, Pennsylvania;Tom McEwen, Institute for Law andJustice, Alexandria, Virginia; Phillip G.McGuire, New York City PoliceDepartment, New York, New York; SaraMcLafferty, Hunter College, New York,New York; Robert Moland, St. PetersburgPolice Department, St. Petersburg, Florida;Tracy Molfino, Salinas Police Department,Salinas, California; Lew Nelson,Environmental Systems ResearchInstitute, Inc., Redlands, California; MikeNeumann, Cincinnati Police Department,Cincinnati, Ohio; Andreas Olligschlaeger,Carnegie Mellon University, Pittsburgh,Pennsylvania; George Rengert, TempleUniversity, Philadelphia, Pennsylvania; D. Kim Rossmo, Vancouver PoliceDepartment, Vancouver, British Columbia,Canada; Mark Stallo, Dallas PoliceDepartment, Dallas, Texas; Gerry Tallman,Overland Park Police Department,Overland Park, Kansas; Deb Thomas,University of South Carolina, Columbia,South Carolina; Arvind Verma, IndianaUniversity, Bloomington, Indiana; JulieWartell, Institute for Law and Justice,Alexandria, Virginia; John Werner, MesaPolice Department, Mesa, Arizona; Susan Wernicke, Overland Park PoliceDepartment, Overland Park, Kansas; DougWilliamson, Center for Applied Studies ofthe Environment (CAPSE), City Universityof New York, New York, New York.

Acknowledgments

I am particularly grateful to Nancy La Vigne, director of the National Instituteof Justice (NIJ) Crime Mapping ResearchCenter, who facilitated this project. Severalreviewers, anonymous and otherwise,provided invaluable insight that has beenincorporated. I am also grateful to my col-league Tom Rabenhorst, former cartographic

editor of Annals of the Association ofAmerican Geographers, who edited themaps.

Ultimately, all these efforts to spread theblame are in vain, and the author acceptsfull responsibility for errors, omissions,and misinterpretations.

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ForewordIn 1997, when the National Institute ofJustice (NIJ) was planning to create itsCrime Mapping Research Center (CMRC),we convened a 2-day strategic planningmeeting to seek advice on the Center’sgoals, direction, and mission. Before themeeting, we had assumed that manyagencies were already using mapping andthat NIJ’s goal would be to encourage thefield to move beyond descriptive mapping(e.g., pin maps) toward analytic mapping.The meeting helped us recognize thatanother goal must be to assist the largenumber of agencies that are not usingmapping.

Keith Harries, who received one of thefirst grants from CMRC, has prepared thiscomprehensive guide for agencies that arein the early stages of using geographicinformation systems (GIS). His words aredirected to law enforcement professionalswho have a little knowledge about GISand want to learn more about its benefitsand limitations.

He has collected more than 110 maps toillustrate how GIS is used. These picturesexpress the truth of the phrase “one pic-ture is worth a thousand words.”

Dr. Harries’ guide is not designed to standalone. Law enforcement agencies willneed other curriculum materials as well—especially software manuals—but it willbe a starting place. Additional materialsand links to other sources of informationcan be found at CMRC’s World Wide Website (http://www.ojp.usdoj.gov/cmrc). As a clearinghouse of information aboutcrime mapping, CMRC also sponsors a list-serv ([email protected]), which hasmore than 640 subscribers, and an annualconference, which draws more than 700attendees.

Today about 13 percent of law enforce-ment agencies are using GIS regularly toanalyze their crime problems, and we arecertain to see this number increase signifi-cantly as more and more agencies beginusing computerized crime mapping toidentify and solve their crime problems.We hope this guide will help them getstarted. For agencies that are alreadyusing crime mapping technology, we hope this guide will spark ideas aboutnew ways to use it.

Jeremy TravisDirectorNational Institute of Justice

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PrefaceThis guide introduces the science of crimemapping to police officers, crime analysts,and other people interested in visualizingcrime data through the medium of maps.Presumably most readers will be workingin law enforcement agencies, broadlyinterpreted to include courts, corrections,the military police, and Federal agenciessuch as the FBI, U.S. Bureau of Alcohol,Tobacco and Firearms, National ParkService, U.S. Customs Service, and U.S.Secret Service, as well as police depart-ments. The material is designed primarilyfor those who know little or nothing aboutmapping crime and who are motivated tolearn more.

This is not a guide to software. Nowhereis there more than a word or two on howto do anything technical involving a com-puter. A purely technical guide wouldquickly be out of date, and a guide thatserved one set of software devotees wouldnot serve others. Technical guidance isbest sought from the manuals and interestgroups specific to each software package.

What will be found here is a broadapproach addressing the kinds of ques-tions crime mapping can answer and how,

in general terms, it can answer them.Caveats are given from time to time,notably the caution against uncriticallyaccepting all the default settings thatcrime mapping software so convenientlyprovides.

Most readers will not read this guide fromcover to cover. Some will concentrate onapplication-oriented material. Others willhave an interest in the history of crimemapping, realizing that where we havebeen can help us figure out where we are going.

The presentation employed in this guideleans heavily on examples. Indeed, theguide is made up of examples with thewords draped around them. Crime analystsand researchers from across the UnitedStates and from Canada and the UnitedKingdom have contributed. Without theirhelp, this guide would be an empty shell.I am extremely grateful to all who donatedtheir work so graciously, and a partial list-ing of these kind souls is found in theacknowledgments.

Keith Harries

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ContentsAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Chapter 1: Context and Concepts . . . . . . . . . . . . . . . . . . . . . . . . 1

From pins to computers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Ancient history: Cartography and crime mapping . . . . . . . . . . . . . . . . . . . . . . . . 3

Mapping as a special case of data visualization. . . . . . . . . . . . . . . . . . . . . . . . . . 6

Mapping as art and science. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Maps as abstractions of reality: Benefits and costs . . . . . . . . . . . . . . . . . . . . . . 10

Crime incidents: Measuring time and space. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Map projection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Coordinate systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Map elements: The usual bits and pieces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Types of map information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Maps of crime: Thematic maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Data, maps, and patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Other theoretical perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

A note on cartograms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Reminder: Information is inevitably lost in the process of abstraction . . . . . . . . . 30

A note on the maps in this guide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Chapter 2: What Crime Maps Do and How They Do It . . . . . . . . . 35

What crime maps do . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

How crime maps do what they do . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Choosing a crime map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Contents

Examples of thematic maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Classifying map information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Maps and statistics: Exploratory spatial data analysis . . . . . . . . . . . . . . . . . . . . 53

Map design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Design, abstraction, and legibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Chapter 3: Maps That Speak to the Issues . . . . . . . . . . . . . . . . . 67

Patrol officers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Investigators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Police managers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Maps in support of community oriented policing and problem oriented policing . . 80

Maps and community policing: The city of Redlands, California, approach. . . . . . 82

Courts and corrections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Policymakers: The medium and the message . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Community organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Chapter 4: Mapping Crime and Geographic Information Systems . 91

The GIS revolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

The GIS perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Spatially enabling the data: What is geocoding? . . . . . . . . . . . . . . . . . . . . . . . . 97

Data selecting, filtering, and mapping according to useful criteria . . . . . . . . . . . 100

Selecting and displaying specific information . . . . . . . . . . . . . . . . . . . . . . . . . 100

Using GIS to measure from maps: Aggregating data . . . . . . . . . . . . . . . . . . . . 105

Derivative measures: How to create new indicators . . . . . . . . . . . . . . . . . . . . . 109

GIS as a tool for data integration and exploration . . . . . . . . . . . . . . . . . . . . . . 110

Hot spots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Buffering: Meaning and applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Data, data, everywhere: What’s an analyst to do?. . . . . . . . . . . . . . . . . . . . . . 121

Cautions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

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Chapter 5: Synthesis and Applications . . . . . . . . . . . . . . . . . . . 127

Synthesis 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Mapping applications for the millennium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Crime analysis and the census. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Chapter 6: Crime Mapping Futures . . . . . . . . . . . . . . . . . . . . . . 151

Geographic profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

High-resolution GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Forecasting: Complex statistical methods and crime mapping . . . . . . . . . . . . . . 155

Digital aerial photography in policing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

The Integration of GIS and GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

1


Chapter 1

Chapter 1:Context andConcepts

From pins to computers

Crime mapping has long been an integral part of theprocess known today as crime analysis. The NewYork City Police Department, for example, has tracedthe use of maps back to at least 1900. The traditionalcrime map was a jumbo representation of a jurisdictionwith pins stuck in it (figure 1.1). The old pin mapswere useful for showing where crimes occurred, butthey had serious limitations. As they were updated,the prior crime patterns were lost. While raw datacould be archived, maps could not, except perhaps by photographing them.1 The maps were static; theycould not be manipulated or queried. For example, itwould have been difficult to track a series of robberiesthat might overlap the duration (a week or month) ofa pin map. Also, pin maps could be quite difficult toread when several types of crime, usually representedby pins of different colors, were mixed together. Pinmaps occupied considerable wall space; Canter (1997)noted that to make a single wall map of the 610square miles of Baltimore County, 12 maps had to bejoined, covering 70 square feet. Thus pin maps hadlimited value—they could be used effectively but onlyfor a short time. However, pin maps are sometimesstill used today because their large scales allowpatterns to be seen over an entire jurisdiction in detail.Today, “virtual” pin maps can be made on the

Context and Concepts

computer, using pins or other icons assymbols (figure 1.2).

The manual approach of pin mappinggave way during the past decade or so tocomputer mapping—specifically, desktopcomputer mapping. For decades beforedesktop computer mapping, the processwas carried out on gigantic mainframecomputers using an extremely labor-intensive process. First, much labor wasinvolved in describing the boundaries ofthe map with numbered coordinates onpunched cards. Then came the labor ofkeypunching the cards, followed by a similar process of coding and keypunchingto put the data on the map.

Recognizing all the work needed to produce a map on the computer, manypotential cartographers2 (mapmakers)concluded that computer mapping was toolabor intensive. They were right—it was a “royal pain.” It was productive only if many maps were needed (making itworthwhile to prepare the base map, orboundary map, of the jurisdiction), and if the personnel necessary to do the datacoding and keypunching were available.Few organizations could afford the luxury.

Since the mid-1980s, and particularlysince the early 1990s, when computerprocessing speed increased dramatically,desktop mapping became commonplace

2 Chapter 1

Figure 1.1

Pin maps.

Source: Keith Harries.University of Maryland,

Baltimore County,Baltimore, Maryland.

Figure 1.2

A “virtual” pin map.

Source: SanGIS andthe San Diego Police

Department CrimeAnalysis Unit.

and fast, aided and abetted by the concur-rent availability of cheap color printers.What a partnership! Only a generationearlier, maps on paper (as compared withthe pin maps on walls in police depart-ments) had been drawn by hand usingindia ink3 and special pens, with templatesfor lettering. If a mistake was made, thelettering had to be scraped off the paperwith a razor blade. If shading was needed,Zipatone® patterns were cut to fit the areaand burnished to stick. Those were thedays? For character building, perhaps!

What does all this have to do with map-ping crime today? A newfound access todesktop mapping means that more indi-viduals than ever before have the task—or opportunity—to produce computermaps. Also the huge demand for mapsmeans that most crime analysts, policeofficers, and others involved in mappingcrime have received no formal training inmapmaking—the science called cartogra-phy. A body of accepted and useful princi-ples and practices has evolved over thelong history of mapmaking. Most cartog-raphy texts contain those principles buthave no regard for the special needs ofthe crime analyst, police officer, manager,or community user of crime maps. Thegoal of this guide, therefore, is to provide a guide to cartography that is adapted tospecialized needs and speaks the cartogra-pher’s language.

This guide is not intended to stand alone,isolated from evolving developments incrime mapping. Ideally, it should be usedin conjunction with other materials andapproaches. In 1998, for example, agroup working with the support of theCrime Mapping Research Center (CMRC)at the National Institute of Justice devel-oped a national curriculum for crime map-ping. Much information contained in thisguide can be found fleshed out in the cur-riculum and other materials disseminatedby CMRC. Readers are strongly encour-aged to visit the CMRC Web site athttp://www.ojp.usdoj.gov/cmrc.

Ancient history:Cartography and crime mapping

Conclusive evidence from clay tabletsfound in Iraq proves that maps have been around for several thousand years—perhaps tens of millennia (Campbell,1993). Evidently, the need to display geographic data is basic and enduring.Nowhere is the need for maps more com-pelling than in the field of navigation,whether for an epic around-the-worldvoyage or for a rookie cop’s struggle tofind an address in a city map book. Mapsfor navigation can be matters of life anddeath, and the inability of early naviga-tors to locate themselves accurately on the

3


Chapter 1

Maps

■ Are pictures of information about areas and places.

■ Help us visualize data.

■ Are like the proverbial pictures worth a thousand words.

■ Enable information to be seen at a glance.


surface of the Earth have often spelleddisaster, as described vividly in DavaSobel’s book Longitude (1995).

Fortunately, crime mappers do not have to be concerned about such epic matters.However, mapping crime is a scientificactivity—an application of the broader scientific field of cartography, which hasundergone a transformation with theadvent of geographic information systems(GIS). Many mapmakers now see cartog-raphy as a branch of information technol-ogy. A decade or so ago, cartography wasmuch broader in scope than GIS withapplications in fields as diverse as survey-ing, navigation of all kinds (including orienteering and highway mapping), geology, space exploration, environmentalmanagement, tourism, and urban plan-ning. Today, however, the convergence of cartography and GIS is nearly complete.Both are tools in a broad range of applica-tions, reflecting the most important use ofmaps—to communicate information.

Crime mapping, as noted at the beginningof this chapter, has quite a long history.Phillips (1972) pointed out that “hun-dreds of spatially oriented studies of crimeand delinquency have been written bysociologists and criminologists since about1830. . .” and recognized three majorschools:

■ The cartographic or geographicschool dominated between 1830 and1880, starting in France and spreadingto England. This work was based onsocial data, which governments werebeginning to gather. Findings tended to center on the influence of variablessuch as wealth and population densityon levels of crime.

■ The typological school dominatedbetween the cartographic period andthe ecological period that would followin the 20th century. The typologistsfocused on the relationship betweenthe mental and physical characteristicsof people and crime.

■ The social ecology school concentrat-ed on geographic variations in socialconditions under the assumption thatthey were related to patterns of crime.

The social ecologists recognized and clas-sified areas in cities with similar socialcharacteristics. Shaw and McKay (1942)produced a classic analysis on juveniledelinquency in Chicago. This work is gen-erally recognized as the landmark piece ofresearch involving crime mapping in thefirst half of the 20th century. Shaw andMcKay mapped thousands of incidents of juvenile delinquency and analyzed relationships between delinquency andvarious social conditions. Work by the“Chicago school” of researchers also delin-eated an urban model based on concentriczones, the first attempt to develop a theo-ry to explain the layout of cities (Burgess,1925). Other significant contributors tothe ecological school included Lander(1954), Lottier (1938), and Boggs (1966).

Most likely, the first use of computerizedcrime mapping in applied crime analysisoccurred in the mid-1960s in St. Louis(McEwen and Research ManagementAssociates, Inc., 1966; Pauly, McEwen,and Finch, 1967; Carnaghi and McEwen,1970; for more discussion, see chapter 4).Ironically, professional geographers werelate getting into the act. Early contribu-tions came from Lloyd Haring (whoorganized a seminar on the geography

4 Chapter 1

of crime at Arizona State Universityaround 1970), David Herbert in theUnited Kingdom, Harries (1971, 1973,1974), Phillips (1972), Pyle et al. (1974),Lee and Egan (1972), Rengert (1975),Capone and Nichols (1976), and others.Among the most remarkable (and littleknown) pieces of research emphasizingcrime mapping were Schmid and Schmid’sCrime in the State of Washington (1972)and Frisbie et al.’s Crime in Minneapolis:Proposals for Prevention (1997)(figures1.3 and 1.4). The latter, in particular, wasnotable for bridging the gap between aca-

demic crime mapping and analysis/appli-cations specifically aimed at crime preven-tion. Early computer mapping efforts usedline printers as their display devices, sotheir resolution was limited to the physicalsize of the print characters. This precludedthe use of computer maps for the repre-sentation of point data, at least until plot-ters that were able to draw finer lines andpoint symbols came into more generaluse. (For an excellent review of early mapapplications in crime prevention, seeWeisburd and McEwen, 1997, pp. 1–26.)

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Figure 1.3

A map showing homeaddresses of male arrestees

charged with driving under theinfluence, Seattle, Washington.

1968–70.

Source: Schmid and Schmid,1972, figure 7.14, p. 311.

Figure 1.4

A map showing the rate of com-mercial robbery by census tract,Minneapolis, Minnesota. Lineprinter technology.

Source: Frisbie et al., 1977, figure 7.2, p. 122.


Even as late as 1980, the breakthroughinto widespread GIS-style crime mappingwas about a decade away. It was neces-sary to wait for improvements in desktopcomputer capacity, printer enhancements,and price reductions before desktop map-ping could become an everyday, broadlyaccepted phenomenon.

To illustrate how matters have improved,a snippet of personal history is offered. In April 1984, the author bought his firstpersonal computer, a Kaypro 10 manufac-tured by Digital Research, Inc. This wonder ran at 4 megahertz and had 64 kilobytes of random access memory(RAM) and a 10-megabyte hard drive.(“How could you ever use all that stor-age?” friends asked.) It also had a tinymonochrome display and ran on the CP/M operating system, the precursor ofMicrosoft DOS. And all this for the rock-bottom price of $2,795 in 1984 dollars.The Silver Reed daisy wheel printer pur-chased to complement the computer was$895 (extra daisy wheels were $22.50each, tractor feed for paper was $160),and the 300k-baud rate modem was$535. After adding a few other knick-knacks, getting started in personal com-puting cost almost $5,000 (again, in1984 dollars).

By comparison, the typical RAM in 1999 is perhaps 1,000 times larger (64megabytes), the processor speed is 100times faster (at least 400 megahertz), andhard drives routinely are 100 times bigger(10 gigabytes), all at a lower price. It wasthis type of computing environment thatwould facilitate the entry of GIS into lawenforcement (and elsewhere) and permitcartographic principles and practices to be

used on a day-to-day basis. Mappingcrime has come into its own primarilybecause of advances in computing that, in turn, have facilitated GIS applications.Apart from all the obvious advantages, amajor benefit is that computer mappingallows free rein to experiment, a luxurydenied in the old days of manual map-ping. Are you wondering what a certainmap design would look like? Try it out.You don’t like it? Start over and have anew map in minutes.

Mapping as a special case of data visualization

Desktop computing has put graphic toolswithin the reach of virtually everyone.Preparing a publication-quality graphic,statistical or otherwise, was an arduousprocess a generation ago. Today it is mucheasier, although the process still demandsconsiderable care and effort. This newease and flexibility have broadened our perspective on graphics as tools for thevisualization of information. This has hap-pened because people no longer have todevote themselves to one specialized,time-consuming methodology, such ascartography. Now, maps can be producedmore easily, and the computer has ineffect freed people to produce other kindsof graphics as needed, such as bar charts,scatter diagrams, and pie charts.

The downside to such ease of productionis that it is just as easy to produce trashas it is to create technical and artistic perfection. Famous graphics authorityTufte (1983, chapter 5) referred to whathe called “non-data-ink,” “redundant-

6 Chapter 1

data-ink,” and “graphical parapherna-lia”—all summed up by the term“chartjunk,” a concept equally applicableto maps and charts. An exemplary map,according to Tufte, was prepared byJoseph Minard in 1861 to depict thedecline of Napoleon’s army in Russia in1812–13 (figure 1.5). Tufte noted that “it may well be the best statistical graphicever drawn” (Tufte, 1983, p. 40). Whatmakes it so good is that it shows six vari-ables with extraordinary clarity and with-out the use of color variation. The widthof the bands is proportional to the numberof troops, starting with 424,000, whichwas reduced to 100,000 by the time theyreached Moscow. The map shows attritionon the return trip (with vertical raysexpressing temperatures on selected dates)that left only 10,000 men still alive whenthe army returned to the starting point. Thefact that the map illustrates the devastat-ing loss of life further adds to its drama.

Today’s simplified graphics-producingenvironment helps put maps in perspec-tive. Maps are but one way of represent-ing information, and they are not alwaysthe most appropriate mode. If informationabout places is being represented, mapsmay be the best format. However, if no

geographic (place-to-place) information ispresent, such as when all the data for acity are combined into one table, there isnothing to map. The whole jurisdiction isrepresented by one number (or severalnumbers, each representing the city as awhole), so the map, too, could portrayonly one number. In this situation, a barchart simply showing the relative levels of each crime category would be the bestchoice.

What does it mean to say that maps are aform of visualization? Simply that a mapis data in a form that we can see all atonce. Books or tabulations of data are alsovisualizations in the sense that we assimi-late them visually, but they are labor-intensive visualizations. Maps and othergraphics are essentially pictures of infor-mation, those proverbial pictures “worth athousand words.” If they are well done,they convey their message more or less ata glance.

Mapping as art and science

Like other forms of visualization, mapsare the outcome of scientific activity:hypothesis formulation, data gathering,

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Figure 1.5

Minard’s 1861 map ofNapoleon’sadvance to andretreat fromMoscow.

Source: Tufte,1983, p. 41.Reproduced bypermission.


analysis, review of results, and evaluationof whether the initial hypothesis shouldbe accepted or rejected in favor of a modi-fied version. This cycle, known as thehypothetic-deductive process, is usedthroughout science as a fundamental tool.It is a universal paradigm, or model, forscientific investigation.

Constructing a map involves taking a setof data and making decisions consistentwith the hypothetic-deductive process.Decisions need to be made about the kindof map to be prepared, how symbols orshading will look, how statistical informa-tion will be treated, and so forth. Thesedecisions must be based on the objectiveto be achieved, including consideration ofthe target audience. Certain scientific prin-ciples can be applied. For example, if weare preparing a shaded map with a rangeof statistical data to be divided equally,then a simple formula can guide us:

Range in data values ÷ Number of classes.

Thus, if the range (difference betweenmaximum value and minimum value) ofdata values is 50 and it has been deter-mined that 4 classes, or “subranges,” ofdata would be appropriate, each will span12.5 units. This simple example demon-strates an elementary cartographic principle.

However, this science provides no helpwith elements of map design, such aschoice of colors, symbols, or lettering, andthe arrangement of map elements on thepage or within a frame. These elementsare pieces of the art of cartography andare just as important to the overall purposeand effectiveness of the map as the scien-tific elements. A map that is scientifically

perfect may be ineffective if it is an artisticdisaster.

Art and science may merge imperceptiblyand confound one another in unfortunateways. For example, oversized symbols orlettering may draw attention to parts ofthe map that should be understated or lessemphasized from a purely scientific per-spective. A poor choice of colors or inter-vals for statistics can also make a bigdifference. Thus the scientific mission ofthe map can be subverted through inap-propriate artistic choices, and poor choiceson the science side can similarly affect theartistic elements. In cartography, as inmedicine, art and science are inseparable.The perfect map blends art and scienceinto an effective tool of visual communi-cation. Figure 1.6 shows elements ofweaker (middle figure) and stronger (bot-tom figure) map design. The chart (topfigure) known as a histogram accompa-nying the maps shows an approximatelynormal (bell-shaped) distribution of scoreson an index representing the associationbetween violence and poverty in a neigh-borhood of Baltimore. The scores have amean, or average, of zero and a measureof spread around the mean (standarddeviation) of 1.0. Technically, this meansthat the census block groups in the range–1.0 to +1.0 are not significantly differentfrom the average. Those with scores lowerthan –1.0 or higher than +1.0 are consid-ered relatively extreme. We may wish tomap the extremes because they couldconvey information of value in communi-ty policing—areas with a strong linkbetween poverty and violence may war-rant special allocations of crime preven-tion and social service resources.

8 Chapter 1

If the objective of the map is to representthe high and low extremes, then the bot-tom figure does a much better job thanthe middle one, both in terms of how thedata are divided and how they are sym-bolized with colors. The bottom figuredivides the data into standard deviationunits such that the blue and red valuesare the focus of attention. In the middlefigure, the visual message is almost lost ina combination of a poor choice of colorsand shading symbols and a confusingdivision of the data into classes. The visu-al messages conveyed by the maps aredifferent, yet the underlying geographiesand statistics are identical. This exampleemphasizes the importance of the map asa medium for the interpretation of infor-mation. Subjective choices relating to boththe art and science content of the map arecritical.

Figure 1.6 also reminds us that it is justas easy to lie with maps as it is to lie withstatistics. (Remember the three kinds oflies: lies, damn lies, and statistics—andmaps, perhaps?) “Lying” may be a bitstrong. It is more likely that the compilerof the map misleads readers by choosinginappropriate designs rather than byintentionally falsifying information. Thismay happen on several levels, the mostbasic being that the author of the mapcreated it to achieve an objective differentfrom that experienced by the readers.Next are issues related to data manipula-tion and cartographic art. Ultimately, thepossibilities for misrepresentation and mis-interpretation are virtually infinite. Thekey question is whether these problemsare fatal flaws with respect to specific maps.

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Figure 1.6

Examples of maps with differingimpact depending on design.

Source: Keith Harries.


Maps as abstractions of reality: Benefits and costs

Maps try to display some aspect of reality.But like books, movies, television, ornewspapers that try to do the same, theyfall short (figure 1.7). The only perfectrepresentation of reality is, after all, realityitself. (“You had to be there.”) All else is, to a greater or lesser degree, anabstraction. Abstractions present choices.How much abstraction can we tolerate?How much information can we afford tolose? The fundamental tradeoff is:

■ More abstraction equals less informa-tion (farther from reality).

■ Less abstraction equals more informa-tion (closer to reality).

The tradeoff can also be viewed this way:

■ More abstraction equals greater sim-plicity and legibility (more effectivevisual communication).

■ Less abstraction equals greater com-plexity, less legibility (less effectivevisual communication).

In our quest to represent reality as faith-fully as possible, we may be tempted toput too much “stuff” in our maps or othergraphics. This may give us maps that havea lot of information but that may be illegiblejunk heaps. Usually, the abstraction-reality

10 Chapter 1

Figure 1.7

Maps showing the contrastbetween more abstraction

(“stick” streets—top) and lessabstraction (an aerial photograph

of the same area—bottom).

Source: Orthophotography:Baltimore County, Maryland, GISUnit, March 19, 1995 (Phase II),

pixel size 1”, compilation scale1” = 200’. Reproduced by

permission.

tradeoff should tilt us in the direction ofsimplicity. Better to have one or twoimportant points clearly understood thanto have utter confusion pushing readerstoward anger and frustration. Worse yet,that confusion may cause readers to drawincorrect conclusions. That outcome couldbe worse than no map at all.

Consideration of this tradeoff should bepart of the map production process. Withpractice, it will become “second nature”and will not need much thought. But forthe novice, it is an important issue callingfor thought and perhaps discussion withcolleagues. Reading Tufte (1983, 1990)will raise awareness.

Crime incidents:Measuring timeand space

Crimes happen—everything happens—inboth time and space. Vasiliev (1996) hassuggested that time is a more difficult concept than space. Spaces and locationscan be seen and measured quite easily bymeans of simple reference systems, suchas x-y coordinates. Time, however, isharder to grasp, and maps have represent-ed time in multiple ways (Vasiliev, 1996,p. 138), including:

■ Moments. Providing times of eventsin geographic space. When did crimeincidents occur and where?

■ Duration. How long did an event orprocess continue in a specific space?For example, how long did a crime rateremain above or below a certain level

in a particular area? How long did ahot spot (an area of high crime) persist?

■ Structured time. Space standardizedby time (for example, shift-basedpatrol areas, precincts, and posts).

■ Distance as time. We often expressdistance as time. “How far is it?”“About 20 minutes.” More to thepoint, perhaps, is concern withresponse times. In practice, a fixed,maximum permissible response timecorresponds to the maximum distancefeasible for patrol units to drive.Another application would be aninvestigation to see whether a suspectcould have traveled from the last placehe was seen to the location of thecrime within a certain time period.

Specialized activities, such as policing,have their own unique perspective ontime and space; thus, the elements ofVasiliev’s typology will vary in their rele-vance from time to time and place to place.

There is no question that time is animportant element in crime mappingowing to the time-structured way inwhich things are organized in policedepartments—by shifts. Patrol areaboundaries may differ by shift, command-ers may call for maps of crime by shift,and resources may need to be allocateddifferently by shift. Maps may need to beprepared on a weekly, monthly, quarterly,or annual basis to illustrate trends. Time-based maps may be further refined tempo-rally by adding the shift dimension. Crimedata could be mapped vertically, in effectstacking (merging) maps on top of each

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other, using different symbols for differenttime periods. Alternatively, a different mapcould be prepared for each time period. Allthese modifications can be automated byusing GIS.

The importance of selecting appropriatetime periods for mapping cannot beoveremphasized. For example, a map covering a month may mask noteworthyweek-by-week variations. Or weeklymaps could hide day-to-day changes.Mapping intervals selected for administra-tive convenience may not be the best foranalytical purposes. For example, the cal-endar week may be best for police depart-ment convenience, but local events, suchas an industrial holiday, a sporting event,a plant closure, or some seasonal phenom-enon may cut across administrative timeunits and may also have relevance tocrime frequencies. Related questions are,

How much time elapses before a map isout of date? All data are obsolete sooneror later, but when is sooner? When islater? These decisions are quite subjectiveand call for a “feel” for the data underreview.

Another aspect of mapping crime in timeand space relates to the representation ofchange. One of the most crucial questionsasked in police departments is, How hascrime changed in this neighborhood in thelast week or month or year? Maps canhelp answer this question by symbolizingchange in several possible ways, such asby showing crime as a “surface” withpeaks representing high levels of occur-rence (gray areas) and valleys low levels(red areas) (figure 1.8). This approachborrows from the methodology of thetopographic map, on which the land sur-face is represented by contour lines, each

12 Chapter 1

Figure 1.8

A representation of the aggra-vated assault “surface” in part

of Baltimore County, Maryland.Red and darker shading indi-cate higher assault densities.


joining points of equal elevation. On thecrime surface map, areas of decliningcrime can be shaded differently from thosewith increases. Crime mappers are limitedonly by their imagination when represent-ing time and space in two dimensions orsimulated three dimensions.

As usual, we should keep an open mind.Some space-time data may be best repre-sented with a “non-map” graphic, or acombination of map and statistical graphics,such as precinct bar charts or pie chartsembedded in a map of precincts. Here wecome full circle, back to cartography as artand science and to the abstraction/detailtradeoff. Only crime mappers can decide,probably on the basis of some experimen-tation, which graphic representations arebest for themselves and for their audiences.

Map projection

What is it?

A fundamental problem confronting map-makers is that the Earth is round and thepaper we put our maps on is flat. Whenwe represent the round Earth on the flatpaper, some distortion (perhaps a lot,depending on how much of the sphericalEarth we are trying to show) is inevitable.Map projection is so called because itassumes that we have put a light sourcein the middle of a transparent globe (figure1.9). The shadows (grid) made on a nearbysurface are the projection.4 Their charac-teristics will vary according to where thesurface is placed. If the surface touchesthe globe, there will be no distortion atthat point or along that line in the case of a cylinder being fitted over the globe,

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Chapter 1

Figure 1.9

Diagrams showing cylindricaland conical projections.Shadows of the globe’s gridlines on wraparound paper; a cylindrical projection results(top). Construction of a conic projection (middle and bottom).

Source: H.J. Blij, HumanGeography: Culture, Society,and Space, Fifth Edition. New York, NY: John Wiley &Sons, Inc., ©1996, Figures R7and R8, pages R6 and R7.Reprinted by permission ofJohn Wiley & Sons, Inc.


touching along the Equator. Away fromthe point or line of contact, distortionincreases. Among the most common projections are the cylindrical (alreadydescribed) and the conical, for which acone is fitted over the globe, like a hat,with the top of the cone over the pole (seefigure 1.9). The cylinder or the cone is cutand flattened to create the map. The cylin-drical projection tends to have much moredistortion at high latitudes (closer to thepoles) compared with the conical.

The better known projections include thevenerable Mercator 5 (useful for naviga-tion), the Albers conical equal-area 6, and the Lambert conformal conic 7 (thelast two are frequently used for U.S.maps). Another well-known format is the Robinson, now the default world map projection used by the NationalGeographic Society. Its advantage is that itminimizes high-latitude distortion of areascompared with most other cylindrical pro-jections. U.S. States are typically repre-sented by either the Lambert conformalconic or the transverse 8 Mercator, depend-ing on the size and shape of the State. Forexample, Dent (1990, pp. 72–73) notedthat for Tennessee a conical equal-areaprojection would be appropriate, with itsstandard parallel (the line of latitudewhere the projection cone touches theState) running through the east-west axisof the State. Projections are a highly tech-nical branch of cartography, and readerswho want to learn more are referred tostandard texts in cartography such asCampbell (1993), Dent (1990), andRobinson et al. (1984).

Why we don’t need to worry

Map projections are vitally important forcartographers concerned with representinglarge areas of the Earth’s surface owing to the distortion problem and the myriadchoices and compromises available in various projections and their numerousspecialized, modified forms. But crimeanalysts do not need to be overly con-cerned with map projections becausepolice jurisdictions are small enough sothat map projection (or Earth curvature) isnot a significant issue. However, there isthe possibility that analysts will deal withmaps with different projections and thatthose maps will not fit properly whenbrought together. Streets and boundarieswould be misaligned and structures mis-placed. However, this misalignment canalso occur for reasons unconnected withprojection, as explained below. Althoughcrime analysts do not need to worry aboutprojections per se, a related issue, coordi-nate systems, generally needs to be givenmore attention because the analyst proba-bly will encounter incompatible coordinatesystems.

What are the differences between projec-tions and coordinate systems? Projectionsdetermine how the latitude and longitudegrid of the Earth is represented on flatpaper. Coordinate systems provide the x-yreference system to describe locations intwo-dimensional space. For example, latitude and longitude together are a coor-dinate system based on angular measure-ments on the Earth’s sphere. But there are other ways of referring to points. Forinstance, all measurements could be based

14 Chapter 1

on distances in meters and compass direc-tions with respect to city hall. That wouldalso be a coordinate system. Distances infeet and directions could be based on thepolice chief’s office as a reference point.The following discussion provides somedetails on commonly used coordinate systems.

Coordinate systems

What are they?

Coordinate systems allow users to refer topoints in two- or three-dimensional space.You may have heard of Cartesian geome-try, named for the 17th-century Frenchmathematician Descartes, the founder ofanalytic geometry and the Cartesian coor-dinate system. In two dimensions, weusually refer to two principal axes, the x(horizontal) and y (vertical). If necessary,we add the z axis for the third dimension.Points are located by referring to theirposition on the scales of the x and y (andz) coordinates. Diagrams usually knownas scatter diagrams, or scatter plots (figure1.10), are based on Cartesian coordinates,with their origin in the lower left corner.

Things get a bit more complicated whenthe coordinate system is applied to thespherical shape of the Earth.

Latitude/Longitude

For the Earth’s sphere, angular measure-ments must be added to the x-y coordi-nate system. Latitude angles aremeasured from the center of the Earthbetween the Equator and poles, 90degrees north and south, with the Equatoras 0 degrees and each pole 90 degrees.Longitude angles are also measured fromthe center of the Earth, 180 degrees eastand west of the Prime Meridian runningthrough the Royal Naval Observatory inGreenwich, England,9 a location fixed byinternational agreement in 1884. So all ofthe United States is described in degreesnorth latitude and west longitude. Forexample, New Orleans is located atapproximately 30 degrees north latitudeand 90 degrees west longitude. Latitudelines are also known as parallels becausethey are, in fact, parallel to one another,and longitude lines are called meridians, asin “Prime Meridian.” State boundaries westof the Mississippi River are predominantly

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Chapter 1

Figure 1.10

A scatter diagram showing therelationship between per capitaincome and median value ofowner-occupied homes by census tract in Orange County,California, that illustrates theuse of x-y coordinates.



made up of segments of meridians andparallels. For example, the longer westernborder of Oklahoma is a segment of the100th meridian.

State Plane and UniversalTransverse Mercator

As noted previously, appropriate map pro-jections have been adopted for each State,yielding “Earth” projections with coordi-nates based on latitude and longitude, the universal reference system. But these“latitude/longitude” references, as theywill be referred to, are quite cumbersome,given that they are in degrees, minutes,and seconds.10 Two principal alternativecoordinate systems are found in additionto latitude/longitude: the State PlaneCoordinate System and the UniversalTransverse Mercator (UTM).

The State Plane Coordinate System wasdevised for greater user convenience, witha rectangular grid superimposed over thelatitude/longitude graticule, producingState plane coordinates expressed inmeters, yards, or feet. In effect, this sys-tem assumes that the individual States are flat so they can be described by planegeometry rather than the spherical grid.For local applications, this use of planegeometry is acceptable because error dueto failure to take Earth curvature intoaccount is not significant over relativelysmall areas such as police jurisdictions.

Large States are divided into zones withseparate grids for each to avoid the distor-tion problem. Texas, for example, is divid-ed into the North, North-Central, Central,South-Central, and South zones; Louisianainto North, South, and Coastal. Typically,

the origin, or zero point, for a State planesystem is placed in the southwest corner,just like the scatter plot shown in figure1.10, to avoid the inconvenient possibilityof having to express coordinates in nega-tive numbers. The origin is also placedoutside the study area for the same reason.

The UTM system is used to refer to mostof the world, excluding only polar regions,and consists of 60 zones, north-south,each 6 degrees of longitude wide. Eachzone has a central meridian, and originsfor each zone are located 500,000 meterswest of the central meridian. A samplelocation in Texas (the capitol dome inAustin) is shown in figure 1.11. Thisexample specifies that the dome is621,161 meters east of the central meridi-an of zone 14 and 3,349,894 metersnorth of the Equator, the latitude of origin.The acronym NAD will appear in refer-ences to the State plane and UTM coordi-nate systems. NAD stands for NorthAmerican Datum, a reference system thatwas based at a Kansas ranch in 1927.Demands for greater accuracy and moreaccurate surveying made possible bysatellites led to a new NAD in 1983,hence the reference in figure 1.11 to NAD83 UTM coordinates.

Why we do need to worry

Crime mappers using computerized datafrom different sources may run into aproblem resulting from different data setsbeing digitized (assigned their x-y coordi-nates) in, or converted to, incompatiblecoordinate systems or even incompatibleprojections. The most frequent conflict islikely to be between latitude/longitude and State plane coordinates owing to the

16 Chapter 1

likelihood that locally developed data (oftenin State plane coordinates) will be mixedwith data from more general sources,probably expressed in latitude/longitude.We saw that New Orleans is referenced as30 degrees north latitude and 90 degreeswest longitude in the latitude/longitudesystem. But if we refer to it using theState plane system, it is in the LouisianaSouth Zone, with coordinates 3,549,191feet east of the origin and 592,810 feetnorth of the origin. Clearly, if we try toput the State plane data on a map set upfor latitude/longitude, ugly things willhappen. (What usually happens in a GISis that the map goes blank.11)

Data sets placed together on a map shouldhave compatible projections and compati-ble coordinate systems. Fortunately, GISsoftware permits conversion from one projection to another and from one coordi-nate system to another, so the problem, ifit does arise, is reasonably easy to fix. Butif incompatibilities are not recognized atthe outset, they can be quite frustrating.Positive action to check the projection andcoordinate properties of a new data set is

recommended because the coordinate system may not be apparent initially.

Map elements: Theusual bits and pieces

Generally, maps have the following elements that help provide consistencyand comprehensibility for readers.

■ A title (or caption) to describe themap.

■ A legend to interpret the content ofthe map, such as the symbols and colors.

■ A scale to translate distance on themap to distance on the ground.

■ Orientation12 to show compass direction.

However, a review of maps in cartographytexts and elsewhere reveals the inconven-ient fact that these conventions are oftenoverlooked. Context should dictate whethersome or all of these elements are included.

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Figure 1.11

An example of a UniversalTransverse Mercator (UTM)location.

Source: Peter H. Dana, TheGeographer’s Craft Project,Department of Geography,University of Texas at Austin.Reproduced by permission. Visit Dana’s Web site dealingwith coordinate systems:http://www.utexas.edu/depts/grg/gcraft/notes/coordsys/coordsys.html.


For example, in a situation in which mapsof the same jurisdiction, or parts of it, areproduced and distributed to the sameaudience on a repetitive basis, a labeltelling readers the area would be a wasteof ink. But if the map were intended forwider distribution to audiences that mightnot know what they were looking at, acaption or heading would be appropriate.Similarly, scale may be redundant whenthe users of the map are educated to thespatial relationships on the map, oftenthrough years of field experience.Orientation, too, is unlikely to be criticalin locally used crime maps, for whichnorth is almost always “up.” It may beworth noting that Minard’s map ofNapoleon’s army (see figure 1.5) has noorientation, and if you want to be picky, it has no legend per se.

Indeed, a legend is perhaps the only some-what (but not entirely) indispensable mapelement. Readers need to be absolutelysure what map data mean, and the legendis the only reliable guide to this informa-tion. Some maps may be so simple that a legend is not necessary. An examplewould be a map showing points, each ofwhich represents a residential burglary.The heading or caption of the map (suchas, “Locations of residential burglaries inSt. Louis, September 2000.”) can containthe necessary information and overridethe need for a legend.

This discussion may imply that one canbe slapdash about map elements, but thatimplication is not intended. Careful consid-eration should be given to whether a par-ticular element should be included; if anelement is included, it must contain com-plete and accurate information. (For fur-

ther elaboration, see “Map design” sectionin chapter 2.)

Types of map information

Maps can provide a rich variety of infor-mation, including but not limited to loca-tion, distance, and direction as well aspattern for maps displaying point or areadata. Each type of data means differentthings to different users.

■ Location is arguably the most impor-tant of all the types of information tobe represented on, or gleaned from, amap from the perspective of a crimeanalyst. Where things have happened,or may happen in the future, is themost sought after and potentially use-ful piece of information because it hasso many implications for investigatorsand for the allocation of patrol andcommunity resources, in addition toutility in the realms of planning andpolitics.

■ Distance is not much use as anabstract piece of information. It comesto life when translated into some kindof relationship: How far did the victimlive from the place where she wasrobbed? What is the maximum dis-tance police cars can travel within aspecific urban environment to provideacceptable response times? How farcould a suspect have gone in a particu-lar time period?

■ Direction is most useful when consid-ered in conjunction with distance,although it is not typically an impor-tant piece of map information in crime

18 Chapter 1

analysis unless it relates to other rele-vant processes or conditions. It is gen-erally used in a broadly descriptivecontext, such as “the hot spot of bur-glaries is spreading to the west,” or“serial robberies are moving south-east,” or “the east side is becoming a high-crime area.” In the exampleshown in figure 1.12, serial robberswere found to have a tendency tomigrate south from Baltimore along the major highways.

■ Pattern is an especially useful conceptin crime analysis, as so much of whatcrime analysts do involves describingor analyzing the pattern of crimeoccurrences. Pattern can be a powerfulinvestigative tool because the waypoints are arranged may tell us some-thing about the process driving thatarrangement. Patterns are usually clas-sified as random, uniform, clustered,or dispersed. In a random arrange-ment, points are just as likely to be atany place on the map as at any other.Points are distributed haphazardlyaround the map. A uniform distributionhas points that are equally spaced.Alternatively, we can say that in a uni-form distribution the distance between

neighboring points is maximized. In aclustered pattern, points are clumpedtogether with substantial empty areas.

It is tempting to assume that the nonran-dom distributions (uniform and clustered)automatically mean that some interestingunderlying process is at work, providinguseful information about crime. This mayor may not be true. For example, burgla-ries show up in a cluster, suggesting a hotspot. But further investigation shows thatthe cluster corresponds to a neighborhoodwith a dense population, so the high frequency is no more than an expressionof the geography of risk. The termsdescribing the types of patterns are subject to some semantic confusion. Forexample, what does dispersed mean? A dispersed pattern could be random oruniform. Is “less dispersed” the same as“clustered”? Various indexes have beendeveloped to measure regularity, random-ness, and dispersion. For additional reading, see, for example, Taylor (1977,chapter 4) and Hammond and McCullagh(1974, chapter 2).

Line data (for networks, for example) and both discrete and continuous arealdistributions are additional types of data.

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Figure 1.12

A map of selected robberies inHoward County, Maryland, con-centrating along the U.S. 29, U.S.1, and I–95 highway corridors.The Baltimore-Washington areaRegional Crime Analysis Programhas used maps like this to helpapprehend serial robbers whocross county lines. For a fullaccount of this process, seeMishra, 1999.



■ Line data. Linear features or process-es can be abstracted on maps. TheMinard map (see figure 1.5) did thisby symbolizing the flow of troops toMoscow and back. The street mapsused to map crime also contain linedata that show points on streets, indi-cating the linear arrangement of inci-dents. A map connecting places wherevehicles are stolen to the places wherethey are recovered is prepared withlines connecting the places. Or a linemap might connect victim addresses to suspect addresses. Traffic flow can beshown with lines proportional in thick-ness to the flow (again like Minard’smap in figure 1.5).

■ Discrete distributions. When pointdata are combined within unit areassuch as precincts, patrol areas, censustracts, or neighborhoods, each area isseparated from the others; it is “dis-crete.” Mapping by discrete areas canbe used to reorganize the point datainto a context that may have moremeaning for a specific purpose. Forexample, commanders may want tosee the distribution of drug offenses bypatrol beat to decide how workloadsshould be assigned. This could bemeasured by density, which expresseshow often something happens withinan area. A common application is pop-ulation per square mile, for example.Density also is used increasingly frequently to describe crimes, and pop-ulation density data can provide addi-tional explanation of the risk or rateconcepts in public forums. Graphic rep-resentations of this general type, map-ping data by administrative or politicalareas, are known as choropleth13

maps. (A more detailed explanation isgiven in chapter 2.)

■ Continuous distributions are usedless in crime analysis than discrete distributions, but they can be usefuland are finding more frequent applica-tion in conjunction with, for example,commonly used software such asArcView® Spatial Analyst. Continuousdistributions are phenomena found innature, such as the shape of the land(topography), temperature, and atmos-pheric pressure. All places on Earth(above and below sea level) havetopography and temperature, and allplaces above sea level (and a few ondry land below sea level) have atmos-pheric pressure. Thus it makes nosense to represent these conditions onmaps divided into areas such as countiesor cities, except for reference purposes.

What does this have to do with crimemapping? It is sometimes useful toassume that crime can be representedas a continuous distribution to preparea generalized statistical surface repre-senting crime density. This can providea “smoother” picture of crime thatcan be enhanced by adding three-dimensional effects (figures 1.13 and1.14). This implies an acceptance ofthe tradeoff between loss of detailedlocational information from the pointmap and the smoothed, generalizedpicture provided by the quasi-continu-ous presentation. This smoothed version may have the advantage ofbetter legibility than the original detail.Another advantage of a surfacesmoothed across jurisdictions is that itmay vividly illustrate that political

20 Chapter 1

boundaries have little or no meaningfor criminals.

Scale

Maps are miniature representations of partof reality. Scale tells us how miniaturethey are. Scale is commonly expressed asa ratio, in words, or as a bar graph locatedsomewhere in the map frame. A ratiostates the scale as a unit of map distancecompared with distance on the ground.This is referred to as the representativefraction (RF). Thus an RF of 1:10,000

means that 1 unit of linear measurementon the map represents 10,000 of thoseunits on the ground. Units are inter-changeable; for a 1:10,000 map, 1 inchon the map equals 10,000 inches on theground, 1 centimeter on the map equals10,000 centimeters on the ground.

Scale terminology can be confusing. Forexample, large-scale maps and aerial photographs used by local governments(and quite likely to be used by crime analysts) are often at a scale of 1:2,400.If expressed in inches and feet, this is

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Figure 1.13

Early (1976) three-dimensionalmaps of Tulsa, Oklahoma, usingthe SYMVU program. The robberyrate is based on the population(top) and number of persons perdwelling unit (bottom). The bot-tom map illustrates the point thatthe denominator in crime ratesdoes not have to be population.

Source: Harries, 1978, figures 43and 44.

Figure 1.14

A contemporary three-dimensional map ofRedlands, California. Thevertical dimension por-trays the relative risk ofcrime across the city.

Source: Redlands PoliceDepartment. Reproducedby permission.


equivalent to 1 inch equaling 200 feet.Hence users often refer to the maps sim-ply as “200 scale.”

Large scale or small scale?

The distinction between large scale andsmall scale is also a source of confusion,but there is a simple rule to help us under-stand. Look at the representative fraction.If it is a large fraction, the map scale islarge, and vice versa. For example, I openmy National Geographic Atlas of theWorld and flip through it until I come to amap of the world. The scale is expressedin words as follows: “Scale 1:66,300,000at the Equator.” This is extremely smallscale, as 1/66,300,000th ofsomething is not much. Afew pages farther, I come toa map of the upper mid-western States of the UnitedStates. Its scale is expressedas “1:2,278,000”—a frac-tion 29 times larger thanthat for the world map. As scales get larg-er and larger, we move from the realm of“maps” to what we customarily refer to as“plans,” such as 1:20, where 1 unit onthe map (plan) equals 20 units on theground or the floor plan. These very large-scale renderings are the domain of thearchitect or engineer rather than the cartographer.

Scale has implications for the interpreta-tion and meaning of maps. Implicit in theconcept of scale is the now-familiar trade-off between abstraction and detail. Smallscale implies more abstraction, large scaleless. Evidence from cognitive psychologycited by MacEachren (1995) suggests the

importance of progressing from small scaleto large scale when presenting maps ofdifferent areas, the principle of global-local precedence. In other words, if youwere to prepare maps for the entire juris-diction and also for a small part of it, thebest mode of presentation would put thesmaller scale map (the entire jurisdiction,perhaps in the form of a satellite image)first. The smaller area (larger scale) wouldfollow this.

Another implication of scale is that crimedata will look different at different scales,such as citywide and beat levels. Thesame data will appear to be more spreadout (less dense) at the scale of the beat

and more crowded (more dense) at thecitywide scale. But in reality densities(crimes per unit area) are the same onboth maps. What has changed is scale.

Scale also has implications for data collec-tion. If maps are to be large scale, detaileddata collection is appropriate. But small-scale maps are incapable of representingfine detail, so if the only scale availablefor a specific purpose is small, collectingdata at the micro level is pointless. Thedetail will be lost on a map incapable ofshowing it. The converse is also true.Gathering data at the ZIP Code level willbe too coarse a scale on a map showingstreet addresses.

22 Chapter 1

Rules of Thumb for Map Scale

■ Small-scale maps show large areas.

■ Large-scale maps show small areas.

Maps of crime:Thematic maps

What is a thematic map? Quite simply, asthe label suggests, it is a map of a themeor topic. Thematic maps have almost infi-nite variety and include most of the mapsin the media showing, for example, thespread of fire ants, the status of salestaxes by State, or world population densi-ty. Thematic maps are like a comprehen-sive toolkit—we can select a topic andthen choose from many possible ways ofconverting the data into a legible map thateffectively communicates with the intend-ed audience. Thematic maps may bequantitative or qualitative.

■ Quantitative maps portray numericalinformation, such as numbers ofcrimes in an area or crime rates.

■ Qualitative maps show nonnumericaldata like land use types or victim/offender characteristics, such as maleor female, juvenile or adult.

Crime analysts use both quantitative andqualitative maps. Thematic maps caninclude four kinds of measurement data:nominal, ordinal, ratio, and interval. (Fora more detailed explanation, see any basicstatistics textbook, such as Burt andBarber, 1996.)

■ Nominal measurement names orlabels items in unordered categories,such as race. If a map shows homicidevictims by race, it is a qualitative the-matic map. Mapping by race, agegroup, or marital status puts labels on groups without ranking them as higher or lower or better or worse.(Quantitative information can also be

inferred from this type of map. Forexample, the number of incidentsaffecting racial groups by areas can becounted, thus combining qualitativeand quantitative interpretations. Typesof measurements are often mixed onmaps.)

■ Ordinal measurement classifies inci-dents, victim or offender characteris-tics, or some other attributes (perhapsareas) according to rank. Thus patrolareas or precincts might be rankedaccording to their crime rates, theirincidence of complaints, or the averageseniority of officers. This involves onlysorting and evaluating the data accord-ing to their relative values so that sub-jects can be ranked. How much thesubjects differ is not considered. Thuswe can put qualitative characteristicson an ordinal scale, such as a hierar-chy of police areas based on size, withdistricts above precincts, which are inturn above patrol beats.

■ Ratio scales, such as distance in inches,feet, yards, millimeters, meters, and soforth, start at zero and continue indefi-nitely. Zero means there is none of itand 20 means there is twice as muchas 10. For example, the homicide rateis 3 per 10,000 persons in the city.Crime analysts will use nominal, ordi-nal, and ratio scales for these data butare very unlikely to use the fourth kindof measurement, interval.

■ Interval scales show values but cannot show ratios between values.Temperature is a good example. Wecan measure it, but we cannot say that80 degrees is twice as warm as 40degrees, since the starting points of

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both the Fahrenheit and Celsius scalesare arbitrary—that is, they are not truezeros. A possible exception to theassertion that crime analysts will notuse interval scales could be seriousnessweighting. (See Wolfgang et al., 1985aand 1985b, for more on this topic.)

Thematic maps come in considerable vari-ety and will be examined in more detail inchapter 2. Each type represents some kindof data best. Information with address-level detail calls for one kind of thematicmap, whereas data measured only at theneighborhood, precinct, or census tractlevel require a different approach.Examples of various ways in which the-matic maps can be designed are containedin the following categorization of thematicmaps:

■ Statistical maps use proportionalsymbols, pie charts, or histograms tovisualize the quantitative aspects ofthe data. Typically, the statistical sym-bols are placed in each subdivision onthe map, such as patrol areas, censustracts, neighborhoods, or wards. Suchmaps can be quite difficult to read ifthey contain a large amount of detail,particularly when many geographicsubdivisions and several attributes ofthe information are being mapped.Nominal data, such as the race of vic-tims as proportions in precinct-basedpie charts, can be represented on a statistical map.

■ Point (pin) maps use points to repre-sent individual incidents or specificnumbers, such as when five incidentsequal one point. (Aggregating multipleincidents to single points would be

done only on a small-scale map.) Amap showing locations of drug mar-kets by types of drugs prevalent ineach is an example of a point mapwith nominal scale data. Point mapsare probably the most frequently usedmaps in policing, as they can showincident locations quite precisely ifaddress-level data are used.

■ Choropleth maps show discrete distri-butions for particular areas such asbeats, precincts, districts, counties, or census blocks. Although point data can give us the best detail in terms ofwhere events happen, information maybe needed for areas in summary formthat has meaning in terms of planning,management, investigation, or politics.Note that point data and choroplethrepresentations can both be put on thesame map, if it is appropriate and theresult is not a garbled mess. For exam-ple, burglary (point) data could be putover neighborhood boundaries (choro-pleth data) or any areas representingpolice geography. Also, choroplethmaps can be given a three-dimensionalappearance by making each area into a raised block, with the height of theblock representing the relevant datavalue.

■ Isoline is derived from “iso,” theGreek prefix for equal, and refers tomaps with lines that join points ofequal value. Physical geography isreplete with isoline maps that use thefollowing: isobars (equal barometricpressure), isohyets (equal rainfall),isotherms (equal temperature), iso-baths (equal depth), and, in a raredeparture from use of the iso prefix,

24 Chapter 1

contour lines to join points of equalelevation. The form most likely to beused in crime analysis is the isopleth(equal crowd), in which data for areas,such as crimes per neighborhood orpopulation density, are calculated andused as control points to determinewhere the isolines will be drawn.14

(See Curtis, 1974, for an example ofan isopleth map of homicide, rape, andassault in Boston.)

■ Surface maps can be regarded concep-tually as a special case of an isolinepresentation. Such maps add a three-dimensional effect by fitting a raisedsurface to data values. Typically, anarbitrary grid is placed over the mapand the number of incidents per gridcell are counted. These counts form thebasis for what is, in effect, an isoplethmap that is given its third, or Z (vertical), dimension derived from the isoline values. The resulting map is rendered as if it were being viewedfrom an oblique angle, say 45 degrees.(If it were to be viewed from overhead,like a normal map, the surface wouldbe lost and the map would appear tobe a flat contour map.) Such continu-ous surface maps can make a powerfulvisual impact, but they have dual dis-advantages in that data values arehard to read on the map and detailbehind data peaks is lost (see figures1.13 and 1.14).

■ Linear maps show streets and high-ways as well as flows using linearsymbols, such as lines proportional in thickness to represent flows. Apartfrom base maps of streets and high-ways, crime mappers use linear maps

infrequently—the most common appli-cation is vehicle theft investigationshowing connections between theplace of theft and place of recovery.

Data, maps, and patterns

The stage is set. We have a truckload ofdata, computers, software, and printers.Provided our data have been gathered in aform that permits computer mapping (orhave been converted to such a format; see chapter 4), we are at least technicallyready. It should be noted at this point thatcomputer mapping is not “plug and play”but is more akin to word processing—thedata have to be entered and sentences(read “maps”) composed. Similarly, wewill not be able to do computer mappinguntil we have data in a format that theprogram can understand. Also, manychoices will have to be made. Automatedmapping is automated only up to a point.(When you put your car on cruise control,someone still has to steer!)

Before we plug it all in and start mapping,it might be helpful to pause and thinkabout the reasons underlying the patternswe observe and map. Those patterns mustbe generated by specific conditions andprocesses, and theories can be employedto help us understand them.

Each type of crime tends to be influencedby different conditions. For example,shoplifting is the outcome of circum-stances different from those that producehomicide. Even within crime types, thereare qualitative differences in circum-stances. For example, a drug-relatedhomicide may be the result of a conflict

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Chapter 1


over turf or unpaid debts, whereas a domestic homicide may be the product oflong-simmering animosities between partners.

Crimes may have distinctive geographicpatterns for two underlying reasons thatoften overlap:

■ First, crimes must have victims, andthose victims (or their property) havedefinite geographic coordinates at any given moment, although thesecoordinates can shift, as in the case of vehicles.

■ Second, some areas in cities, suburbs,or rural areas have persistently highrates of crime, so that for certainneighborhoods there is a rather perma-nent expectation that crime is a majorsocial problem. For example, Lander’s(1954) research on Baltimore revealedhigh rates of juvenile delinquency tothe east and west of downtown from1939 to 1942. Some 60 years later,the pattern has changed a little but isbasically the same.

A broad-based discussion of the causes ofcrime is beyond the scope of this guide,and the reader is referred to the substan-tial literature of criminology for guidance.However, we can consider issues withmore obvious geographic implications,and we can do this by moving along acontinuum of scale from the macro to themicro level.

On the macro scale, interpreted here tomean national or regional, geographicvariation is apparent for some crimes,notably homicide, in the United States.

Although the pattern has decayed some-what in recent decades, a stream ofresearch has addressed what has beencalled by some the southern violence con-struct (SVC), the tendency for high ratesof homicide to be concentrated in theSouth. Similar regional variations are seenin other countries. In India, for example,high homicide rates are seen in the dense-ly populated northern states.

On the intermediate scale, we see varia-tions in crime rates among cities, althoughmuch of the apparent variation can beexplained by boundary effects. In otherwords, “underbounded” central cities(where the urbanized area spills overbeyond the city limits) tend to have highrates, since their territories exclude low-crime suburbs. Conversely, “overbounded”cities (such as Oklahoma City) tend tohave low rates. However, by no meansare all explanations of rate variation necessarily found in boundary anomalies.Cities differ in social structure, traditions,mores, the strength of various social insti-tutions, and other conditions relevant topotential criminality. These include eco-nomic conditions, the impact of gangs,and gun and drug trafficking. The widevariation in homicide rates among cities isrepresented by figure 1.15, which alsoreinforces the point that similar numbersof incidents may yield vastly differentrates. (For an interesting example ofinterurban comparisons examining multi-ple factors, including gangs, guns, anddrugs, see Lattimore et al., 1997.)

On the micro or intraurban scale, a broadarray of environmental factors must betaken into account if crime patterns are tobe understood. Arguably, the most impor-

26 Chapter 1

tant general principle is usually known asdistance decay. This is a process thatresults from another behavioral axiom,the principle of least effort, suggestingthat people usually exert the minimumeffort possible to complete tasks of anykind. Distance decay (see also chapter 6)is the geographic expression of the princi-ple of least effort. As shown in figure1.16, the relationship between the number of trips and distance is represented by aline showing that people take many shorttrips but few long ones. This principle hasbeen observed to apply to a broad rangeof behaviors, including shopping, healthcare, recreation, social visiting, journeyingto work, migration, and last but not least,

journeying to crime. It is possible to createfamilies of distance-decay curves to repre-sent different classes of movement behav-ior. For example, shopping trips can bedivided into convenience and comparisontypes. Convenience shopping is character-ized by many very short trips becausemost people will get items such as milkand bread from the closest possiblesource. Comparison shopping occurs whenbuyers need big ticket items such as appli-ances, cars, houses, and college educa-tions. Longer trips in search of moreexpensive goods and services are thoughtto be worthwhile because price savingsproduced by better deals will pay for thelonger distances traveled, at least in theory.

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Figure 1.15

A chart showing meanannual homicide countsand rates for 32 U.S. cities,1985–1994.

Source: Lattimore et al.,1997, figures 2–4, p. 10.

Figure 1.16

A diagram representing therelationship between dis-tance and the intensity ofinteractions, also known asthe distance-decay concept.



Similar reasoning might be applied tocriminal movements, although not enoughdata have been published to permit muchin the way of generalization. The pioneer-ing work of Frisbie et al. (1977), inMinneapolis, showed that more than 50percent of residential burglary suspectstraveled less than half a mile from theirhomes to their targets. Commercial bur-glars went somewhat farther, with some50 percent of incidents occurring within0.8 miles. Stranger-to-stranger assaultshad a wider range, with the cumulative50-percent threshold not accounted foruntil a radius of about 1.2 miles from theoffenders’ homes. Commercial robbers alsoreached the cumulative 50 percent of inci-dents at about 1.2 miles. However, a larg-er proportion of the commercial robberstraveled longer distances compared withthose who committed the other crimes,presumably to locate suitable targets andalso to avoid the recognition that maycome with robbing the corner store. Traveldistances tend to reflect population densityand other characteristics of the physicalenvironment (such as the geography ofopportunities)15, so it is unlikely that dis-tance-decay curves for crimes could everapply universally. Nevertheless, the con-cept of distance decay is still a useful one,even if curves for specific crimes cannot becalibrated very accurately.

Although journeys to crime vary amongcrime types and with the demographiccharacteristics of offenders, targets or victims tend to be chosen around theoffender’s home, place of work, or otheroften-visited locations. If your home isburglarized, the chances are that the burglaris a not-too-distant neighbor. The long-established prevalence of violence among

intimates is further confirmation of theidea that most interactions—includingnegative ones—occur at short range.

Distance decay is a useful general concept,but a detailed understanding of the finepoints of local crime patterns demandsdetailed local knowledge. Where are theneighborhoods that are experiencing thegreatest social stress? What are the patternsof mobility of the population? Who are themovers and shakers in the drug and gangscenes, and do their movements affect crimepatterns? What changes are going on?

Other theoretical perspectives

Two of the more compelling theoreticalperspectives deal with routine activities(Cohen and Felson, 1979; Felson, 1998) and criminal spatial behavior(Brantingham and Brantingham, 1984).In the routine activities interpretation,crimes are seen as needing three ingredi-ents: a likely offender, a suitable target,and the absence of a guardian capable ofpreventing the criminal act. Guardian isbroadly interpreted to mean anyone capa-ble of discouraging, if only through his orher mere presence, or interceding in, crim-inal acts. The mention of guardians begsdiscussion of the density paradox. Thisrefers to the idea that, on the one hand,high population densities create a highpotential for crime because people andproperty are crowded in small spaces.There are many likely offenders and suit-able targets. On the other hand, surveillanceis plentiful, and criminal acts in publicspaces are likely to be observed by others,who, however unwittingly, take on the

28 Chapter 1

role of guardians. Crime can be preventedor reduced by making people less likely tooffend (by increasing guilt and fosteringdevelopment of the “inner policeman”who tames criminal impulses), by makingtargets less available, and by makingguardians more numerous or effective. Theprocess of making targets less available invarious ways has become known by thegeneric term situational crime prevention(Clarke, 1992).

Putting the routine activities approach andits sibling, situational crime prevention,into a geographic context involves askinghow each element is distributed in geo-graphic space. Where are the likelyoffenders? (What is the geography of theyouthful male population?) Where are thesuitable targets? (What is the geographyof convenience stores, malls, automatedteller machines, poorly illuminated pedes-trian areas?) Where are the guardians?(What is the potential for surveillance,both formal and informal, of targets orareas that may contain targets? Where arethe public or quasi-public spaces that lacksurveillance and are ripe for graffiti andother incivilities?)

The perspective that focuses on criminalspatial behavior develops a scenario inwhich the motivated (potential) criminaluses cues, or environmental signals, toassess victims or targets. Cues, or clustersof cues, and sequences of cues relating tothe social and physical aspects of theenvironment are seen as a template thatthe offender uses to evaluate victims ortargets. Intimately tied to this process isthe concept of activity space, the area inwhich the offender customarily movesabout and that is familiar to him or her(Brantingham and Brantingham, 1984).

At the micro level of analysis, these con-cepts are useful in that it is known thatactivity spaces vary with demographics.For example, younger persons tend tohave constricted activity spaces. They donot usually have the resources to travelfar. Historically, women have had moregeographically limited activity spaces thanmen due to the higher probability thatmen would work farther from home andthat their jobs would be more likely togive them greater mobility. This is lesstrue today but is still valid to some degree.

Analysts considering crime patterns froma theoretical perspective might think interms of putting the crimes of interestthrough a series of “filter” questions. Themost obvious is the question, How impor-tant is geography in explaining this pat-tern? (Is the pattern random, or not? Ifnot, why not?) Can routine activity theoryor criminal spatial behavior theory helpexplain this pattern? Is this pattern normalor unusual for this area? If the pattern isan anomaly, why is this? What resourcescan be brought to bear to better under-stand the social and other environmentaldynamics of the area of interest? Analystscan take their intimate knowledge of thelocal environment and develop their ownset of diagnostic questions, which couldbe the foundation of an analytic model.

The basic point of this section is to sug-gest that a systematic approach to analy-sis rooted in theory may yield moreconsistent results with a deeper level ofexplanation. This is not to say that ana-lysts need to be preoccupied with whethertheir work is consistent with all theresearch ever done but, rather, to advo-cate a thoughtful research design consis-tent with some of the more widely

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accepted concepts in the field. As noted,this short explanation cannot do justice tothe rich array of material dealing with theory in this realm. Readers who maywish to follow up should consult Eck andWeisburd (1995) and the other referencescited previously.

A note on cartograms

Maps that distort geography to emphasizea specific type of information are calledcartograms to imply a combination ofmap and diagram. The media often publish maps showing world or U.S. population data as cartograms, with theareas of countries or States proportional totheir populations. Cartograms may alsorepresent linear data by showing traveltime, for example, rather than physicaldistance between places.

Cartograms have not found widespreadapplication in crime mapping, althoughthey could be useful. For example, urbansubdivisions could be shown with theirareas proportional to the number of crimeincidents. The major limitation in usingcartograms for crime data is the lack ofsoftware availability to permit their easypreparation. Historically, cartograms havebeen labor-intensive projects, each need-ing to be custom drawn. When viewedfrom a cost-benefit perspective, the novel-ty and impact of cartograms for crime datahave not been seen as worth the cost.

Reminder: Informationis inevitably lost in theprocess of abstraction

Cartographer Mark Monmonier (1991)pointed out that the three fundamental

elements of maps—scale, projection, andsymbolization—can each be distorted. Increating the abstraction called a map, lossof information is taken for granted. Giventhat there will be information loss, thequestion is whether we are properly repre-senting the “residual” information leftafter the data are reduced to manageableproportions. As noted earlier in this chap-ter, our maps may “lie” as a result of sinsof omission or commission. We may for-get to do something and get errors as aresult, or we may do something that cre-ates errors—or both.

Because all abstractions lie in some way,we come full circle to the awareness ofboth art and science in cartography. Ascrime mappers, we should maintain abackground awareness that we may makeartistic (design) decisions that obfuscate.We may make scientific decisions thatmisinform. Could our map sidestep adegree of accuracy that it might otherwisehave achieved? (How should these databe preprocessed? Should the mean or median be used to characterize thesedata?) More often than not, only the analyst/cartographer knows for sure howtruthfully a map conveys its message, so she or he has considerable ethicalresponsibility.

A note on the maps in this guide

Please note that the publication processputs some limitations on the quality of the maps used as examples in this guide.While crime analysts are typically able toproduce high-quality maps, often in largeformat for display purposes, we are limitedhere to small maps that were usually in

30 Chapter 1

larger format when produced. Fortunately,we are able to produce maps in color,which helps enliven the visual messageand also helps to convey the various concepts involved. Loss of definitionoccurs in some cases because the originalhas been scanned or resized, or both. Insome cases, only a low-resolution originalwas available, yielding a low-resolutionreproduction.

Thus most of the maps seen here arecompromised in some respects and areunlikely to be the perfect exemplars thatwe would prefer. Perhaps “do as I say butnot necessarily as I appear to have done”would be a fair warning! Ideally, ofcourse, maps should be clear and crispwith appropriate shading or symbolizationand legible labels and headings. If that isnot the case here for any of our examples,please accept our apologies. The originalauthors of the maps who have so gener-ously consented to their use here are notto blame for any shortcomings in theirreproduction or adaptation.

Purists may be shocked by the presenta-tion of so many maps that lack some ofthe basic elements normally consideredessential in map design, such as a scale, anorth arrow, or even a legend. Relativelyfew maps have what may be called classicgood looks. The maps used here wereoften selected for their ability to illustrateone central point, and strict conformity toclassic design criteria was not seen as acritical determinant for inclusion. Indeed,had the strict classical criteria beenenforced, this volume would not existbecause, as noted elsewhere, few maps inany field go by the book. It would have

been necessary to modify most maps tomake them conform to strict standards,and this was simply impractical.

Some maps identifying specific neighbor-hoods in specific cities in sufficient detailto permit the possible identification ofindividual residences have been construct-ed using hypothetical data or have beenotherwise fictionalized to preserve privacy.It is not my intent to present accurate ren-ditions of crime patterns in specific citiesbut, rather, to illustrate elements of designand map application. No map reproducedhere should be used as a reliable guide toactual crime patterns in actual places.

Summary

Chapter 1 has explained:

■ What this guide is about and howcrime mapping fits in the historicalcontext of mapmaking.

■ Why cartography is both an art and ascience.

■ Why it is important to balance costsand benefits when considering mapdesign and production.

■ That maps can represent informationrelating to both time and geographicspace.

■ The meaning and significance of mapprojections and coordinate systems.

■ The traditional elements that help provide consistency and interpretabilityin maps.

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■ The types of information provided bymaps.

■ Measurement systems and their relevance to mapping.

■ The meaning of the term “thematicmap.”

■ The ethical responsibilities of crimemappers.

■ The difficulties associated with theblack and white reproduction of mapsthat were originally in color.

■ The importance of thinking about thecauses underlying the patterns that wemap and analyze.

■ The meaning and possible applicationof cartograms in crime mapping.

■ Why we should realize that we can lie with maps just as we can lie withstatistics.

32 Chapter 1

What’s Next in Chapter 2?

■ What crime maps should do and how they should do it.

■ How to choose the right kind of crime map.

■ Types of thematic maps.

■ Why the data should be explored.

■ How to choose class intervals in numerical data.

■ What is involved in crime map design.

■ How crime map design, abstraction, and legibility are related.

Notes

1. Photographs were not very usefulbecause they had to be enlarged to animpractical size to be legible.

2. From the Latin c(h)art(a) and Greekgraph(os), something drawn or written,hence “chart drawing.”

3. India ink is a mixture of lampblack and glue.

4. This grid is also known as a graticule,a term meaning the spherical pattern ofmeridians (north-south longitude lines)and parallels (east-west latitude lines).See Campbell, 1993, p. 23.

5. Mercator’s projection, developed byand named for Gerhardus Mercator(1512–1594), a Flemish cartographer, isuseful for navigation because a straightline on the map represents a true directionon the Earth’s surface.

6. Equal-area projections, as the nameimplies, accurately represent area, but notshape. This type of projection shows areadistributions such as vegetation, in whichthe area shown is critical.

7. Conformal projections retain correctshapes. An added advantage is that, ifshape is retained, so is direction.

8. Transverse Mercator has the projectioncylinder fitted horizontally—i.e., east-west,rather than tangentially to the Equator ornorth-south.

9. Latitude is relatively easy to determinewith reference to “fixed” objects such asthe North Star or the Sun. Longitude, asnoted earlier, poses greater difficulties. It isknown that the 360 degrees of longitudedivided into the 24 hours of the day meansthat each hour is equivalent to 15 degreesof longitude. Thus if the time at a knownpoint could be maintained on a ship atsea, longitude could be calculated basedon the time difference as established local-ly from the occurrence of the noon Sun.The problem, until John Harrison’s inven-tion of the reliable marine chronometerin 1761, was that no clock was able toretain its accuracy under the difficult con-ditions of a sea voyage. The extraordinarysaga of Harrison’s success is found inSobel (1995). (I learned more than I caredto about this problem on a trip on a 30-footsailboat from Honolulu, Hawaii, to SantaBarbara, California, when the crew forgotto set the chronometer on leaving Honolulu.The crew always knew their approximatelatitude by using a sextant but neverknew their longitude. Fortunately, theykept moving east and eventually bumpedinto North America.)

10. In case inquiring minds want to know,1 nautical mile, or “knot,” is equivalent to1 minute of latitude, or 1,852 meters(6,076.12 feet). One nautical mile is alsoequivalent to 1.1507 statute miles (Dent,1990, p. 40).

11. This happens because a GIS will try toput both locations on the same map in theoriginally specified map units. This mayresult in the map changing to extremelysmall scale (see explanation of scale in thischapter) as the GIS tries to make Stateplane coordinate values into degrees oflatitude/longitude, placing the new data far,far away. The GIS may try to show datafor small areas in Brooklyn and what itreads as Afghanistan or Antarctica on thesame map.

12. The term orientation is derived frommedieval T-in-O maps. The “O” was theworld ocean; the “T” was formed by theMediterranean Sea (vertical), the DonRiver (left top of the T), and the NileRiver (right top of the T). Paradise in thisstyle of map was typically at the top, inthe east. Hence the verb to “orient” a mapcame to mean adjusting it to its properdirection in relationship to the Earth. Today,however, we orient a map so that north isat the top, which is something of a mis-nomer (Campbell, 1993, pp. 246–247).

13. From the Greek choros for place andplethos for fullness or crowding, hence“area crowd.”

14. Isolines based on continuous data,such as temperature or topography, arereferred to as isometric lines. Actualmeasurements can be made to providecontrol points instead of averaging pointsover areas.

33


Chapter 1


15. The term “geography of opportunities”refers to the idea that the world offersabundant possibilities for crime that maybe more complex than we realize at firstglance, encompassing both qualitative andquantitative dimensions. For example,

although rich neighborhoods may notoffer more opportunities than poor neigh-borhoods for, say, property crime, eachopportunity may offer the perpetratorhigher value because the goods stolenmay be worth more.

34 Chapter 1

35


Chapter 2

Chapter 2: What Crime MapsDo and How TheyDo It

What crime maps do

Maps are often thought of solely as display tools. Infact, maps have a wide-ranging role in the process ofresearch, analysis, and presentation. Mapping is mosteffective when those broad capabilities are recognizedand used to their fullest extent. The map is the endproduct of a process that starts with the first-respondingofficer’s report that is processed by data entry person-nel, entered into a database, and transformed into asymbol on paper. In this narrow interpretation, a mapis merely a picture or part of a database. But mapscan be useful in other ways. MacEachren (1994) andMacEachren and Taylor (1994), following DiBiase(1990), noted the distinction between visual thinkingand visual communication in the use of maps andgraphics (figure 2.1).

Visual thinking

In visual thinking, the map is used to generate ideasand hypotheses about the problem under investigation.By inspecting a map, for example, we may notice arelationship, or correlation, between environmentalfactors that otherwise might have gone unnoticed.This correlation may be vertical in the sense that wesee connections between different phenomena, such

What Crime Maps Do and How They Do It

36 Chapter 2

as crimes, land uses, and demographics.Alternatively, we may see a horizontalrelationship in which we recognize a com-mon factor across a particular crime type,such as graffiti in similar types of crimelocations. Visual thinking is a private activ-ity involving exploration and confirmation.

In the exploratory phase, maps may becrude and are not intended for display orpublication. A computer-printed map ofburglary patterns for the most recent weekmight be marked with handwritten infor-mation provided by investigators or withother data not in digital form. Informationmight be transcribed from a mental mapto a paper map. Another possibility is that

the tools of exploratory spatial data analysis(ESDA) are used to find anomalies indata, such as an unexpected cluster ofincidents, that could point to unexpectedrelationships.

At this stage the analyst may generate a formal hypothesis, or educated guess, to explain the process producing theobserved crime pattern. Did the observedcluster of burglaries pop up by chance? Isthere some recognizable cause? Is a serialburglar operating in the area? Do officersin the field have insight to offer? Bydeveloping a hypothesis, the analyst isin the mainstream of scientific research,using a venerable methodology—the

Visual Thinking versus Visual Communication

■ Visual thinking is abstract and internal. Some ideas for putting datainto maps, charts, or other media may never see the light of day.

■ Visual communication is a tangible expression of visual thinking. It isputting thoughts about data and processes into a format others cansee and understand with minimal effort.

Figure 2.1

The roles of graphics in theresearch process: Visual think-ing and visual communication.

Source: DiBiase, 1990.Reproduced by permission.

so-called (and awkwardly called) hypothetic-deductive method.

Maps and other graphics are integral toolsfor exploration and hypothesis testing. Dopreliminary maps confirm the hunch thata burglary pattern is likely the product ofa repeat offender who is using a busroute, and apparently a specific bus stop,to visit a neighborhood and commit hisoffenses? If so, the preliminary informa-tion will help the hypothesis gel intosomething useful.

At the core of this method is a potentiallyrepetitive process involving:

■ Development of a hypothesis on thebasis of the best available informationderived from both theory and fielddata.

■ Development of a method for testingthe hypothesis, perhaps involving sta-tistical and graphic testing or modeling.

■ Analysis of the data.

■ Evaluation of the results.

■ A decision to accept or reject the orig-inal hypothesis.

■ Reevaluation of the original hypothe-sis, if it was unsatisfactory. It may bemodified to take into account newknowledge. If so, the process beginsanew.

The confirmation stage tells the analystwhether the hypothesis does indeed havea factual basis that will withstand scruti-ny. If it does not, we reevaluate and makenecessary adjustments, perhaps gatheringmore information to add depth to whatis already known and to shore up thehypothesis, which itself may now havebeen modified to take new data intoaccount.

MacEachren (1994) cautions that investi-gators should realize that maps and othergraphics are prone to error resultingfrom their underlying data, inappropriatedesign, or even the margin of error

37


Chapter 2

What’s Hypothesis Got to Do With It?

Will the typical crime analyst go through the rigmarole of visual thinkingand visual communication—plus the process of hypothesis testing? It’snot likely, since most analysts work under tough deadlines with inade-quate resources. (Just like everyone else!) Also, much of the work is prescribed, routine, and repetitive, leaving little flexibility for research.

So, what’s the point? The formal structure outlined here is an ideal modelfor map-related research—a paradigm or modus operandi. Thus it is unlikelyto be replicated often in practice. Like other models, it provides an idealguide and enables the analyst to apply whatever parts of the processcan be applied in the time available.


introduced by the normal process ofabstraction. If possible, the analyst shouldnot rely on any one data source, whetherit be a map, field observation, or survey, ifother sources can be used to complementeach other.

Visual communication

As we move from visual thinking to visu-al communication, we go from the privaterealm to the public activities of synthesisand presentation. Synthesis implies merg-ing various types of information—in thiscase, geographic information system (GIS)layers—into a coherent final product.Although synthesizing is essentially scientific, human judgment is at the core of this filtering and refining process.

Synthesis is assisted by the ability to findoverlaps (intersections) between layers in aGIS. But even then decisions have to bemade about what to put in, what to leaveout, and what importance to attach toeach layer. A presentation puts all the rele-vant pieces together in a map. The map canbe highly persuasive if it provides informa-tion germane to the question at hand and iswell designed. As MacEachren (1994, p. 9)noted, “People believe maps.”

How crime maps dowhat they do

A detailed discussion of how maps com-municate through processes of visualcomprehension is beyond the scope of this guide. However, a few points aremade here to explain the underlyingprocess and underscore the idea that peo-ple see maps differently due to differences

in, for example, their eyesight, aptitudefor visual comprehension, and prior train-ing. A background problem that goeslargely unrecognized in the community ofmapmakers is that, for some people, mapshave no meaning. They may grasp nei-ther scale nor symbolization. As a result,they have no sense of distance, relative orabsolute, and are unable to draw mean-ingful conclusions from a map.

This problem is, in part, a legacy of thedisappearance (until recently) of geogra-phy from school curriculums. But it maygo deeper, seemingly having to do withgender- and race-specific differences inpersonal mobility that, in turn, may hin-der the development of spatial experienceand reduce individuals’ abilities to takeadvantage of maps as tools. For example,in the past, women’s traditional roles inchildrearing have limited their mobility,thus denying them opportunities to learngeography by directly experiencing places.Race has had a similar indirect effectthrough the mechanisms of discriminationand depressed economic status. Insofar asminority groups have experienced dispro-portionate levels of poverty, their mobilityhas been limited and their geographiclearning correspondingly stunted. (SeeMontello et al., 1999, for a discussion ofrelated questions.) While the police are verygeographically aware, in part due to muchfield experience, individual members ofthe community may not be. An argumentmight be made for giving special attentionto maps intended for the community. Forexample, digital photos of landmarkscould be embedded in a community mapas visual anchors to show residents howthe map relates to their environment.

38 Chapter 2

All messages, including maps, are lacedwith nuance. “The medium is the mas-sage,” wrote McLuhan and Fiore (1967),arguing that literate people had been rendered visually incompetent by anexcessive dependence on text. Since thatfamous remark, personal computers haveprovided an interactive platform, allowingwhat is, in effect, environmental manipu-lation on the fly. Maps, text, and datahave moved from the realm of the passiveto the active and interactive, encouragingperception of the map as a tool ratherthan as a mere display device.

Peterson (1995) has outlined several theories and models that have beenadvanced to explain how visual informa-tion is processed:

■ Stage model. Visual informationmoves through three memory stages.The first (iconic) is very short anddeals with initial recognition. The sec-ond (short-term visual store) is longerbut has less capacity so complexitybecomes an issue. Moving from iconicto short-term demands attention. Theinformation is then sent to long-termvisual memory. Long-term images pro-vide cues to help with recognition ofnew visual stimuli.

■ Pattern recognition theory. Iconicimages are converted into somethingrecognizable through pattern matching.

■ Computational model. This sophisti-cated three-dimensional model is simi-lar to the process of abstraction incartography. (For additional discus-sion, see Peterson, 1995, chapter 1.)

These theoretical considerations arereminders that producing a map is onlyhalf the story. We also have to be con-cerned with how it is interpreted by theintended audience. The storage of cuesfor the interpretation of visual images inlong-term memory means that familiarityprovides a substantial advantage in theinterpretation of maps. We may be oblivi-ous to the fact that our map is extremelyfamiliar to us but means little or nothingto those who have no reference points intheir long-term memory or who have hadinsufficient time to study and process thedetails.

Another way of visualizing the process ofmoving a concept from the analyst to themap user is illustrated in figure 2.2, show-ing that the cartographer’s and map user’srealities are both abstractions of reality.The cartographer creates a cartographic

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Chapter 2

Figure 2.2

A model of cartographic communication.

Source: Peterson,1995, figure 1.1, p. 5. Adapted by permission ofPrentice Hall, Inc.,Upper Saddle River,New Jersey.


abstraction and translates this into a mapthat is read by the map user and trans-ferred to the user’s mind.

Choosing a crime map

Chapter 1 characterized thematic maps asfalling into the following broad categories:statistical, point, choropleth, isoline, sur-face, and linear. How do we choose themost appropriate type for mapping crimeand crime-related phenomena? Some deci-sions jump out at us while others are opento interpretation.

For example, if we want to see the preciselocations of burglaries for the last month,then we use a point map of addresses ofincidents. Or perhaps a city council mem-ber has asked the police department for amap summarizing the number of incidentsof graffiti per structure by city neighbor-hoods. This calls for a choropleth map,with neighborhood boundaries making upthe geographic units. Links between victimand offender residences demand a linearrepresentation. A generalized picture ofcrime risk or incidents is seen best with anisoline or surface map, and census infor-mation depicting the relationship betweenpoverty and race can be shown usingeither a statistical or choropleth map.

Because of the infinite potential combina-tions of crime-related conditions that canbe depicted on maps, we can combinemap types to put more information on thesame map. For example, we can combinenominal and ratio data, such as a choro-pleth map of drug-related crime by patrolbeats and add the locations of drug mar-kets on the same map. Crime mappersshould be aware of the potential for

combining thematic map types, providedthat the result is not overloaded withinformation—or just plain incomprehensi-ble. An overloaded map will have somuch information that the eye is unableto take it all in. It will prevent the readerfrom discriminating between what isimportant and what is not.

Examples of thematic maps

Perhaps the best way to get a feel for thekinds of maps used to display crime data isto look at examples and to think about whyeach type of map was selected. A good placeto start is the Web site of the NationalInstitute of Justice Crime Mapping ResearchCenter (http://www.ojp.usdoj.gov/cmrc),which provides links to police departmentsacross the United States. Another usefulWeb site is maintained by Hunter College inNew York (http://everest.hunter.cuny.edu/capse/projects/nij/crime.html). (See theappendix for additional information.)

Thematic maps using point symbols: The dot map

When should point symbols be used? Thefirst prerequisite is that you have locationaldetail—information specific to your points,such as street addresses or coordinates inlatitude/longitude or some other system,such as State Plane (explained in chapter 1).The second prerequisite is that the audi-ence needs locational detail. If you havepoint data, but the audience wants infor-mation summarized by patrol areas orneighborhoods, then the point data can beadded up, or aggregated, to the areas ofinterest. Examples of point, or dot, mapsare shown in figures 2.3 and 2.4.

40 Chapter 2

41


Chapter 2

Figure 2.3

A point data map discriminating amongthree crime categorieswith different symbolsand colors.

Source: San Diego,California, PoliceDepartment.Reproduced by permission.

Figure 2.4

A point data map ofdrug arrests using different symbols and colors.

Source: San Diego,California, PoliceDepartment.Reproduced by permission.


When there are too many points to bemapped, using point data may result in amess of superimposed points that have lit-tle or no meaning. This could happen ifcalls for service are mapped using address-es in a large city. The point data may needto be summarized by areas to make thedata legible. Point maps also get toocrowded if long time periods are summa-rized for more frequent crime categories.Thus, even though you have reasonablyprecise locational information, aggregationby areas in the form of a choropleth mapmay yield a more legible map than thepresentation of each individual point.

Thematic maps using statistical symbols

At its most primitive, a statistical mapconsists of raw numbers written in thesubdivisions of the map. The advantage isthat the reader can see exactly what thestatistic is. The downside is that mapsdesigned in this way are difficult to read

quickly. It could be argued that, in effect,they defeat the purpose of the map, whichis to facilitate visualization of the data.Admittedly, this form of map does putdata in its geographic context, but in aninconvenient format. Cartographers arguethat if you want to see only the raw num-bers, then a table, not a map, is needed(see next section, “Thematic maps usingarea symbols”).

Statistical symbols commonly take theform of pie charts, bar charts, graduatedcircles, or dots representing incidentcounts (dot density) placed in the relevantmap subdivisions (figure 2.5). This allowsmultiple variables to be mapped at thesame time. Examples could include barcharts with bars representing both crimeand poverty or graduated circles like thosein figure 2.6, showing the U.S. House ofRepresentatives vote on an Omnibus DrugBill provision requiring a 7-day waitingperiod for the purchase of handguns. Atfirst glance, the symbols in figure 2.6 look

42 Chapter 2

Figure 2.5

Thematic mapping optionsin MapInfo®, a desktop

mapping program.

Source: MapInfoCorporation, Troy,

New York.

like pies, but circle segments are all 90degrees. This is actually a graduated circlemap, in which the area of the 90-degreesegments is proportional to the number ofyes votes (top part of the circle) or novotes (bottom part of the circle), with theleft side of the circle representing votes byDemocrats, the right by Republicans.1 Themap shows both nominal data (party affil-iation and yes/no votes) and quantitative

data (the number of votes), as well aslocation of votes by State. Although read-ing this map takes some effort, it is rich ininformation and gives that information aclear geographic context.

More typical graduated symbol maps usedin crime analysis applications are shownin figures 2.7, 2.8, 2.9, and 2.10. Notethat points and proportional circles can be

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Chapter 2

Figure 2.6

A graduated circle mapshowing a U.S. House ofRepresentatives vote onan omnibus drug billprovision requiring a 7-day waiting period for the purchase ofhandguns.

Source: T. Rabenhorst,University of Maryland,Baltimore County,Maryland. Reproducedby permission.

Figure 2.7

A graduated circlemap showing aggra-vated assaults by census block group,Baltimore, Maryland.

Source: Keith Harriesfrom Baltimore City,Maryland, PoliceDepartment.


44 Chapter 2

Figure 2.8

A graduated circlemap showing conven-ience store robberies,

Baltimore County,Maryland.

Source: Philip Canter,Baltimore County,Maryland, Police

Department.Reproduced by

permission.

Figure 2.9

A graduated circlemap showing

robberies in OverlandPark, Kansas.

Source: SusanWernicke, Overland

Park, Kansas, PoliceDepartment.

Reproduced by permission.

combined if this helps convey the essen-tial information to readers and avoidsoverloading the map. Note also that thesize range for symbols is a judgment call.If the size range is too small, readers willhave difficulty extracting meaning fromthe map. Also, some symbols are moreeffective than others in conveying themessage. Solid symbols probably workbest in most cases because they engagethe eye more effectively.

A disadvantage of using statisticalsymbols on maps is that they may over-lap one another and result in an illegiblemess. Map design must take into accountthe final size of the map, the scale to beused, and the possibility of overcrowding.

The use of statistical devices of variouskinds on maps is limited only by the ana-lyst’s imagination. For example, it may beuseful to accompany a map with a scatterdiagram showing a collateral relationship,such as calls for service by time of day(chart) and calls for service by location(map). Mapping software offers numerouspossibilities because the programs usually

can make both charts and maps and combine them in layouts in useful ways.

Thematic maps using area symbols

When we think of making maps that rep-resent areas, it’s the choropleth map thatusually comes to mind, with administra-tive or political areas shaded according totheir statistical values, whether they arefrequency counts, averages, or other rele-vant measures. When is a choropleth mapappropriate? Strictly speaking, mappingtotal numbers, such as crime counts,using choropleth mapping is unacceptableowing to the misleading impression givenby unequal areas. For example, if thelargest and smallest areas have the samefrequency, they will be shaded the sameon the map, which fosters possible misinterpretations based on per capita ordensity considerations. However, manydepartments overcome this by using aregular grid for choropleth mapping. Thishas the advantage of equalizing areas butthe possible disadvantage that the units ofthe grid may not be “natural” local areas.

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Chapter 2

Figure 2.10

A graduated symbolmap showing calls forservice in a San Diegoneighborhood.

Source: San Diego,California, PoliceDepartment. Reproducedby permission.


In other words, the areas exist only onpolice department maps and may be diffi-cult to interpret in the field.

For places with boundaries following therectangular land survey, a grid of squaremiles (sections) or quarter-square mileswill be substantially visible in the localstreet pattern system. This applies to mostareas west of the Appalachian Mountains.East of the Appalachians, survey systemswere usually based on irregular “metesand bounds” and do not lend themselves

to grid-based maps, except where citieshave a regular block grid. (See also chap-ter 4, “Definition in geographic space.”)Examples of choropleth maps are shownin figures 2.11 and 2.12.

Choropleth maps are best used for areaaverages, such as crime rates, populationdensity, and percentages, as well as nominal-scale information such as landuse. Care is needed in the interpretation of all maps, and choropleth maps are noexception. Take, for instance, a choropleth

46 Chapter 2

Figure 2.11

Choropleth maps showingresidential burglaries in

Baltimore County, Maryland.

Source: Philip Canter,Baltimore County, Maryland,

Police Department.Reproduced by permission.

map of a crime rate based on population.Map values are expressed in terms ofnumbers of crimes per population unit.But what population? Normally, the resi-dential population as enumerated in thecensus is implied. What about downtownbusiness areas with negligible residentialpopulations? They still have crimes andthe statistical effect is to inflate the crimerate. Is this realistic?

One could take this scenario to theextreme: If small areas are used for crimerate calculation, we may have areas withcrimes but no resident population, producingan infinite crime rate! Bear in mind thatthe crime rate concept is a loose one and

crime rate maps are only an approximation,particularly for smaller areas. Althoughneither the offenders nor the victims arenecessarily residents of the areas wherethe crimes happen, they would neverthe-less be represented in crime rate maps.

Experience has shown that it may behelpful to add a disclaimer note on somemaps to point out, where appropriate, thatsome areas with high crime rates havesmall or zero residential populations.Another form of this disclaimer wouldnote that rates have not been calculated,or have been intentionally omitted, forareas with small or no residential population.This will have to be handled carefully,

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Chapter 2

Figure 2.12

A choropleth mapshowing land uses(choropleth colors) andauto thefts (proportion-al circles) in OverlandPark, Kansas.

Source: Susan Wernicke,Overland Park, Kansas,Police Department.Reproduced by permission.

Rule of Thumb

Avoid generating choropleth maps of crime rates for small areas like cityblocks because spurious results could be produced for areas where thereare crimes but no residents.


however, since some readers may con-clude that the police department hassomething to hide if data are manipulatedin what appears to be a selective way.

Note that choropleth maps can be producedin a three-dimensional format with theheight of each area proportional to its datavalue.2 The advantage of this type of pres-entation is visual appeal and vividness. Thedisadvantages are that it can be difficult todecipher the actual data value, and that atall column will hide other areas, as is thecase in three-dimensional surface maps.

Thematic maps using surfaces

Although crime maps using isolines have been around in some form since the1960s, they have only recently becomewidely used.3 This is a result of the avail-ability of algorithms in mapping programsthat perform complex calculations at high speed. With the addition of three-dimensional capabilities, surface mapswith textured surfaces are now within thereach of most crime analysts. Such mapsare tempting as they are visually moreappealing than two-dimensional rendi-tions. But the same caveats noted aboveapply. Just because a surface map orthree-dimensional rendition can be pro-duced, it does not necessarily follow thatit is the most appropriate or useful formfor visualizing the data. For example, itmay be difficult to add legible landmarkicons or even boundaries to a three-dimensional map, depending on factorssuch as scale and amplitude, or the degreeof peakedness, of the map, as well as theangle of view. (See figure 1.14 and chap-ters 4 and 6 for other examples of three-dimensional maps.)

Ultimately, deciding whether to use a sur-face map involves balancing scientific andartistic judgment, and in many cases thedecision can be made only through exper-imentation. Fortunately, maps can be pro-duced rapidly with desktop computers, soexperimentation can and should be a rou-tine part of the mapmaking process.

Thematic maps using linear symbols

Flows between points are shown with lin-ear symbols, with their width or thicknessgenerally proportional to the volume ofthe flow (figure 2.13). Maps of this typehad their origins in economic geography,first showing passenger flows on Irishrailways in the 1830s and, later, commod-ity flows among nations (Campbell, 1993,p. 264). These maps can be used in crimemapping, to show, for example:

■ Links between where vehicles werestolen, where they were recovered, andsuspects’ addresses.

■ Routes between victims’ and offenders’home addresses (e.g., Pyle et al.,1974; Frisbie et al., 1977, p. 88).

■ Passes along streets by patrol cars toillustrate patrol density.4

■ Traffic density.

Virtually no flow maps have been seen inthe recent literature on crime mapping,even though such maps are in use inpolice departments. Their absence is notdue to lack of data. It may be that theapparent lack of this type of map in theliterature is due to the absence of readily

48 Chapter 2

available flow-mapping algorithms in theGIS programs most popular among crimemappers. While it is unlikely that flowmapping will ever be a major componentof crime maps, it will be increasingly usedowing to its obvious utility in limitedapplications.

Classifying map information

Generally, information on maps is classi-fied in some way; data are not symbolizedindividually. For example, all burglariesare shown with the same symbol on apoint map. It would be absurd to showeach crime with its own symbol.

In effect, maps contain two levels ofabstraction:

■ The overall level of detail and the scaleused to present the data.

■ The way data are symbolized, becausethere is a continuum from highly

detailed to extremely generalized in thesymbolization process.

To some extent, the choice of scale con-trols the level of abstraction of the contentbecause it is impractical to load a small-scale (large area) map with local detail.MacEachren (1994, p. 41) argues that forcategorical information, “features that endup in the same category should be moresimilar to one another than features in different categories.”

What does this mean for crime maps? Amap of drug offenses might group relateddrug categories together. Generically relat-ed robberies could be put together in thesame category and symbolized the sameway on a general crime or violent crimemap. If a map were specific to robberies,however, symbolization might be separat-ed into commercial and street or weapontype or time of day. This type of adjust-ment is intuitive and naturally occurs inthe crime context where data are typicallysorted into categories as part of normal

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Chapter 2

Figure 2.13

A thematic map usinglinear symbols thatshows numbers of residential burglaries,arrests, and suspectmovements, Akron,Ohio.

Source: Pyle et al.,1974, figure 42, p. 167. Copyright,1974, University of Chicago.


50 Chapter 2

How Many Classes in a Map?

Use no more than six—and not less than four—classes, or shading levels,of data in a choropleth map.

processing. But the situation becomesmore intricate when moving from nominaland ordinal data to ratio-type data.

It is less obvious how to classify numericaldata when several alternatives presentthemselves. Common mapping softwarepackages offer options, including a default,for grouping numerical data in thematicmaps but rarely explain how to chooseamong these approaches. Dividing up thedata range in a way that best represents it involves the abstraction issue again.

Total abstraction would be represented bythe use of one shade for all areas on themap. This says that there are data, but lit-tle or no specific information is suppliedabout them. At the other extreme, eacharea would have its own shade, and if cityblocks were shown, the map would havethousands of shades. Obviously, neither ofthese alternatives is useful, and the solu-tion lies somewhere between.

Greater accuracy dictates the use of moreclasses of data, although readers pay aprice for this in terms of comprehension asthe map moves along the continuum ofabstraction toward reality and complexity.The underlying question is, What is thismap being used for? MacEachren (1994,pp. 42–43) suggests that if we are in thevisual thinking stages of exploration andconfirmation, we will need more detail(more classes), but as we progress towardsynthesis and presentation it becomes

more important to show general trendsrather than detail, hence fewer classes.Furthermore, limitations on human visualcomprehension must also be taken intoaccount—the limit is about six levels ofcolor or gray scale shading in the contextof a map.

Are there natural breakpoints in crimedata? For example, in a robbery map of acity we could embed the State, regional,and national robbery rates as breakpoints.This might be informative but could get apolitical “thumbs down” if the local juris-diction compares unfavorably. (Conversely,it could be a popular approach.) Choicesavailable to cartographers in common desk-top mapping packages are represented bythe drop-down menus shown in figure 2.14.

The choices available, and the relativeease of using them, invite experimenta-tion. How will a particular database lookwhen mapped in a particular way? Whatmethod conveys the crucial informationwith the least distortion and best visualimpact? Good maps are likely to resultfrom a working environment that encour-ages experiment because it is ultimatelythrough trial and error that most learningis done. This is said not to invite a “shot-gun” approach but, rather, to encouragethe responsible testing of options underthe assumption that alternative methodsof representation are tested for a reasonother than the sake of doing somethingdifferent.

Each of the alternatives typically employedin data mapping is introduced here andillustrated in figures 2.15 and 2.16.

■ Equal ranges or intervals. The datarange (difference between maximumand minimum) is calculated and divid-ed into equal increments so that thewithin-class ranges are the same, suchas 1–3, 4–6, 7–9, and so on.

■ Equal count (quantiles). Approxi-mately the same number of observa-tions is put in each class. The numberof classes determines the technical def-inition of the map (quartile if there arefour classes, quintile if there are fiveclasses, and so forth). The term quan-tile is the generic label for data withobservations divided into equal groups.This software option gives the user theopportunity to enter the number ofclasses desired. (This is the default inMapInfo®.)

■ Equal area. Breakpoints betweenclasses are based on equality of area

rather than equality of range or anobservation count. If areas in a choro-pleth map vary greatly in size, thistype of map will differ from an equalcount map based on the same data. Ifareas are roughly equal in size (suchas city blocks), the result will be simi-lar to an equal count presentation.

■ Natural breaks. In this approach,gaps or depressions in the frequencydistribution are used to establishboundaries between classes. This is thedefault in ArcView®, which employs aprocedure know as Jenks’ Optimizationthat ensures the internal homogeneitywithin classes while maintaining theheterogeneity among the classes.(For more details, see Dent, 1990,pp. 163–165, and Slocum, 1999, chapter 4.)

■ Standard deviation (SD). SD is astatistical measure of the spread ofdata around the mean, or average.In the literature of stocks and mutualfunds, for example, SD is often used

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Chapter 2

Figure 2.14

Choices for class interval determination as presented in two widely used desktop mappingprograms. Left, ArcView®; right, MapInfo®.

Sources: ESRI, Inc., Redlands, California, and MapInfo Corporation, Troy, New York.

Table 2.1. Criteria for Selecting a Method of Classification

Criterion Equal Quantiles Standard Natural Interval Deviation Breaks

Considers

P P G VGdistribution of data along a number line

Ease of VG VG VG VGunderstanding

concept

Ease of VG VG VG VGcomputation

Ease of VG P G Punderstanding

legend

Legend values P VG P VGmatch range

of data in a class

Acceptable for U A U Uordinal data

Assists in P P P Gselecting number

of classes

P=Poor G=Good VG=Very good A=Acceptable U=Unacceptable


as a risk index, since it expresses theamount of price fluctuation over time.In the context of crime, SD can be auseful way of expressing extreme val-ues of crime occurrence or portrayingvarious social indicators. Generally,classes are defined above and belowthe average in units of 1 SD. Thedrawback is that this method assumesan underlying normal distribution, orbell-shaped curve, something of a rari-ty in social data.

■ Custom. As the label suggests, thisoption allows users to determine class

intervals according to their own criteria,such as regional or national norms andthresholds determined for policy reasons.

Table 2.1 summarizes the criteria forselecting methods to define class intervalsfor maps, providing a guide with respectto data distribution, ease of understand-ing, ease of computation, and other stan-dards. (For a comprehensive discussion ofissues relating to the determination ofclass intervals for maps, see Slocum,1999, chapter 4.)

52 Chapter 2

Source: Thematic Cartography and Visualization, T.A. Slocum, 1999, figure 4.7, p. 74.Reprinted by permission of Prentice Hall, Inc., Upper Saddle River, New Jersey.

Maps and statistics:Exploratory spatialdata analysis

Some statistical methods have been men-tioned in the preceding discussion, andconsideration of statistical concepts isunavoidable when considering how bestto visualize numerical data. As noted ear-lier, because we can lie with statistics, wecan also lie with statistical maps. Indeed,maps have been used throughout historyas propaganda tools (Campbell, 1993,pp. 229–235), so potentially we can havehonest error as well as pure cartographicdeceit. Perhaps the greatest danger in themapmaking process is that people tend tobelieve the information in maps (whatMacEachren, 1995, p. 337) called theconnotation of veracity), and they alsobelieve that maps are unbiased (the con-notation of integrity).

But mapmakers, like other elements ofsociety, are culturally conditioned, selec-tively including and excluding dataaccording to the values of the responsibleparties. Given that maps can harbor manypossible errors and biases, both intentionaland accidental, it is incumbent on thecrime analyst to be aware of possiblesources of error and to work to avoidthem. Nowhere is there more scope fordistortion and misinterpretation than in

the preparation of maps based on numeri-cal data. This is due to the potential com-plexity of the information and the infiniteset of display permutations, whether inraw form or as some derivative measuresuch as a rate or percentage.

Mapmakers can gain a preliminary under-standing of what the numbers meanthrough the process of exploratory spatialdata analysis. It is quite helpful to under-stand what the distribution of a set ofnumbers looks like when expressedgraphically. Is this a normal (bell-shaped)distribution with most observations clus-tering around the mean, or average, and afew very low and a few very high values?Is it a skewed distribution with extremevalues to the right (high values) or theleft (low values)?

In the unlikely event of a normal, sym-metrical, bell-shaped distribution, mapscreated by all of the classing methods looksimilar. Almost always, however, real-world data are somewhat skewed, and dif-ferent classing methods produce maps thatlook different and convey different visualimpressions to readers.

Consider in some detail what will happenwhen different methods are applied to adata set that has a strong positive skew(figures 2.16 and 2.17).

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A Note on Skewness

A normal distribution is the familiar bell-shaped curve that is seldom seenin crime data. Most crime data are positively skewed, meaning there is along right "tail" representing a few high values. Hot spots (high crimeareas) are geographic expressions of skewness, which presents difficultiesin mapping numerical data. See the box, How Much Exploration? andfigures 2.15 and 2.16.


Let’s review each histogram (or frequencycurve) and map in figures 2.15 and 2.16,method by method.

■ Equal count. On the histogram, theright tail (highest values of the distri-bution) is prominent because there arefew extremely high values. Thus, theprogram has to seek the lower rank-ordered data values (farther to the lefton the histogram) to come up with the13 observations for the class. (Notethat the number of observations perclass is uneven, ranging from 13 to17.) The resulting map tends to visual-ly exaggerate the seriousness of theproblem because color saturates moremap areas.

■ Equal range. Because the distributionis right-skewed, equal range will tendto favor lower data values. The twolowest classes have 23 and 26 mem-bers, respectively, while the higherclasses have 7 and 3. The map con-trasts with the equal count version,now visually minimizing the problem.

■ Natural break. This method appearsto have struggled to come up with nat-ural breaks, which is a problem, alongwith breaks in awkward places. Theresult here is quite similar to the equalrange breakdown, with cuts betweenclasses shifted to the left (lower val-ues) as compared with the equal

range. It comes as no surprise that theequal range and natural break mapsare quite similar.

■ Standard deviation. Here, the break-down of class intervals is set with ref-erence to the average, or mean, so thatan interval of 1 SD is established tothe left of (below) the mean (blue line,0.47), and above the mean at thesame distance. The effect of this on theright-skewed distribution is a symmet-rical breakdown, with about as manyobservations in the lowest (10) andhighest (9) classes and in the two mid-dle classes (22 and 18). The visualimpression conveyed by the associatedmap is close to the severity of the

equal count method. Thisis due to the similar num-ber of observations in thetop category.

The basic point to be madefrom this discussion is thatcases that may fall in a

given class by one method may be in adifferent class by another. The only cer-tainty is that the highest and lowestvalues will always be in the top and bot-tom map classes, respectively. Whatmethod is preferable? MacEachren (1994,p. 47) noted that, “for any skewed data,quantiles are a disaster for a presentationmap!” In the above example, quantilesresult in such a large data range in thehighest class as to be almost meaningless.Standard deviation classes may be helpfulin some situations where the distributionis not extremely skewed.

Note that a frequency curve shows skew-ness in the rank-ordered data values, but

54 Chapter 2

Best Choice?

Generally, natural breaks or equal intervalswill be the best methods for creating area-type maps in crime analysis.

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Figure 2.15

East Baltimore City homicide rate in choropleth maps using different methods.


Figure 2.16

Histograms associated with maps in figure 2.15 with class interval breaks (vertical yellowlines) and normal distributions superimposed.



only a map can show skewness in geo-graphic space. Are the high values distrib-uted geographically in a random way orclustered? Either method yields usefulinformation. If the high crime rates areclustered, it may indicate a hot spot. If thehigh rates are random, the net impact onthe community may be about the same,but we are now unable to point to a hotspot.

We can see an empirical relationshipbetween map scale and skewness, whichis minimized in a small area (large scale)and maximized in a large area (smallscale). Think of it this way: A very smallarea in the community, say 1 square yard,can have no spatial skewness becauseonly one event can happen there. But asthe spatial scope increases (smaller scalemaps covering larger areas), the potentialfor skewness increases because there canbe bimodal, or split, distributions in space(as well as time). A clump of events canoccur in one small area with the restempty—an extremely skewed pattern.

This is what the crime scene is like on aregional, national, or global scale. Clusterscorrespond to opportunities presented bythe underlying controlling condition, pop-ulation distribution. At the smallest scale(region or world), the crime map is for allpractical purposes the same as the popula-tion map, but at larger scales (city orneighborhood) we refine the view and seethat the presence of people actually meansvariations in rates conforming to variedsocial and physical environmental condi-tions. Also, at larger scales we will seedifferent patterns depending on thedenominators used to calculate crimerates.

Another way to visualize a distribution isthe use of a box plot, which shows howdata are spread in relationship to themean, median, mode, and quartiles, withoutliers symbolized in a special way.(Outliers are values more than 1.5 boxlengths from either the 25th or 75th per-centiles.) If we examine the HOMRATEdata set using the box plot routine in theStatistical Package for the Social Sciences(SPSS), the result appears as shown infigure 2.17.5 Note that the box plot is analternative way of visualizing the samedata shown in figures 2.15 and 2.16. Inthe box plot in figure 2.17, the red boxrepresents 50 percent of the data values,with the median shown by the bold lineacross the box. The 75th and 25th per-centiles are the top and bottom of the box,respectively. The ends of the Ts representthe smallest and largest observed valuesthat are not defined as outliers. Althoughthe box plot seems to be repetitive, it pro-vides a different perspective on the data—one that complements the more frequentlyused histogram. (For a detailed explana-tion, see SPSS documentation, such asSPSS for Windows Base System User’sGuide Release 6.0, p. 186.)

Only the immediate objective and theavailable tools limit the amount of explo-ration and preprocessing crime mappersdo. Perhaps the single most importantexploratory step is the creation of a his-togram, box plot, or comparable graphicwith which to visualize the shape of thedata and answer the fundamental ques-tion: Is it severely skewed or in someother way not normal (e.g., bimodal ordouble peaked)? How will this affectmaps, and what type of map will permit apresentation that minimizes distortion and

56 Chapter 2

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accurately portrays the data? Again, thisexamination of the data is the ideal. Notall analysts will have the tools or the timeto go through this step. Nevertheless,these possibilities are outlined here toraise awareness of what constitutes thebest practice.

Map design

The field of map design has generatedsubstantial literature in cartography focus-ing on how people comprehend maps andthe impact of various design elementssuch as symbol size, color, and line thick-ness. Also of interest is the impact of thearrangement of a map within the mapframe as well as the merits and demeritsof various types of maps.

Debate continues, for example, over thedominance of choropleth maps to repre-sent numbers. Opponents point to themost obvious choropleth defect: its use ofone data value to represent an entire area,an absurdity that becomes acute when

most of a geographic subdivision containsvirtually no human activity because of theterrain or the existence of bodies of water.6

Muehrcke (1996) quoted Ronald Abler,Executive Director of the Association ofAmerican Geographers, who, in 1987,said something to the effect that choro-pleth maps were an abomination that GISwould soon eliminate through the use ofdasymetric mapping.7 (The death of thechoropleth map has been slow!) However,the increased use of density surfaces invarious desktop GIS programs is a moveaway from such heavy reliance on eitherdot or choropleth modes of representationand is consistent with the concept of arealaveraging—without slavish adherence topolitical or administrative boundaries.

Map design is at once a technical andan artistic effort. Dent (1990) devotes5 chapters and about 120 text pages todesigning thematic maps; we can giveonly broad consideration to a few issuesrelating to typical parts of a map and howthey should be organized. The reader is

Figure 2.17

A box plot of homicide rate data.Compare with his-tograms (see figure2.16).



referred to the following textbooks for afull explanation, particularly on suchdetails as lettering and labeling: Campbell,1993; Dent, 1990, 1993; Robinson et al.,1995; and MacEachren, 1994, 1995.

It may be helpful for newcomers to map-making to make a flow chart in which thedesign of the map is adjusted to ensurethat the map fulfills its stated purpose.This activity may become less important

58 Chapter 2

How Much Exploration?

A histogram of the numerical data, as well as statistics for skewness andkurtosis will help determine what kind of map would be most effectiveand least misleading. Common statistical packages such as SAS® andSPSS® enable the rapid production of these diagnostics. Also, MicrosoftExcel and other spreadsheets yield the following statistics as well as a histogram option. (In Excel, see the Tools menu, then Data Analysis.)

Interpretation: Values of the skewness and kurtosis are centered at zero. If either is relatively large, a nonnormal distribution is likely. See Norcliffe (1977) for more detail on this, and consult figures 2.15 and 2.16 forgraphic interpretations.

*HOMRATE is the homicide rate per 100 persons in a selected part of Baltimore,

Maryland. These statistics were used to compile figures 2.15, 2.16, and 2.17.


HOMRATE*Mean 0.74Standard Error 0.06Median 0.67Mode N/AStandard Deviation 0.47Sample Variance 0.22Kurtosis 1.44Skewness 1.10Range 2.20Minimum 0.12Maximum 2.32Sum 42.43Count (number of block groups) 57.00Largest (1 case) 2.32Smallest (1 case) 0.12Confidence Level (95.0%) 0.12

over time as intuition and experience takeover from reliance on a formally struc-tured process. Dent (1990, p. 316) liststhe following elements of the thematicmap, which could serve as a checklist forinclusion:

■ Title (or caption).

■ Legend.

■ Scale.

■ Credits.

■ Geographic content (showing informa-tion that may not necessarily be includ-ed in the subject matter of the map,such as orientation or north arrow).

■ Graticule (spherical coordinate system:latitude/longitude, State plane).

■ Borders and neatlines (the lines thatbound the body of a map, usually parallels and meridians).

■ Symbols.

■ Labels.

Most of these elements are necessary in atypical crime map. The principal exceptionis the graticule (spherical coordinate grid),which normally serves no useful purpose.8

Also, credits are rarely used because dataare likely to be locally derived. However,if data sources are not self-explanatory,credits clarify exactly where the data camefrom. This information could be listedunder the body of the map using the key-words “source” or “data source.” We couldadd author to the list to assist in the

process of accountability, inconspicuouslynoting the name or initials of the analyst-cartographer in a corner of the map.

A useful approach to learning more aboutdesign is to look at examples of crime(and other) maps that have been deemedacceptable by their respective audiences.Fortunately, there is no shortage of maps,whether on crime or on other phenomena.The easiest access is via the World WideWeb, and the appendix of this guide listssome useful Web site addresses. Chapter 3uses examples to discuss various applica-tions of crime mapping. Here we confineourselves to outlining principles.

Dent (1990, chapter 13) has noted severalelements of map composition: balance,focus of attention, and internal organization.

■ Balance refers to the need to arrangeparts of the map in a way that enhancesits visual symmetry. However, thecrime cartographer may have littleflexibility with respect to balanceowing to inherent content limitations.For example, the jurisdiction may beextremely asymmetrical, making it difficult, if not impossible, to mapwithout leaving considerable whitespace on the paper. Cities with long“shoestring” annexations, like LosAngeles, or States with long panhan-dles, like Oklahoma, are good exam-ples of difficult map shapes. Thisproblem sometimes can be solved bychopping the city or other area ofinterest into its component parts. Aninset, or miniature map of the whole,is used to show how the pieces fit backtogether. Another solution is to routine-ly map individual precincts or districts

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under the assumption that the man-agers of those areas are first and fore-most interested in seeing patterns intheir areas of responsibility. The draw-back to this is that crime patterns donot pay much attention to administra-tive or political boundaries, so thatlooking at individual subdivisions inisolation from the rest of the area maycause someone to miss hot spots orother useful patterns by fragmentingthem.

■ Focus of attention is a concept basedon the assumption that people readmaps like they read the printed page,by moving their attention from upperleft to lower right.9 Hence the opticalcenter of a map is somewhat above thegeometric center suggesting that, ideal-ly, the most significant informationshould be closer to the optical center.Again, this is easier to manage in the-ory than in practice. Still, it is a usefulconcept to bear in mind because crimeanalysts will sometimes have enoughdiscretion in design that the focusof attention can be exploited to advantage.

■ Internal organization refers to thealignment of the parts of a map orindividual maps on a page or withina frame. Map elements should bearranged in a logical way rather thanplaced haphazardly on the page. Thecore contents of the map, for example,should dominate the space, and otherelements should be secondary.

According to Dent, contrast also is impor-tant to visual perception. Line, texture,

value, detail, and color are powerful toolsbecause they allow map elements to bedifferentiated from one another. More con-trast makes objects stand out, less allowsthem to fade into the background. Linethickness, or weight, can assist in thisprocess, and using more than one lineweight on the map can add interest.Texture can add variety and draw atten-tion to an important part of the map.Value refers to the use of lighter or darkershades of color, and detail draws the eyein. As noted elsewhere, however, detail isa two-sided coin. It adds interest, butwhen used to excess it can cause clutterand make the map illegible. If a map is to be reduced for publication, fine detailmay be completely lost in the reductionprocess. Experiment with enlarging andreducing on a photocopier to learn moreabout how this works in practice.

Color is extremely important in theprocess of area differentiation. It is also acomplex issue owing to the physiological,psychological, and physical processesinvolved. Dent (1990, chapter 16) notesthat color has three dimensions: hue,value, and chroma.

■ Hue is the term given to color labels—red, yellow, and blue, the primarycolors—and the millions of permuta-tions derived from them.

■ Value refers to the degree of lightnessor darkness of a color. GIS programscan help you select color values byproviding color “ramps” (or series ofrelated shades or values of a hue) in avisually logical sequence ranging alongan intensity spectrum. Colors vary

60 Chapter 2

along a continuum from light to dark.For example, reds may range fromlight pink to deep red, and blues mayrange from sky blue to navy blue.

■ Chroma is understood through theconcept of color saturation. A less sat-urated color appears to contain moregrays, and a saturated color has nogray and appears as the “pure” color.In photography, some films have areputation for conveying more satura-tion than appears in natural scenes(bluer blues, greener greens), “largerthan life” color that is pleasing to someviewers but excessive to others.

Choices of color in maps need to be madequite carefully because color may havestrong emotional connotations for somereaders. For example, should red be usedfor a map of violent crime, given the sym-bolic connection to blood? It is tempting tooverload crime maps with warm colors,such as red and orange, but the analystshould be mindful of the symbolic effectand the impact this may have on theintended audience.

Just as color makes maps and othergraphics come alive, color also enhancesour ability to mislead people with mapsthrough the use of inappropriate hues andvalues. For example, a crime category thatis a local political “hot potato” could bevisually minimized through the use of

cool colors in subtle shades lacking satu-ration. The use of color in maps andgraphics is complicated by the fact that asignificant portion (8 percent of males and0.5 percent of females) of the populationis at least partially colorblind.

Crime mappers can take advantage of var-ious models of color sequencing. GIS soft-ware typically defaults to a part-spectralplan with shades from yellow to brown.In a full-spectral plan, colors range fromwarm to cool, and in a double-ended plan,data values representing an increase (orabove average) are in one color and adecrease (or below average) in another.Increases (or higher values) are typicallyshown in warmer colors, decreases (orlower values) in cooler (see Dent, 1990,p. 387). GIS software normally permitsthe customizing of colors to fit your purpose.

Design, abstraction,and legibility

Map design and abstraction are insepara-ble. The map design defines the level ofabstraction to be imposed. “To representis to abstract,” wrote Muehrcke, and“abstraction frees us from the tyranny ofour physical existence” (1996, p. 275).He presumably meant that it gives uslicense to, so to speak, “mess with reality.”Many of the issues that concern cartogra-phers, such as the degree of distortion onworld maps, are of little concern to crime

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Using Colors and Shades

■ Use darker colors or gray shades for more or higher values.

■ Use lighter colors for less or lower values.


mappers. Where should we focus ourattention when it comes to thinking aboutabstraction in our maps? What can weafford to ignore? Are any map elementsindispensable on most crime maps?

Abstraction, the reduction of detail onmaps, permits us to design our maps inways that make them attractive and effec-tive. Abstraction is like the sculptor’s chisel—it determines what remains of theraw material and what form the finishedproduct will take. As noted earlier, mostmap elements are dispensable at one timeor another, depending on the context.

First and foremost, the analyst must con-sider the audience and the medium ofpresentation. Will the map go to one per-son and be seen at arm’s length? Will itbe a page in a report? (If so, will it be incolor? How would the map look if it wasconverted to gray scale?) Will it become atransparency for an overhead projector ora 35-mm slide? Will it be incorporatedinto a digital projector production inMicrosoft PowerPoint® or comparable software?

If a map is to be projected, lettering sizeand line weight become quite critical. Youmay have a brilliant map with potentiallygreat visual impact, but if two-thirds ofthe audience can’t read the lettering whenit is projected, your creativity is wasted.10

Also consider the “demographics” ofthe audience to be addressed: are theyyounger? older? more educated? less educated? predominantly female?

If the audience is not similar to the gener-al population, some adjustments in map

design may be needed. Research hasshown, for example, that there may besubtle differences in the way men andwomen read maps (Kumler andButtenfield, 1996).

This begs the corollary question of exaggeration in maps to gain legibility.Sometimes detail must be retained, but this may result in objects running togetherowing to the thickness of the lines repre-senting them. Line thickness may need tobe adjusted and objects may need to bemoved slightly to maintain visual separa-tion. Bear in mind that line work on mapsoften greatly exaggerates the true dimen-sions of linear features. A typical Statehighway map may be used as an example.On this map, interstate highways are1/16th of an inch wide. The representativefraction (RF) of the map is 1:380,160, or6 miles to the inch, which represents awidth of 660 yards. This is probably, onaverage, double the width of most inter-state highways. By comparison, areafeatures such as a city block, a city, or acounty, should be accurately renderedbecause exaggeration is not needed tomake them visible.

Even point data generally exaggerate thesize of the location at which a crime inci-dent occurred or the address of the victimor offender. Point symbols are actuallymarkers for general locations and shouldbe interpreted as approximations owing to(a) the size of the point symbol and (b)normal problems with address interpolationtouched on in chapter 4 of this guide. It istempting to see point data as the epitomeof accuracy, but this accuracy is relative.

62 Chapter 2

Crime mappers might consider using moreperspective symbol landmarks or mimet-ics11 to help readers orient themselves,particularly in metropolitan areas (see fig-ures 1.2, 3.25, and 5.9). These symbolsare pictorial and characterize the landmarksthey represent, such as the use of an air-plane symbol to represent an airport or silhouettes of familiar structures, such as a school, church, ballfield, cathedral, cityhall, or highrise tower. Such pictorialdevices are more important for lay audi-ences than for police officers, who arefamiliar with the area, although theassumption that all cops are equally famil-iar with entire cities or metro areas is afallacy. We—even cops—are victims ofour daily routines and the neighborhoodsthey take us through. None of us cancomprehend entire metropolises, at leastnot at the level of street name familiarity.

Summary


■ What crime maps should do.

■ How crime maps do what they do.

■ How to choose the right kind of crimemap.

■ Types of thematic maps.

■ How to choose class intervals innumerical data.

■ Why data should be explored.

■ What is involved in crime map design.

■ How crime map design, abstraction,and legibility are related.

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Map Design Questions to Consider

■ Is this the best map for the stated objective?

■ Is the scale appropriate?

■ Does the design account for both data representation and aesthetics?

■ Could a flowchart ensure the inclusion of all necessary elements?

■ Are the sources of data, authors of the map, and date of preparation shown?

■ Are balance, focus of attention, and internal organizationconsidered?

■ What colors work best?

■ Is the map legible in all the contexts in which it will be used (print, slide, fax, PowerPoint®, and overhead transparency)?


Notes

1. Circle radii are a function of the squareroot of data values. A slightly larger value(“Flannery’s Constant”) is used in someprograms to take into account visualunderestimation of areas (Campbell, 1993, p. 272). This map was customdesigned and executed by cartographystudents under the supervision of ThomasRabenhorst, Department of Geographyand Environmental Systems, Universityof Maryland, Baltimore County.

2. This is sometimes referred to as a“stepped” surface map as compared witha “smoothed” surface. The former is basedon choropleth area subdivisions; the latteris not.

3. For an interesting early example of isolines applied to crime mapping, seeBrassel and Utano (1979).

4. A map of this type, which has probablynever been produced, would demand datafrom a global positioning system.

5. SPSS is described at http://www.spss.com. Another widely used package, theStatistical Analysis System (SAS), isdescribed at http://www.sas.com. See alsothe Web site for S+ at http://www.mathsoft.com/splus/.

6. San Bernardino County, California,about the size of Denmark, is an extremeexample. The urbanized part is small, andmost of the county is part of the sparselyinhabited Mojave Desert. Yet a choroplethmap of population density will show theentire county as having the same density(the average value), giving the visualimpression that the desert is populated.

7. Dasymetric mapping ameliorates theareal averaging of the choropleth map byrecognizing the internal variation inherentin the subdivisions. The result resemblesan isoline map with political or adminis-trative subdivisions superimposed. (Foradditional information, see Campbell1993, p. 218.) For an illustration of whatTufte calls the “mesh” map alternative to

64 Chapter 2


How maps can be designed to address specific issues and audiences,such as:

■ Patrol officers.

■ Investigators.

■ Managers.

How maps can be used:

■ To support community oriented policing and problem oriented policing.

■ In courts and corrections.

■ When communicating with policymakers.

the choropleth map, see Tufte, 1990,pp. 40–41.

8. Some police departments rely on a proprietary map system for navigation;embedding that coordinate system in theircurrent mapping applications could behelpful. In Baltimore County, for example,locational references are to the grid ofmaps published by ADC of Alexandria, Inc.

9. This assumption is culturally condi-tioned by the way the person reads,

which is itself language dependent.(Arabic is not read in the same way asEnglish, for example.)

10. Take into account the size of the roomand how far the back row of seats is fromthe image. Similar considerations apply ifyou have a poster-size map to be dis-played to an audience.

11. Mimetic symbols mimic the appear-ance of the real object. (See MacEachren,1994, pp. 56–57; 1995, pp. 258–259.)

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Chapter 3:Maps That Speak to the Issues

This chapter discusses how map designs can addressspecific issues and audiences. The underlying assump-tion that “one map fits all” is inadequate because eachaudience has its own perspective on crime and how it can be prevented or controlled. Community leadersmay have the latest notorious incident on their minds.Policymakers may be concerned about how to trim $1 million–$2 million from their budgets while makingthe community safer. Members of the court and correc-tions communities may be concerned with overloadedsystems, overcrowding, and the ramifications of releas-ing offenders early. Investigators may need tools tohelp them organize place-related facts and processes.Police managers often worry about accountability,resource allocation, displacement problems, and theimplications of demographic change. On the front lineswhere patrol and community officers operate, commu-nity information is a core resource rarely available in sufficient quantity or quality.

Patrol officers

Officers who spend time on the street are entitled tothe most up-to-date and comprehensive data related totheir patrol areas. These data should be easily accessi-ble and user friendly.

Maps That Speak to the Issues

The most useful kind of informationshould focus on recent area history, withan emphasis on change. Effective policingemphasizes patterns, and mapping andunderstanding change are key to under-standing these patterns. The most basicinformation shows what happened andwhere. For example, what has happened

during the past two shifts? Are new hotspots emerging? Have significant develop-ments occurred in outstanding cases? Is itnecessary to communicate with specificneighborhood watch or citizen patrol rep-resentatives? Examples from Jacksonville,Florida, and San Diego, California, areshown in figures 3.1 and 3.2.

68 Chapter 3

Figure 3.1

An intranet mapshowing calls forservice classified

by crime types,Jacksonville,

Florida.

Source:Jacksonville,

Florida, PoliceDepartment.

Reproduced bypermission.

Figure 3.2

A map showingauto thefts, PacificBeach area of SanDiego, California.

Source: CrimeAnalysis Unit of the

San Diego,California, PoliceDepartment and

SanGIS. Reproducedby permission.

Quick mapping systems that supportpatrol functions have been developed byseveral police departments. These includeChicago’s Information Collection forAutomated Mapping (ICAM) program,which defaults to a map of reportedoffenses (based on the user’s selection of a crime type) during the previous 10days in the district. Figure 3.3 shows anexample.

What are the basic elements of usefulmaps for patrol officers? Primarily, mapsmust be detailed and geographically legi-ble. Generalization is acceptable on largecity or county maps, but maps of individ-ual beats or posts can include details suchas street names, landmarks, and locationsof significant events relevant to an offi-cer’s tasks. The ICAM model also incorpo-rates some tabular data to give the mapadded depth. For example, a map of crimi-nal damage to vehicles could display atable with the date, beat, time, address,

make and model of the vehicle, and other details for each incident. A map ofassaults could have embedded within it atable with similar detail plus data on thecharacteristics of the victim and suspect,weapon used, and number of assailants.Maps should be clear and unambiguous.Contrast should be strong because mapsmay be read under low light conditionsand may need to be understood quickly.

Maps are reference documents, so mapusers do not memorize map detail anymore than they would memorize the content of an encyclopedia. A well-usedmap will be referred to frequently, and this requires that the map be exceptionallylegible. Crime analysts may considerestablishing basic criteria for line weights,font and symbol styles and sizes, color,and shading. This should ensure that themaps convey their meanings clearly underall conditions. Maps lacking contrast orcontaining small lettering, symbols, or

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Chapter 3

Figure 3.3

A map showing residentialburglary offenses inChicago’s Sector 2.

Source: Rich, 1996, p. 13.


poorly defined lines will be relatively hardto read (figure 3.4).

Investigators

The documented applications of mappingas a support tool for investigation suggestseveral generalizations applicable to theuse of maps.

Maps:

■ Bring together diverse pieces of infor-mation in a coherent way.

■ Provide vivid visualizations of case-related data and descriptive patternsthat may suggest answers to investiga-tive questions.

■ Provide opportunities for spatial analy-sis with selection and query tools. (See chapter 4.)

■ Serve as tools to persuade managers todeploy resources in a specific manner.

A recurring theme is that maps oftenreveal a whole picture that is greater thanthe sum of its parts. This happens whenmany small and seemingly isolated andinsignificant pieces of evidence take oncritical importance when viewed as part of a pattern.

Without maps, data may be incomprehen-sible or available only in the form of a list.A list of suspects or pieces of physical evi-dence means little if key information is

70 Chapter 3

Figure 3.4

Contrasting maps, lesslegible (top) and more

legible (bottom).


seen best in graphic form. Even a list ofaddresses may be hopelessly confusing ina metropolitan area with thousands ofstreets.

These points are illustrated in several casestudies outlined by La Vigne and Wartell(1998). In a McLean County, Illinois, caseinvolving rural burglaries, for example, aninexpensive proprietary mapping programwas used to plot incidents on a countymap. Almost all incidents occurred closeto major highways, suggesting theinvolvement of traveling criminal groupsthat specialized in burglaries. The burgla-ries also seemed to occur close to cemeter-ies, which were thought to be lookoutplaces. Based on this information, morepatrols were placed around cemeteries,leading to the arrest of persons belongingto a group called the Irish Travelers.Although there were no convictions, therewas a sharp decline in burglaries in theregion, suggesting that the group hadbeen displaced (Wood, 1998).

In a Knoxville, Tennessee, rape case, ex-offenders living near a crime scenewere mapped. Within 2 miles of the crimescene, there were 5 sex offenders, 15parolees, and 2 juvenile habitual offend-ers. When additional offenses occurred,victims were able to identify the offenderfrom the suspect group selected accordingto geographic proximity. The interpreta-tion noted that “without the spatial analy-sis of the offender databases layered ontop of the crime scene map, the offenderinformation would not have been readilyknown” (Hubbs, 1998).

In Aurora, Colorado, the time and day of occurrence, location type, method of

break-in, and property characteristics ofseveral burglaries were used to determinethat the burglaries were geographicallyclustered. The events had taken place inthe afternoon in single-family dwellings,and similar entry points and crime scenebehaviors (including damaging property inthe home) were evident. This information was distributed to the police department’sburglary unit and patrol bureau, whichimmediately identified and eventuallyapprehended a suspect whose homeaddress was at the center of the observedcluster (Brown et al., 1998).

Other relevant case studies involvedpreparing murder trial evidence in St.Petersburg, Florida (see chapter 5);employing an autodialing system inBaltimore County, Maryland (see chapter5); predicting serial crime in Los Angeles,California; and mapping evidence left by a murderer in Lowell, Massachusetts (seeLa Vigne and Wartell, 1998, for details ofthese and other cases).

Police managers

Police managers are confronted withmany challenges. Not only must they beaware of crime problems, but they alsomust be able to address problems involv-ing labor relations, public relations, andpolitical influences. The following are typical issues affecting police managers,which can be addressed by using mappingas a management tool. The five issues are analyzing calls for service (CFS), hotspot mapping, crime displacement, theimplications of demographic change, and accountability as exemplified by the CompStat process in New York.

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Management issue 1: Mapping calls for serviceto assist resource allocation

CFS are generally accepted as a crudeindex of the demand for police services.As such, they can be mapped. Thisprocess uses either point symbols (eachsymbol represents one or more calls) orchoropleth form (calls are assigned to specific geographic or reporting areas).Although patrol officers find CFS mapshelp indicate “where the action is,” mapswith greater detail are more useful.

CFS maps are most useful as a tool to helpmanagers allocate resources. In BaltimoreCounty, Maryland, for example, descriptive

data on the number of calls that variouspatrol units respond to are mapped (figure3.5). This same police department thenanalyzes and “weights” the calls for serv-ice to designate or modify police “posts”or patrol areas. “Weight” in this contextmeans that CFS involving more seriouscrimes are assigned higher values thanCFS regarding less serious incidents. Theweighted values are then totaled to indi-cate each post’s workload. As needed,post boundaries are drawn (or redrawn)to equalize the workload (figure 3.6). Thisis done using a MapInfo® redistricting rou-tine, which was originally intended tohelp redraw political districts followingeach census. The objective of political

72 Chapter 3

Figure 3.5

A map showingdescriptive data onpatrol responses to

calls for service.

Source: Philip Canter,Baltimore County,Maryland, Police


permission.

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Chapter 3

Figure 3.6

A map showing policeposts based on work-load assignments byreporting area.

Source: Canter, 1997,figure 4, p. 173.

Figure 3.7

A map showing the daywith the highest average

reported crime density,Bronx County, New York,

October 1, 1995, toOctober 31, 1996.

Sources: Map byJ. LeBeau, Southern

Illinois University, withdata from the New YorkCity Police Department.


redistricting is to maintain an equal num-ber of residents in each political area tomaintain the “one person, one vote” prin-ciple—a process analogous to equalizingincidents or workload per officer in the

police context (Canter, 1997). (See chapter5 for another application of this method.)

Another example with implications forresource allocation is shown in figure 3.7,


74 Chapter 3

which illustrates the day of the highestaverage reported crime density. In thiscase, point data were aggregated intomore than 950 census block groups, andthe number of crimes per day, per squaremile for each block group, was calculated.

Management issue 2: Anapproach to hot spot mapping

Hot spots are discussed in more detail inchapter 4. Here, we describe an applica-tion designed to identify specific locationsfor increased law enforcement activity andto help law enforcement managers solveproblems. This method, developed by Eck,Gersh, and Taylor (in press), is called repeataddress mapping (RAM). It defines a hot

spot as “a single place with many crimes.”The term “many crimes” is defined usingminimum plotting density—meaning simply that a minimum number of eventsmust occur in a specific place for it toqualify as a hot spot.

The hot spot designation procedure isillustrated in figure 3.8. The steps include:

■ Sort places according to the number ofcrimes so that the place with the mostcrimes is at the bottom of the list andthe place with the fewest is at the top.

■ Divide the list into 10 equal sections,with the top group containing thefewest crimes.

Figure 3.8

A map showing the RAMmethod applied to data forEast Baltimore, Maryland.Note the distinctive differ-ence between the patternfor “all event mapping”and the pattern for “repeataddress mapping.”

Source: Eck, Gersh, and Taylor, in press.Reproduced by permission.

■ Designate addresses within the bottomgroup as hot spots.

Management issue 3: Mapping displacement

In general terms, displacement occurswhen criminal behavior is replaced bysome other behavior or moved from oneplace to another. Spatial displacementoccurs when offenders move from onearea to another in response to a lawenforcement effort. A fundamental prob-lem in the analysis of displacement is thenatural and inherent conflict between thelocal and general benefits of crime preven-tion. For example, a crime hot spot isidentified and a prevention program is put in place. This has the effect of chasingoffenders out of the area, only to movethem across the city, county, or State line.Thus the local problem has been solved(at least temporarily), but the net generalgain is zero—and could be negative if theoffenders are displaced to an area that ismore vulnerable because of weaker lawenforcement. Taking this line of reasoningto its logical conclusion, it could be saidthat preventing crime in one place causescrime in another!

Barnes (1995) noted that the literature hasidentified six kinds of crime displacement:

■ Temporal. Offenders perpetrate crimesat times seen as less risky.

■ Target. Difficult targets are given up infavor of those easier to hit.

■ Spatial. Offenders move from areasthat may be targets of crime preven-tion programs to less protected areas.

■ Tactical. Tactics are changed to getaround security measures.

■ Perpetrator. New offenders take theplace of those who move, quit, or areapprehended.

■ Type of crime. Offenders take upanother type of crime if one typebecomes too difficult to commit.

In theory, if perfect data were available,all six of these displacement types couldbe mapped. In practice, this is highlyunlikely, and from the perspective of spatial analysis, spatial displacement isthe prime candidate for mapping.

But mapping even spatial displacementalone is difficult due to measurementproblems. Although it may seem simpleenough to establish a law enforcementeffort target area and a surrounding dis-placement area, measuring displacementthere is, in reality, far more complex. Theability to measure displacement is alsoaffected by the impact of the enforcementprogram, as well as the size of both thetarget area and the displacement zone andtheir existing crime levels.

For example, if the displacement areaalready has a high level of crime, the effectsof the enforcement program could be indis-tinguishable from the normal variations incriminal incidents in the displacement zone.As shown in figure 3.9, the existing crimerate is a key factor in isolating crime dis-placement. Displacement would be difficultto identify in the top map. However, in anarea with little existing crime, displacementmay be obvious—or at least appear to beobvious (bottom map).

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To suggest that spatial displacementanalysis is too difficult to be viable is anexaggeration; managers and analystsshould assess viability on a case-by-casebasis. However, spatial displacementanalysis is harder than one might think,and even a well-designed displacementstudy may need to hedge its conclusions(see Weisburd and Green, 1995; Reppetto,1974; Hakim and Rengert, 1981; andBarr and Pease, 1990).

Management issue 4:Demographic change and its implications for managers

A key fact affecting police departmentmapping is that some 26.3 million immi-grants, about three times as many as in1970, now live in the United States(Escobar, 1999). This 10 percent of thepopulation is critically important to lawenforcement, whose primary functionrequires interaction with the public.Although many communities may feel

76 Chapter 3

Figure 3.9

Maps illustratingdisplacement.

The dense pat-tern in the topmap makes it

difficult to detectdisplacement.

There is no suchproblem in the

bottom map.

Source: KeithHarries.

little effect (particularly those that aremore isolated and rural), major metropoli-tan areas with many immigrants haveexperienced profound social change.

It is in such places that community polic-ing takes on special significance. Not onlyare “traditional” minorities (primarilyAfrican-Americans and Hispanics) voicingtheir opinions, but relatively new groups,such as Bosnians, Dominicans, Russians,Koreans, and Vietnamese, are becomingmore visible and politically articulate.Matters are further complicated by the factthat a specific national group, such as theVietnamese, may not necessarily consti-tute an ethnic group. Vietnam, for exam-ple, has 53 ethnic groups, leading topotential stresses along ethnic lines.

Each group’s perceptions of what is legalor illegal may differ sharply from localnorms. Opportunities for misunderstand-ings with law enforcement are rife, andcommunity policing officers must under-stand the cultural values and practices ofthe groups they encounter. Language barriers mean:

■ Citizen reports may be difficult orimpossible to understand for officerswho are unfamiliar with a language.

■ Confusion and frustration over misun-derstood reports may result in under-reporting of crime because personswho assume they will not be under-stood may stop reporting crimes.

■ It is unlikely that first-generationimmigrants will become police officers.

What does this have to do with crimemapping? Because police agencies need toknow what is going on demographicallyin their communities to react appropriate-ly, mapping demographics and related fac-tors may translate into better communityrelations. The Washington Post publishedthe article “When Fighting Crime Isn’tEnough: Fairfax, Montgomery [Counties]Seek Police Chiefs Adept at Community,Employee Relations” (Shear and Shaver,1999). Surprisingly, perhaps, what wasmissing from the discussion about recruit-ing new chiefs was the crime issue. Thedebate was peppered with phrases such as“effective management,” “comfortablewith changing demographics,” and “diver-sity.” Traffic generated more interest thanburglary!

A basic need is mapping where the minor-ity and immigrant groups are located. Arethey scattered throughout the communityor clustered in distinct areas? What aretheir institutions and facilities (e.g.,churches and temples, community centers,and schools)? What are the issues of con-cern to communities and what are theirlocational attributes? A map such as theone in figure 3.10 shows how census datacan be used to locate ethnic groups anddetermine the overall degree of ethnicdiversity. Based on 1990 census data, thefigure illustrates the relative evenness inthe proportion of five ethnic groups incensus tracts. The index varies between1.0 (equal numbers of all groups, maximumdiversity) and 0.0 (only one group in thetract, minimum diversity). The ethnicdiversity map could be viewed as a modelfor achieving the same ethnic distribution

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Chapter 3


in the police departments serving variouscommunities. A department does not matchthe community if it is less ethnicallydiverse than the area it serves.

Another possibility is to maintain a geo-graphic inventory of minority and immi-grant concerns so that managers can seewhat and where the issues are. Communityleaders could be identified, perhaps bysubareas of the larger ethnic community,to ensure that a contact person exists foreach neighborhood. Mapping can also beused to show where second-generationminorities are reaching adulthood, enablingtargeted police recruiting. The 2000 censuswill permit this type of analysis on theWorld Wide Web.

A major problem for demographic map-ping is the lack of current population data.Until data from the 2000 census becomeavailable, alternative data sources are nec-essary. Analysts may have to improvise.

Field mapping may need to be done andcommunities delineated on the basis ofvisual checks, perhaps with the aid of aglobal positioning system (GPS). Otherpossible data sources include those onschool enrollments, utility hookups, andbuilding permits and inspections. Althoughnot yet fully operational, another promisingresource is the American Community Survey(visit Web site http://www.census.gov).

Management issue 5:Accountability and New York’sComStat process1

New York City’s Computerized Statistics(ComStat) process was initiated in 1994in the form of crime control strategy meetings. As a result of sharp declines inthe city’s crime, the system is now widelyimitated. According to the police depart-ment, ComStat’s objective is “to increasethe flow of information between theagency’s executives and the commanders

78 Chapter 3

Figure 3.10

A map showingethnic diversity

in 1990, Los Angeles,

California.

Source: Allenand Turner,

1997.Reproduced by

permission.

of operational units, with particularemphasis on the flow of crime andquality-of-life enforcement information.”Crime strategy meetings, held from 7 to10a.m. twice a week, are part of an “interac-tive management strategy” intended toimprove accountability “while providinglocal commanders with considerable dis-cretion and the resources necessary toproperly manage their commands.”Precinct commanders present at the meetings twice a month.

The process format requires that precinctcommanders appear before the ComStatmeeting prepared to discuss crime andpolicing in their areas. A big-screen computer map shows the precinct underreview. For example, a string of robberieswith similar circumstances might lead toquestions about known habits of robberyparolees living in the vicinity. As this con-versation develops, a map showing rele-vant parolee addresses illustrates thediscussion.

The crime reduction principles embodiedin the ComStat process are:

■ Accurate and timely intelligence.Information describing how and wherecrimes are committed, as well as whocriminals are, must be available at alllevels of policing.

■ Effective tactics. Tactics are designedto respond directly to facts discoveredduring the intelligence gatheringprocess. Tactics must be “comprehen-sive, flexible, and adaptable to theshifting crime trends we identify andmonitor.”

■ Rapid deployment of personnel andresources. Some problems may involveonly patrol personnel, but “the mosteffective plans require that personnelfrom several units and enforcementfunctions work together as a team.”

■ Relentless followup and assessment.To ensure that appropriate outcomesoccur, rigorous followup is necessary.

Underpinning ComStat crime reductionefforts are eight explicit police strategies:

■ Getting guns off the streets.

■ Curbing youth violence in schools andon the streets.

■ Driving drug dealers out of the city.

■ Breaking the cycle of domestic violence.

■ Reclaiming public spaces.

■ Reducing automobile-related crime.

■ Rooting out corruption and buildingorganizational integrity in the NewYork City Police Department.

■ Reclaiming the streets of New York.

How does crime mapping fit in? The policedepartment explains it like this:

Among the command and control center’shigh-tech capabilities are computerized pinmapping and the capacity to displaycrime, arrest, and quality-of-life data inmany formats, including comparative

79


Chapter 3


charts, graphs, and tables. By usingMapInfo software and other computertechnology, the ComStat database can beused to create a precinct map depictingalmost any combination of crime and/orarrest locations, crime hot spots, and otherrelevant information. These visual presen-tations are a highly effective complementto the ComStat report, since they permitprecinct commanders and executive staffmembers to instantly identify and exploretrends, patterns, and possible solutions forcrime and quality-of-life problems.

A problem often overlooked in other policedepartments—crime patterns that overlapprecincts—is addressed through an expec-tation that precinct commanders cooperatewith other commanders to address issuesof mutual concern. Typical ComStat

process maps are shown in figures3.11–3.15.

Maps in support of community orientedpolicing and problemoriented policing

Three broad categories of maps can beused in support of community orientedpolicing (COP) and problem oriented policing (POP)2:

■ Crime and offender information.This includes information about thetimes, locations, and types of offenses,repeated offenses, methods of offend-ers, property taken, points of entry,linking evidence, types of vehicles

80 Chapter 3

Figure 3.11

A CompStat mapshowing locations ofselected burglaries inBrooklyn, New York.

Source: D. Williamson,Center for Applied

Studies of theEnvironment, Hunter

College, New York,and New York CityPolice Department.


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Chapter 3

Figure 3.12

A CompStat mapshowing the densityof selected burglariesin Brooklyn, New York.

Source: D. Williamson,Center for AppliedStudies of theEnvironment, HunterCollege, New York, and New York CityPolice Department.Reproduced by permission.

Figure 3.13

A CompStat mapshowing potential target areas from burglary densities inBrooklyn, New York.

Source: D. Williamson,Center for AppliedStudies of theEnvironment, HunterCollege, New York,and New York CityPolice Department.Reproduced by permission.

Figure 3.14

A CompStat mapshowing counts ofburglaries withinpotential target areasin Brooklyn, New York.

Source: D. Williamson,Center for AppliedStudies of theEnvironment, HunterCollege, New York,and New York CityPolice Department.Reproduced by permission.


used, and suspect information, such aspersonal appearance and case status,which is also an aspect of accountabili-ty (figure 3.16).

■ Community and governmentresources. These include informationabout neighborhood watch groups,storefront stations (figure 3.17),parolees, probationers, tax assessmentand zoning laws, owner occupancy,utility data, patrol beats, building foot-prints (planimetrics), alarm customers,alley lighting, playgrounds, walls asbarriers, afterschool programs, highsocial stress areas such as low-incomehousing, liquor stores, and crime hot spots.

■ Demographics. These include infor-mation about population change, ethnicity, race, socioeconomic status(SES), the percentage of female-headed households with children, theage of housing, and the school-age population.

Extremely broad-based geoarchives arevery useful in COP and POP applications.Because it is impossible to predict whatwill be needed at any given moment, areference-type archive is necessary. Theideal, noted in chapter 4, is an “enterprise”database that crosses departmental linesbut remains accessible to all agencies.

In addition, there is need for versatilityand flexibility in map preparation. Forexample, line maps of streets will proba-bly need to be superimposed over aerialphotos, which demands that their coordi-nate systems match (for more informa-tion, see chapter 4).

Maps and communitypolicing: The city ofRedlands, California,approach

Under the leadership of Police Chief JamesR. Bueermann, the city of Redlands,California, has transformed its currentmeans of addressing neighborhood qualityof life by consolidating housing, recre-

82 Chapter 3

Figure 3.15

A CompStat mapshowing differences in

counts of burglarieswithin potential target

areas in Brooklyn,New York.

Source: D. Williamson,Center for Applied

Studies of theEnvironment, Hunter

College, New York, andNew York City Police


permission.

ation, and senior services into the policedepartment’s Risk and Protective-FocusedPrevention (RPFP) program—a problem-prevention model developed at theUniversity of Washington. RPFP facilitatesunderstanding of the causes and preven-tion of adolescent substance abuse, delin-quency, violence, dropping out of school,and pregnancy. Further:

By integrating the theoretical con-cepts of Risk Focused Policing

with the cutting-edge technologyof Geographic Information Systems(GIS), Redlands has been able tomap community, family, and schooland peer group risk and protectivefactors at the neighborhood level.This enables the police departmentsand the many community-basedorganizations that share access tothis data to effectively focus theirlimited resources on the mostproblematic areas where the greatest

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Chapter 3

Figure 3.16

A case status map,Wilson, NorthCarolina.

Source: Jeff Stithand Wilson, NorthCarolina, PoliceDepartment.Reproduced bypermission.

Figure 3.17

A photograph of astorefront police station, Long ReachVillage Center,Columbia, Maryland.Geographic analysiscan assist in theprocess of selectingan appropriate loca-tion for neighborhoodfacilities.



potential for change exists. [City of Redlands, 1999; see also Hawkins, 1995.]

The key is to mobilize collaborating institutions—schools, government, andcommunity-based organizations—toreduce risk factors and foster “resilientyouth.” The seven maps shown in figures3.18–3.24 provide a sample of the kindsof maps prepared in support of riskfocused policing in Redlands.

Courts and corrections

As in other realms, law enforcement map-ping can apply to any situation involvingthe display or analysis of spatial data.Courts and corrections are unusual in thatmany of their applications may involvelarge-scale representations of the typereferred to as high-resolution geographicinformation systems and described in

chapter 6. This type of display is alsocalled forensic cartography in courtroomsituations. Such large-scale maps are use-ful for illustrating the location of objects in relation to other objects in a building, a room, or a prison setting where incidentsor gang activities have been problems.

Court and law enforcement mapping appli-cations are in their infancy but will devel-op rapidly. A message on the crimemaplistserv in 1999, for example, referred to GIS applications in the WisconsinDepartment of Corrections. The messagenoted that four neighborhood offices hadbeen selected for an enhanced supervisionproject and they routinely exchangedinformation with the Madison PoliceDepartment about where offenders werelocated. Other applications included usingmapping to reduce the overlap of agentsin a three-county region and for a graffitieradication project (Koster, 1999). In

84 Chapter 3

Figure 3.18

A juvenile arrestees’home addresses map

superimposed on a totalaverage risk map.

Source: City of Redlands,California, 1999.


85


Chapter 3

Figure 3.19

A map showing theaverage risk of familydomain superimposedon a map of communityassets providing skills,opportunities, andrecognition.

Source: City ofRedlands, California,1999. Reproduced by permission.

Figure 3.20

A domestic assaultsmap superimposedon a map showingthe family conflictindex.

Source: City ofRedlands, California,1999. Reproduced by permission.

Figure 3.21

A nuisance crimesmap superimposedon an index of community disorganization.

Source: City ofRedlands, California,1999. Reproducedby permission.


86 Chapter 3

Figure 3.22

A map showing thelocation of the top10 calls for service

superimposed ona map of police

beats.

Source: City ofRedlands,

California, 1999.Reproduced by

permission.

Figure 3.24

A map of where batstested positive for

rabies.

Source: City ofRedlands, California,

1999. Reproduced by permission.

Figure 3.23

A comparison ofneighborhood survey traffic

responses with colli-sions and citations.

Source: City ofRedlands, California,

1999. Reproduced by permission.

addition to forensic and intrainstitutionalinvestigative work, GIS has applications inresource allocation, offender tracking andmonitoring, and facility location. Some ofthese are discussed in chapter 5.

Policymakers: The medium and the message

A police department’s in-house maps maytake on a routine character, as the audi-ence learns what to expect in map formatand style. For an external audience, how-ever, presentation needs to be handled differently. For example, how should databe massaged, if at all? Should data be pre-sented as raw frequencies or in the formof a pin map, or should the numbers ofincidents be converted into rates adjustedfor population? Would other rates or ratiosbe more informative?

Ultimately, only the analysts and thosedoing the presenting can answer thesequestions. The analyst should put herselfor himself in the position of the intendedaudience and ask specific questions. Areaudience members likely to understandmaps? Will they understand your maps?How can a point be communicated simplyand directly? Is the map too complicated?Will this map serve its purpose?

These are extremely important considera-tions when addressing policymakers andcommunity groups. Such groups are politi-cal by nature and will likely attempt to useinformation presented to them to their ownadvantage. When information is complex,the room for political maneuvering increas-es, and the underlying “truth” is morelikely to become a casualty. This does notmean that sophisticated mapmaking istaboo. Rather, it suggests that externalaudiences may need to be educated aboutthe types of maps they can expect to seeand the pros and cons of each.

The need for education about map types is only one possible complication. In addi-tion to being confused by different modesof data presentation, external audiencesmay have little or no appreciation for spatial analysis. This dictates simplicity, atleast in the initial stages of using maps topublicly present crime data. Care must betaken to provide a consistent style andformat, which includes the consistent useof scale. A minor problem may be per-ceived as worse than it is if it is presentedin large type or vivid colors (figure 3.25).

As noted, another issue to be consideredis management of the presentation. Howwill the map(s) be presented? Using hardcopy? As a presentation? (If a presenta-tion, will it use computer software, an

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Chapter 3

To Massage or Not to Massage Data?

The risks of audience misunderstanding and confusion increase as youstray from raw data by, for example, calculating rates based on the population or other conditions. Saying more, better, has a tradeoff. Is it worth the price? You be the judge.


overhead projector, slides, or what?) Byfax? In a report? (If a report, will it beprinted in color or monochrome?) As ane-mail attachment? As noted in chapter 2,each presentation mode requires differentdesign considerations, including color andscale. The difference between what worksin a fax presentation and what works inlarge-format, color hardcopy is extreme.The latter can accommodate detail andcolor; the former demands expression inplain, bold, and simple terms.

Community organizations

Similar considerations apply to presenta-tions aimed at community organizations.Again, the design and mode of presenta-tion considerations need to be taken intoaccount. For example, community mapscould be produced for the following audiences and events:

■ Neighborhood watch groups.

88 Chapter 3

Figure 3.25

Two presentations ofthe same data that

may lead to differentperceptions, i.e., a

“lesser” problem (top)or a “greater” problem

(bottom).


■ Neighborhood patrol groups.

■ Public meetings to address specificproblems (reactive).

■ Public presentations to promote good-will between the police department andthe community (proactive).

In each case, the nature of the audienceinfluences map design, content, and use.For example, neighborhood watch andneighborhood patrol maps may be used inthe field. Therefore, their design must besuited to less-than-ideal reading condi-tions. Public presentations should accommodate the audience’s level ofsophistication, which is often a reflectionof their age and education. If complexstreet patterns or topography make themapped area confusing, the analyst mayuse more icons on maps to “pictorialize”the maps. However, great care is needed

to avoid patronizing audiences or givingthe impression that the police departmentis putting something over on them byusing complicated maps. Figure 3.26shows a map of community resources thatcan be used as a basis for the formation ofconstructive alliances.

Conclusion

If geographic information is useful in alaw enforcement context, ways can oftenbe found to present that information in a map. Geographic crime data alone arenot enough to create a meaningful map,as they must be paired with a base mapand other data to make the map interest-ing. Every day, however, the demand for“geographically enabled” data grows asbusinesses, governments, and organiza-tions begin to appreciate the value ofmaps and spatial analyses.

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Chapter 3

Figure 3.26

Community organi-zations in Detroitmapped as part ofan effort to reduceDevil’s Night arson.

Source: Martin,Barnes, and Britt,1998. Reproducedby permission.


Summary


■ How maps can be designed to addressspecific issues and the needs of specialaudiences, such as patrol officers,investigators, and police managers.

■ How maps can be used in support ofCOP and POP.

■ The importance of the medium and the message in communicating withpolicymakers.

■ How maps can be used in courts andcorrections.

90 Chapter 3


■ The role of GIS in crime mapping.

■ How GIS is used in law enforcement agencies.

■ How GIS affects what we can do and how we do it.

■ Vector and raster formats.

■ Geocoding.

■ Filtering data.

■ Measurement using maps.

■ Constructing derivative measures.

■ Hot spots.

■ Buffering.

■ How large databases can be used in mapping and analysis.

■ Data warehousing and data mining.

■ Factors demanding caution in the mapping process.

Notes

1. This section draws extensively on anOffice of Management, Analysis andPlanning (nd) document cited in the references; also see Dussault, 1999.

2. I am indebted to Sgt. Jeff Dean (SanDiego, California, Police Department) and

Dr. Richard Lumb (Director, StrategicPlanning and Analysis, Charlotte-Mecklenburg, North Carolina, PoliceDepartment) for the elements contained in this list. Any misinterpretations are the author’s.

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Chapter 4

Chapter 4:Mapping Crime and GeographicInformation Systems

The GIS revolution

Although it is tempting to think of geographic infor-mation systems (GIS) as a thoroughly contemporarytechnology, its conceptual roots reach far back. A GISis based on drawing different spatial distributions ofdata on paper (or other suitable media) and overlay-ing them on one another to find interrelated points.Foresman (1998) notes evidence that this model wasused at the Angkor Wat temple complex (in today’sCambodia) in the 11th century. Modern geographicinformation systems can be linked to developments inthe 1960s, including land use analysis in the UnitedKingdom by Coppock (1962), development of theCanadian GIS by Tomlinson (1967), and publicationof McHarg’s Design with Nature (1969).

Early GIS efforts were restricted by the limitations ofolder computer systems lacking memory and speed,such as the 512k memory of the IBM 360/65, a computer widely used in the 1960s and 1970s(Tomlinson, 1998). This limited the size of data setsand made it difficult to simultaneously manipulatemultiple observations or large numbers of variables.

These constraints limited the attractiveness of GIStechnology to law enforcement agencies. Weisburdand McEwen (1997) noted that police departmentstypically lacked the computer resources and the base

Mapping Crime and Geographic Information Systems

maps necessary to support a GIS opera-tion. Labor, startup, and operational costs,such as coding incident addresses for thecomputer, were prohibitive, and the fieldlacked user-friendly software.

The earliest applications of crime mappingappeared in the mid-1960s, according toWeisburd and McEwen, who cited the workof Pauly, McEwen, and Finch (1967) andCarnaghi and McEwen (1970). Most earlycrime maps were produced using theSYMAP program developed at HarvardUniversity. Input required punched cards,and output was produced on a line printerthat limited detail to the size of the printertypeface. Shading of choropleth maps wasaccomplished by overstriking printer char-acters so that they darkened (figure 4.1).All maps produced this way were blackand white. Line symbol mapping (of autothefts, for example) was done on a plotter(figure 4.2). (See Weisburd and McEwen,1997, for additional information.)

Later, the SYMVU variant of SYMAP usedline renditions on a plotter to producethree-dimensional visualizations, like thatshown in figure 1.13. Pioneering workdone in St. Louis by the police departmentinvolved the establishment of a ResourceAllocation Research Unit with the objec-tive of improving the efficiency of patroloperations. The unit recognized that fixedboundaries would have to be establishedfor crime mapping purposes. This wasdone using so-called Pauly Areas, namedfor (then) Sgt. Glenn A. Pauly, who desig-nated mapping areas similar in size tocensus block groups. Thomas McEwenthen devised a system for geocoding byrelating street segments to Pauly Areas.This was the first time in an operationalsetting that computerized visualization ofcrime data was recognized as a manage-ment tool.

GIS applications in policing took off in thelate 1980s and early 1990s as desktopcomputing became cheaper and software

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What Is a GIS?

A GIS is a computerized mapping system that permits information layer-ing to produce detailed descriptions of conditions and analyses of relationships among variables.

Strictly speaking, any system that permits the representation and analysisof geographic information is a geographic information system. Theacronym GIS is understood to refer to computer-based software, generallyin the form of a few popular proprietary software packages. Although aprominent component of a GIS, proprietary software does not define aGIS.

Even the acronym “GIS” is the subject of debate, with some arguing thatthe “s” stands for “system(s),” while others object that this is too narrow,and the “s” should stand for “science.”

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Figure 4.1

A “Flat-tone” map of totalindex crime, St. Louis—

Part I—January 1967.

Source: Pauly, McEwen,and Finch, 1967, p. 744.


Figure 4.2

A map showing auto theftsin the Ninth District, St. Louis, January 1967.

Source: Carnaghi andMcEwen, 1970, p. 401.Reproduced by permission.


became more accessible and user friendly.To date, large departments have beenmore likely to adopt the innovation; how-ever, almost any police agency that wantsa GIS can have one. Foresman (1998, fig-ure 1.2, p.11) recognized five ages of GISdevelopment.

The Pioneer Age, which lasted from themid-1950s to the early 1970s, was char-acterized by primitive hardware and soft-ware. The Research and Development Agelasted into the 1980s and overlapped theImplementation and Vendor Age, whichin turn lasted into the 1990s, when theClient Applications Age began. The Localand Global Network Age followed.

Crime mapping came of age in theImplementation and Vendor period, whencomputing costs began to fall and soft-ware became more immediately useful.Over the past decade we have also seenmore examples of police departmentscommissioning customized versions ofsoftware to meet their individual needs.

A survey of police departments conductedin 1997–98 (Mamalian and La Vigne etal., 1999) showed that only 13 percent of2,004 responding departments used com-puter mapping. Slightly more than one-third of large departments (those withmore than 100 officers) did so, but only 3 percent of small units did. On average,departments had used computer mappingfor 3.3 years. Crime analysts were the pri-mary users of mapping, with relativelyfew patrol officers involved. The types ofdata most likely to be mapped were:

■ Arrests and incidents, including UniformCrime Reports Parts I and II crimes.

■ Calls for service.

■ Vehicle recoveries.

The most frequent applications were:

■ Automated pin mapping (point data).

■ Cluster or hot spot analysis.

■ Archiving data.

The GIS perspective

Software

Other information gleaned from the com-puter mapping survey showed that 88percent of respondents used off-the-shelfGIS software, such as MapInfo® (about 50percent), ArcView® (about 40 percent),ArcInfo® (about 20 percent), and others(about 25 percent). Some departmentsused more than one package. Approximately38 percent of departments that used map-ping had done some kind of customizing,and 16 percent were using global posi-tioning system (GPS) technology.

It would be expected that GIS computermapping would follow the classic bellcurve of innovation adoption (figure 4.3).In the lower left are the early adopters,followed by the early majority, the latemajority, and the laggards. A wider bellmeans a longer process, or greater reluc-tance to adopt. GIS adoption will likely bea rapid process because the technology issimultaneously becoming cheaper andmore powerful.

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Vector? Raster? Say what?

You will probably hear the terms vectorand raster in mapping conversations.How much do you need to know aboutthem? Enough to understand the jargonand enough to make informed choicesabout formats.

Raster maps store data in the form of amatrix, or grid. A raster map representsinformation by assigning each pixel, orpicture element, a data value and shadingit accordingly. The size of a raster datamatrix can be determined by multiplyingthe number of rows by the number ofcolumns in the display. For example, if thedisplay settings on the computer read 640by 480 pixels, the matrix has a total of307,200 pixels—each of which wouldhave a data value on a raster-based map.

Vector-based maps are built from digitizedpoints that may be joined to form lines, orvectors, and polygons, or closed shapes.(Digitizing means recording the exact

coordinates of each point along x-y axes.)You will sometimes see the term topologyused in connection with vector format,and this refers to the study of geometricforms. This type of analysis is integral toGIS but is largely transparent to users.

Each format has advantages and draw-backs. For example, lines in raster dis-plays may appear jagged if resolution isnot high enough. Rescanning an image at a finer resolution greatly increases filesize. For example, if the 640- by 480-pixel screen is doubled to 1,280 by 960pixels, the number of pixels increases fourtimes from 307,200 to 1,228,800 pixels.However, raster processing is quicker.

Vector files are good at showing lines butare labor intensive due to the need toclean and edit vector data (Faust, 1998).Applications that use the vector formatinclude emergency personnel routing anddetermining whether a suspect could havetraveled a particular route in a given

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Figure 4.3

Hypothetical bellcurves for GISadoption in policedepartments.



amount of time. Most databases in urbanareas use vector format; examples includestreet centerlines, precinct boundaries, and census geography. Vector files are not very good for managing continuous distributions, such as temperature or landelevation.

Although crime is not continuously dis-tributed (crimes occur at separate points ingeographic space), we can estimate valuesbetween known points to construct a con-tinuous surface representation. The trian-gulated irregular network (TIN) datastructure is one frequently used way ofdoing this. In it, points are connected toform triangles, the attributes of whichbecome the basis for surface construction.

Summing up raster-vector differences,Clarke (1997) cited Bosworth’s analogyabout the work of composers Mozart andBeethoven. Raster is Mozart (dainty littlesteps), and vector is Beethoven (bigjumps from place to place). Another wayof characterizing the difference is to say,“vector systems produce pretty maps,while raster systems are more amenableto geostatistical analysis” (LeBeau, 1995).However, an alternative view argues thatwhether a vector or raster format is mostuseful depends on the type of analysisand what it will be used for. For example,crime densities are often calculated usingthe raster format, even when the pointdata for crime locations are in vector for-mat. The vector data simply will be con-verted to the raster format.

It bears repeating that the analyst shouldknow what format is used, why it is used,and the limitations and possibilities ofeach. A frequently encountered probleminvolves conversion to another format.Conversion from vector to raster is thesimplest. Going from raster to vector,however, means that each line must beconverted on a pixel-by-pixel basis and a vector equivalent produced. This ismuch more time consuming than vector to raster. Users may also convert from onesoftware system to the other (Clarke, 1997).

Because photographic and satellite imagesare raster products, there is no choicebetween formats unless conversion isundertaken. As imagery of both types isused more frequently in crime mapping,we will see mixing of vector and rastertechnologies. For example, a large-scaleaerial photo (raster) might be used as anunderlay for point crime data (vector) andpatrol area line files (vector). In mostcases, the crime analyst will not be awarethat different data formats are being usedbecause the importation and manipulationwill be seamless (figure 4.4). Althoughphotos and satellite images are in rasterformat, they can be used to digitize datainto vector format. Specific features on anaerial photo, such as the footprints ofbuildings or physical barriers betweenneighborhoods, are linear features amenableto vector representation. Aerial photos areoften the source of other important basedata, such as street centerlines, with databeing digitally traced from the photo intoa vector system.

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Spatially enabling thedata: What is geocoding?

If we break the word geocoding into itscomponents, it means coding the Earth—providing geographic reference informa-tion that can be used for computermapping. The history of geocoding is tiedto efforts at the U.S. Census Bureau tofind ways of mapping data gatheredacross the country, address by address. In the 1960 Census of Population andHousing, questionnaires were mailed torespondents and picked up from eachhousehold by enumerators. In 1970, theplan was to use the mail for both sendingand returning surveys—hence referencesto that census as mail out/mail back. Thisdemanded geocoding capability and, sub-sequently, the development of an addresscoding guide (ACG). According to Cooke(1998), the Data Access and Use Labscreated to accomplish this were responsi-ble for creating today’s demographicanalysis industry.

The first geocoding efforts permitted onlystreet addresses to be digitized (admatch),but the capability to show blocks and cen-sus tracts was soon added. This demand-ed that block faces be recognized, and thiswas done by digitizing the nodes repre-senting intersections. This, in turn, meantthat intersections had to be numbered andaddress ranges had to be reconciled to thecorrect block faces. The shape of the lineson the map had to be precisely determinedand annotated, creating the map’s topolo-gy. The name given to this new blockmapping process was dual independentmap encoding (DIME) and, when com-bined with the address matching process,it was referred to as ACG/DIME. By 1980,ACG/DIME had become geographic basefile (GBF)/DIME. This was followed by a call for a nationwide, seamless, digitalmap, to be called TIGER, short for topo-logically integrated geographic encodingand referencing. Census Bureau geogra-pher Robert Marx and his team imple-mented TIGER for the 1990 Census(Cooke, 1998; Marx, 1986).

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Figure 4.4

A map showing a vectorformat superimposed over a raster format. ArcView®,Baltimore County, Maryland.The heavy blue lines arepolice reporting areas (vector), triangles are aggravated assaults (vector),and the underlay isorthophotography (raster).

Sources: Reporting area polygons and assault data: Baltimore County Police Department.Orthophotography: BaltimoreCounty, Maryland, GIS Unit,March 1995, pixel size: 1inch, compilation scale 1inch=200 feet. Reproducedby permission.


TIGER files contain address ranges ratherthan individual addresses. An addressrange refers to the first and last possiblestructure numbers along a block face,even though the physical structures maynot exist (figure 4.5). For each chain ofaddresses between the start node and endnode, there are two address ranges, onefor odd numbers on the left, the other foreven numbers on the right. For a completeexplanation, see U.S. Census Bureau(1997).

Geocoding is vitally important for crimemapping since it is the most commonlyused way of getting crime or crime-relateddata into a GIS. Crime records almostalways have street addresses or otherlocational attributes, and this informationenables the link between the database andthe map.

How does the computer map in a GIS knowwhere the data points should be put? Itreads the x-y coordinates representingtheir locations. When crime locations aregeocoded, the address is represented by x-ycoordinates, usually either in latitude andlongitude decimal degrees or in State-plane

x-y coordinates identified by feet or metermeasurements from a specific origin. Thebig headache in working with address datais that those data are often ambiguous andmay be erroneously entered in field set-tings. Common field errors include:

■ Giving a street the wrong directionalidentifier, such as using east instead ofwest or north instead of south.

■ Giving a street the wrong suffix orstreet type (e.g., “avenue” instead of“boulevard”); providing no suffixwhen there should be one.

■ Using an abbreviation the streets database may not recognize (e.g., St.,Ave., Av., or Blvd.).

■ Misspelling the street name.

■ Providing an out-of-range, or impossi-ble, address. For example, a street isnumbered 100 to 30000, but an extrazero is added, accidentally producingthe out-of-range number 300000.

■ Omitting the address altogether.

98 Chapter 4

Figure 4.5

A map showingTIGER/Line® address

range basics.

Source: U.S. CensusBureau, 1997,

figure 3–1, pp. 3–9.

As you initially attempt automatic geocod-ing, street addresses are compared againstthe existing street file database, and coor-dinates are assigned to the “hits.” Thisprocess is sometimes called batch match-ing. The process is a one-time affair, doneautomatically. Then, it becomes necessaryto deal with the “misses,” those addressesthat did not geocode automatically.

Handling misses is done manually. Thebad address is displayed with the closestpossible matches the database includes.Analysts use these options to select themost likely match. This involves someguesswork and risks geocoding errors. Forexample, if the address entered is 6256Pershing Street, and the only reference toPershing in the database is to PershingAvenue, then assigning the geocode to“avenue” is not likely to be an error. Onthe other hand, if the database also con-tains Pershing Boulevard, Pershing Circle,and other Pershing suffixes, assigning“avenue” could be wrong. This showshow important it is to have standards forentering addresses into a file, whether thesystem deals with records or computer-assisted dispatching (CAD).

Not all records in large data sets are likelyto be successfully geocoded. The title ofa section in a chapter in the MapInfoProfessional User’s Guide (MapInfoCorporation, 1995), “Troubleshooting:Approaching the 100% Hit Rate,” hintsat this. Some records may not be salvage-able for a variety of reasons, includingambiguity in an address that cannot beresolved. Two other issues deserve men-tion, as well. One is that street addressesare estimated along block faces and maynot represent true block face locations.(For more on this, consult technical docu-

mentation.) Second, address matching canbe done for locations other than streetaddresses, such as street centerlines, landparcels, or buildings, depending on theavailability of each element in a spatiallyenabled format.

Surprisingly, there is no minimum stan-dard for geocoding. Maps can be producedand distributed based on a 25-percent hitrate. Readers may have no idea that amap represents only a small fraction of allcases. Worse, the missing cases may notbe randomly distributed, thus possiblyconcealing a critical part of the database.For example, in the geocoding process, aperson or persons may be inept or maydecide to distort the data. If this error orig-inates in the field, it will probably have ageographic bias based on the location ofthe person making errors. Analysts mayconsider reporting the hit rate for geocod-ing to better inform map readers.

Although most map users may not under-stand the hit rate, a technical footnotereading, “X percent of cases were omitteddue to technical problems, but, the policedepartment considers the pattern shown tobe representative of the total cases underconsideration,” may clarify the information.(Seek legal advice for actual wording.)

Given that there is no minimum standard,the issue becomes: What hit rate is accept-able? This is a subjective decision, but a60-percent hit rate is unacceptable andmay lead to false assumptions. Hit ratesthis low should raise questions about acrime analysis unit’s level of readinessbecause low hit rates indicate that thebase maps in use and/or incoming dataare seriously deficient.

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A distinction needs to be made betweenthe hit rate and another geocoding meas-ure, the match score. The latter is a scorederived from matches on each componentof the address. If all components of anaddress are correct—street name, direction,street type—the address will receive a perfect score. Missing or incorrect partsreduce the score. This differs from the hit rate, which is the percentage of alladdresses that are capable of beinggeocoded in either batch or manual mode.Therefore, the hit rate and match scorecan be used to set acceptable geocodingstandards. However, setting the acceptablethreshold of either rate too high or too lowmay result in too few records making thecut or, in the worst case scenario, incidentsbeing given wrong addresses, thus placingcrimes on the map where they did nothappen.

Like some other aspects of computer map-ping, geocoding can be quite involved anddemand considerable practice and expert-ise before you can regard yourself as anexpert. The technical procedures used tofix geocoding problems are beyond thisdocument’s scope. Readers are referred tothe user’s guides, online help, and refer-ence guides that accompany software orthat are available on a proprietary basis.Asking more experienced GIS users foradvice, perhaps in other departments oflocal government (management informa-tion systems, planning, engineering, andso forth), is another possibility. For addi-tional information, see Block (1995).

Data selecting, filtering,and mapping accordingto useful criteria

The great power of GIS lies in its analyticalcapability, in addition to its capacity toquickly create maps of large, complex datasets. This analytical power takes moreforms than can be described here, but afew examples offer the reader the generalidea. (For an excellent overview, seeMcEwen and Taxman, 1995.) Since pro-cedures vary according to the GIS softwareused, we offer a broad sketch of somepossible analytical approaches. Specificdetails can be found in user’s guides, ref-erence manuals, tutorials, and listservssuch as [email protected], wheremany analysts pose questions and getuseful answers on a variety of crime map-ping topics, including how-to GIS questions.

Selecting and displaying specific information

Perhaps the most basic analytical task inusing geographic information systems isthe process of selecting and displaying scientific information. As shown in figure4.6, when objects, in this case aggravatedassault cases, are selected or “highlighted”on the map with the select tool, their cor-responding database records are highlight-ed in color or with a symbol located nextto the record. If the analyst wants to bringtogether the selected records at the top ofthe table for easier recognition and manip-ulation, they can be “promoted” (inArcView, for example) using the appropri-ate button.

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Mapping time

A more sophisticated process for selectinginformation uses various criteria and isreferred to as “filtering” or “querying.”Sometimes, you will see Structured QueryLanguage (SQL), which is a specializedprogramming tool for asking questions ofdatabases. If the conditions you set aresatisfied, then certain cases are included

or retained in a new data set, as a subsetof the main data. The new file can then be saved, mapped, or manipulated in anyway. For example, the condition “Time isgreater than or equal to 1500 hours andtime is less than or equal to 2300 hours”(written in computerese as time>=1500& time <=2300) would select all cases in the 8-hour shift between 3 and 11 p.m.

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Figure 4.6

A map and table framesfrom ArcView® showingthe relationship betweenobjects selected on themap and their highlightedrecords in the table.



Many variations are possible on the“mapping time” theme. In figure 4.7,maps of domestic disputes in Charlotte-Mecklenburg, North Carolina, showchange over a decade. The units of analy-sis were the central points, or centroids(see the section “Centroid display” later inthis chapter) of 537 response areas. Idrisisoftware (see appendix) was used to gen-erate the two surfaces based on the squareroots1 of the data values for each responsearea, resulting in a clear picture of sub-stantial growth over the time period. In1984, domestic disputes occurred mainlyin the north and west, as shown in green.By 1993, calls had increased in numberand geographic coverage, particularly onthe east side and in the southwest.

Time geography is viewed through a dif-ferent lens as shown in figure 4.8. Usingmethods similar to those used to producefigure 4.7, seven daily maps were con-structed, followed by an eighth map todenote the day of the week with the high-est frequency of calls for each of the 537

response areas. Maps such as those in figures 4.7 and 4.8 could be used to allo-cate resources and to coordinate domestic violence prevention efforts.

Mapping space

Because crimes usually affect some neigh-borhoods more than others, maps mayfocus on certain beats, posts, patrol areas,communities, census tracts, neighbor-hoods, or other units. What is the geogra-phy of crimes in terms of council districts?Could this information be used by thepolice department to anticipate politicalfirestorms? Attention may not be confinedto such official areas, but may involveinformal or ad hoc areas, such as a 500-yard radius around a drug market, busstop, or automatic teller machine, for tem-porary investigative purposes. Providedthat the boundary files for the officialareas are in the computer, queries can beaddressed to them. For example, youcould compute the rate of vandalism inci-dents per 1,000 housing units, per unit of

102 Chapter 4

Figure 4.7

Maps showingdomestic disputecalls for service,1984 and 1993,

Charlotte-Mecklenburg,

North Carolina.

Source: Mapby J. LeBeau,

Southern IllinoisUniversity, with

data from theCharlotte-

Mecklenburg,North Carolina,

Police Department.Reproduced by

permission.

the general population, or per unit of themale youth population. A few alternativebase maps are shown in figure 4.9.

Mapping incident types andmodus operandi

Conditions, or filters, can be used to refinesearches at any level the analyst chooses.For example, the most obvious filterwould isolate all crimes of a specific type.However, filtering can isolate crimes bytime of day, by neighborhood, and bymodus operandi (MO). Conditions can beset to specify all the desired criteria, possi-bly resulting in the isolation of a cluster ofincidents that could be linked to the sameperpetrator. In figure 4.10, for example,

rape has been selected. These incidentsare shown without identifying victims byusing a large symbol to make the locationof each somewhat vague. The analystmust trade some precision to accommo-date the overriding need to protect victims.

Mapping attributes of victimsand suspects

Mapping by characteristics of victims, suspects, or both can also be useful and iseasily accomplished. For example, wherehave females been assaulted? Is there evidence that a cluster of burglaries hasoccurred at homes occupied by elderlypersons?

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Figure 4.8

A map showing theday of the week withthe most domestic dispute calls for service,Charlotte-Mecklenburg,North Carolina, 1993.

Source: Map byJ. LeBeau, SouthernIllinois University,with data from theCharlotte-Mecklenburg,North Carolina, PoliceDepartment. Reproducedby permission.

Figure 4.9

Some alternatebase maps for usein crime mapping.



Mapping other recorded characteristics

Possibilities for filtering are unlimited. Youcan set as many time, space, victim, sus-pect, MO, or other filters as you wish,given data availability. For example,where are the armed robberies occurring?What is the pattern of robberies at gun-point within a 1-mile radius of drug markets? Where are juvenile offenders

victimizing elderly persons at gunpointduring hours of darkness? Are spousalassaults randomly distributed, or are theyclustered?

The only limitation is the availability ofgeocoded or geocodable information in thedatabase. A potentially useful map mightreflect the relationship between personson probation or parole and the types of

104 Chapter 4

Figure 4.10

A map showing the distribution

of rape in the city of Cambridge,

Massachusetts.Large symbols

were used to avoidpresenting the

specific locations of the victims.

Source: Cambridge,Massachusetts,

Police Department.Reproduced

by permission.

crimes they have committed. For example,a rash of robberies occurs in a neighbor-hood: Where are the robbery probationersand parolees in that area? Do the MOsmatch up?

Using GIS to measure from maps:Aggregating data

Why measure? In the most general sense,measurement is the foundation of scientif-ic analysis, and it lies behind any quanti-tative analytical statement. For example,what is the crime rate? To answer this wehave to know the base of the rate. Do wewant it per 1,000 persons, per reportingarea, or per patrol district? To calculatethis rate we must know how many crimeincidents have occurred, and, if we arecalculating a population-based rate, howmany persons there are per unit area. Thisvalue, the base of our rate, is also knownas the denominator, because it is the bot-tom of the fraction used to calculate therate. Therefore:

density = number of incidents per areapopulation per area

Here, “number of incidents per area” isthe numerator (top of the fraction) and“population per area” is the denominator.If there are 428 incidents and the popula-tion expressed in thousands is 3.7, therate is 428/3.7, or 115.68 per 1,000 per-sons. We can check that the calculation iscorrect by multiplying the rate by the pop-ulation to reproduce the original incidentcount:

115.68 x 3.7 = 428.

A GIS program would do these calcula-tions for you, but analysts need to knowhow to provide appropriate instructionsbefore anything useful can be produced.

An application of density analysis isshown in figures 4.11, 4.12, and 4.13.First, the density of burglar alarm callswas mapped using 48,622 locations for alarm calls in 1990 in Charlotte-Mecklenburg, North Carolina. Using theArcView Spatial Analyst extension, a gridwas used to generate the surface shownin figure 4.11. Peaks occurred near thecentral business district, along majortransportation arteries, and in the industri-al northwest area. A similar map (see figure 4.12) was prepared to show thedensity of the 10,288 burglaries reportedin 1990, focusing on both the centralbusiness district and a radial, highway-oriented distribution. In the final phase,the burglary density surface was subtract-ed from the alarm density surface, and aquery tool was used to select parts of thesurface where burglary density exceededalarm call density (see figure 4.13). Thismap directs the analyst to areas wheredisplacement may be occurring and suggests areas for possible interventions.Such interventions could include addition-al alarm installations or other target-hardening measures (see figures3.11–3.15).

The various types of measurement nowavailable in GIS programs are too numer-ous to describe. However, a few types ofmeasurement will be outlined to provide asense of what can be done.

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106 Chapter 4

Figure 4.11

A map showingthe density of burglar alarm

calls, Charlotte-Mecklenberg,

North Carolina,1990.

Source: Map byJ. LeBeau,


data from theCharlotte-

Mecklenburg,North Carolina,


permission.

Figure 4.12

A map showingthe density of

burglaries,Charlotte-

Mecklenberg,North Carolina,

1990.

Source: Map byJ. LeBeau,


data fromthe Charlotte-Mecklenburg,

North Carolina,Police Department.


Count incidents in areas

Although the primary need for countingwill be to total crime incidents, countingother objects or events could be useful,too. It may be helpful, for example, toknow how many and what types of alcoholic beverage licenses (beer, liquor,restaurant, liquor store, and so forth) arein specific areas. The information couldserve as a gross index of alcohol availabil-ity, although it would not necessarily indicate where or how much alcohol isconsumed or by whom. Or perhaps thelocal building inspection agency supplies alist of addresses of code violations. Thesecould be mapped and sorted by any relevantareas, such as neighborhood associationjurisdictions or police operations areas.

GIS software makes such identificationeasy. Users may find it helpful to writedown the operation they want to do toclarify the steps—especially if they antici-pate several steps of filtering, measuring,or other manipulations. In fact, this exer-cise is helpful for any kind of analysis.For example, you might want to countincidents of spousal abuse within patroldistricts, which in some programs mightbe expressed in SQL something like this:

count spousal abuse.objectwithin patrol district.object

This tells the program to evaluate eachspousal abuse incident (“object”) anddetermine in which patrol district itoccurred. Additional instructions may askthe program to group incidents by patrol

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Figure 4.13

A map showing areaswith burglary densitygreater than alarm calldensity, Charlotte-Mecklenberg, NorthCarolina, 1990.

Source: Map byJ. LeBeau, SouthernIllinois University,with data from theCharlotte-Mecklenburg,North Carolina,Police Department.Reproduced by permission.


district by listing in a new table or fileeach patrol district identification numberand the number of spousal abuseincidents that occurred there.

Measure areas and distances

Area measurements are especially usefulfor determining how many crimes occurper unit area. This is not to be confusedwith crime measurements by populationsize or density. Generally, though not nec-essarily, crime densities will reflect popula-tion densities because population densityis an expression of crime potential. Morepeople means more potential victims andoffenders.

Distance measurements are also simple.They require the use of ruler or tape soft-ware tools and, as with area measure-ments, the units can easily be changed.

Measure inclusion and overlap

Areas of interest in policing do not always fit together neatly. Police districts,precincts, patrol areas, and so forth, maynot match school districts, council dis-tricts, census tracts, neighborhoods, community conservation districts, andofficially designated hot spots. GIS toolsallow users to measure overlaps betweenareas or small enclaves in large areasbecause any incidents found in a specificarea can be electronically identified. Allthe crimes (or drug markets, liquor licens-es, parolee addresses, injury accidents,and so forth) in a specific area can beselected and separated as a new data setfor special analysis. How are drug arrestsdivided among council districts or neigh-

borhood association areas? One GIS pack-age, for example, includes the followingfunctions: contains, contains entire, within, entirely within, intersects. Thesecapabilities are typical of this type of analytical function.

Centroid display

A centroid is an area’s center defined asthe halfway point on its east-west andnorth-south boundaries. However, thecentroid will not always be inside thearea. For example, an area may be L-shaped, in which case the centroidtheoretically would fall outside the area. A centroid is generally used as the pointwhere labels will be located by default andwhere statistical symbols will be placed.

Centroids can approximate the geographicmidpoints of areas, which may in turn(theoretically) approximate the mostaccessible points in the areas. Normally,centroids are hidden, but they can be displayed on request. If you have areaobjects without centroids, the centroid(x,y) function can be used to generatethem. Centroids are used infrequently incrime analysis and are typically used assurrogates for other conditions, such asaccessibility.

For surface-fitting purposes, the data values that apply to tracts, block groups,or blocks could be assigned to their cen-troids, thus reducing areas to points forcomputational convenience. Given thecommon use of grid-based, surface-fittingalgorithms, however, this type of centroidapplication is unlikely.

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Derivative measures:How to create new indicators

Derivative measures are new variablescreated by manipulating information inone or more databases. The rate calcula-tions discussed in the section “Using GISto measure from maps” are a simple formof derivative measures that divide a crimecount by a population measurement to produce a population-based rate.Generally, if you can express a sought-after relationship using ordinary mathe-matical logic, then it can be calculated in a GIS. There you will typically find anarray of operators (+, -, =, *, “like,” “contains,” “and,” “or,” “not,” and soforth), aggregates (average, count, minimum, maximum, sum, and so forth),and functions (area, centroid, distance,perimeter, day, year, and so forth). Theseprovide substantial versatility in generalanalysis or in creating derived measures.

Getting a bit more fancy

The complexity of potential derivativemeasures is unlimited. For example, youmight want to create a quality-of-lifemeasure for community areas that goesbeyond mapping income or real estatevalues. This index could include suchvariables as crime rate, education levels,dropout rate, drug addiction measures,and incivility reports. Provided the under-lying data can be geocoded, or joined to a geocoded table, they can be mapped.Then most, if not all, mathematicalmanipulations can be done in the GIS.

Apples and oranges

How do you combine variables measuredin different units, such as dollars, years,or population? The quickest approach is to combine data in the GIS using overlaysand then to use “logical operators” suchas “greater than” or “less than” to reselectgroups using your criteria. A more indepthprocess, but one that leads to greaterfamiliarity with the data, involves con-verting the data values into ranks (ordinalscale measurement) before making anycalculations. Although you will lose theratio level measurement this way, yougain the overwhelming advantage of being able to work with any units ofmeasurement. Another advantage of this process is that conversion to rankssmooths the effects of poorly measureddata by intentionally making them lessprecise. The conversion eliminates someof the “phony” precision of data that areinherently subject to error.

A problem that can generally be overcomeis that GIS software is typically weak instatistical (as compared with purely math-ematical) tools and may not be able toconvert data to an ordinal scale.2 Thiscould be done by using a statistical soft-ware package such as S+, SAS, or SPSS.Or if the number of measures and areas is not too large, the work could be donemanually. Then ranks are summed to gen-erate the index, after taking care to organ-ize ranks so the lower numbers alwaysrepresent either the best or the worst, butnot a mix of both. The resulting numberswill indicate a cumulative rank, or relativestatus, that can be mapped (figure 4.14).Crime data can then be overlaid on theindex map to show a possible relationshipwith, for example, social dysfunction. For

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additional explanation, see Harries andPowell (1994).

GIS as a tool for data integrationand exploration

A GIS is an ideal tool for bringing togethervarious databases that share commongeography. This function will becomemore useful as the importance of dataintegration is increasingly recognized. Notonly is there a need for more data integra-tion, but there is also a need for recogni-tion that most data used in policing aboutland use, street centerlines, liquor estab-lishments, bus routes, schools, subwaystops, and so forth are likely to come fromsources outside the police department.Finding these types of data and adaptingthem for crime analysis often take consid-erable initiative and may also demandattention to data quality. This raises the

issue of metadata. This term refers todata about data. Metadata provide infor-mation about the databases that you use.(See “Minimum for Federal GeographicData Committee-Compliant Metadata.”)Metadata standards are developed underthe auspices of the Federal GeographicData Committee (FGDC), a unit that coor-dinates development of the NationalSpatial Data Infrastructure (NSDI). (Seethe appendix for additional resources.)

All data with common geography can be overlaid. These layers may be manipu-lated—moved up or down, added orremoved (permanently or temporarily), ormade to become visible or invisible onlywhen the map is shown at a specifiedscale. As noted on page 92, “What Is aGIS?,” a fundamental concept in GISis layering. The various forms of thisprocess can provide a GIS with much ofits power and flexibility.

110 Chapter 4

Figure 4.14

A map based onan Urban

Pathology Index.

Source: Harriesand Powell,

1994, figure 2,p. 54. Adaptedby permission.

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Minimum for Federal Geographic Data Committee-Compliant Metadata

Identification_Information: Citation:

Citation_Information: Originator: This is the originator Publication_Date: 000000 Title: This is the title

Description: Abstract:

A concise abstract of the data

Purpose: (Types of projects that use the data, models, general use, illustrations, spe-cific type of analysis, and so forth. Be specific if the data are geared towarda narrow set of applications.)

Time_Period_of_Content: Time_Period_Information:

Single_Date/Time: Calendar_Date: 000000

Currentness_Reference: Publication date of sources

Status: Progress: Complete Maintenance_and_Update_Frequency: Unknown

Spatial_Domain: Bounding_Coordinates:

West_Bounding_Coordinate: -109.9959 East_Bounding_Coordinate: -108.0022 North_Bounding_Coordinate: 37.0671 South_Bounding_Coordinate: 35.9330

Keywords: Theme:

Theme_Keyword_Thesaurus: none Theme_Keyword: None

Access_Constraints: none

Use_Constraints: Limitations of data for use at certain scales and date ranges and for use withother data

Metadata_Reference_Information: Metadata_Date: 19950628 Metadata_Contact:

Contact_Information: Contact_Organization_Primary:

Contact_Organization: Bureau of Land Management Contact_Address:

Address_Type: mailing address Address: PO Box 0047 DWOCity: Denver State_or_Province: CO Postal_Code: 80225-0047

Contact_Voice_Telephone: 555-5555

Metadata_Standard_Name: FGDC Content Standards for Digital GeospatialMetadata

Metadata_Standard_Version: 19940608

Source: http://www.blm.gov/gis/meta/minimum.html


Combining data in geographic space pro-vides opportunities for data explorationand analysis that are lacking when geo-graphic data are missing. An analyst maywant to see how robbery locations relateto the locations of convenience stores.Although this information may be in dif-ferent databases, it can be brought togeth-er in GIS and the locations subjected tothe necessary analysis. For example,buffer zones could be constructed at aspecified distance around each conven-ience store, and the number of robberiesin each zone counted. Then the percent-age of robberies proximal to stores couldbe calculated to provide an indication ofthe importance of this type of store as arobbery target. The possibilities offered bythis type of spatial analysis are virtuallyunlimited. They include hot spot analysis,stolen auto recovery directions and distances, delineations of gang turfs, calculations of area-specific rates, the construction of crime or other “surfaces,”network analyses, boundary determina-tions, and others mentioned elsewhere.

Hot spots

Definition in geographic space

The term hot spot has become part of thecrime analysis lexicon and has received alot of attention. What are hot spots? Howdo we recognize them?

A hot spot is a condition indicating someform of clustering in a spatial distribution.However, not all clusters are hot spotsbecause the environments that help generate crime—the places where peopleare—also tend to be clusters. So any

definition of hot spots has to be qualified.Sherman (1995) defined hot spots “assmall places in which the occurrence ofcrime is so frequent that it is highly predictable, at least over a 1-year period.”According to Sherman, crime is approxi-mately six times more concentratedamong places than it is among individu-als, hence the importance of asking“wheredunit” as well as “whodunit.” (See the appendix for hot spot-relatedresources.)

A great deal of confusion surrounds thehot spot issue, including the distinctionbetween spaces and places. Block andBlock (1995) pointed out that a placecould be a point (such as a building or aclassroom) or an area (such as a censustract or a metropolitan region). However,the former generally are regarded asplaces, and the latter, with their greaterarea, are spaces.

Sherman’s definition notwithstanding,there is currently no widely accepted definition of a hot spot. Indeed, a rigid,absolute definition may not be possible.Except for programs with procedures thatself-define hot spots, such as the Spatialand Temporal Analysis of Crime (STAC)program (Block, 1995), jurisdiction-specific procedures to define hot spotsmay make the most sense because theywill fit local conditions. In BaltimoreCounty, Maryland, for example, hot spotsare identified according to three criteria:frequency, geography, and time. At leasttwo crimes of the same type must be pres-ent. The area is small, and the timeframeis a 1- to 2-week period. Hot spots aremonitored by analysts until they becomeinactive (Canter, 1997).

112 Chapter 4

In many cases, analysts may not be ableto define hot spots but may know onewhen they see it. This makes comparisonsdifficult both within and between jurisdic-tions.3 Furthermore, meaningful time-based analyses are problematic, becausehot spot definition criteria may not beused consistently over time.

Wide interjurisdictional and intrajurisdic-tional variations in environments alsomake the application of absolute definitioncriteria tricky. For example, the size andshape of city blocks vary widely. West ofthe Appalachian Mountains, city layoutsare usually dictated by the rectangularland survey system, and blocks tend to befairly regular and rectangular. In the east,where metes-and-bounds surveys pre-vailed, blocks are more likely to be irregu-lar in shape and size. Densities also varygreatly. Can the same definition criteria be applied in low-density areas as in high-density areas? Crime-prone populationsare found in both environments. Can hotspots exist in very low-density suburbs?Residents would probably think so.

Hot spots and scale

Are hot spots purely a function of scale?Some argue that any set of points in geographic space can be made into a hot spot if the scale is modified enough. At extremely small scale,4 all the crimeincidents in an entire metropolitan areaappear to be a hot spot (figure 4.15, upperleft). As scale increases, points becomemore dispersed (figure 4.15, upper rightand lower left) until, at the largest scale,individual points can be isolated (figure4.15, lower right). The level of resolutionin the absence of absolute criteria makesit possible to manipulate the presence orabsence of hot spots. However, absolutecriteria are difficult to apply in urban envi-ronments (Brantingham and Brantingham,1995).

Generally, the hot spot concept is appliedto street crime rather than white-collarcrime, organized crime, or terrorist crime.That a few white-collar crimes might overwhelm street crime in their economicimpact tends to be ignored. This may be

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Figure 4.15

An illustrationof the scale-dependency ofhot spots.


Figure 4.16

A map showing hotspot changes over

time: Detroit, 1994and 1997.

Source: Martin,Barnes, and Britt,

1998, figure 3, p.10. Reproduced by

permission.


because white-collar crime does not causethe same kind of community fear andanxiety as street crime. Similarly, if a cityexperienced several terrorist bombings orschool shootings within 1 year, it is con-sidered a hot spot that defies the normalhot spot definition. There is a qualitativeaspect to hot spots; they refer only to limited crime types.

Hot spots in time

Just as hot spots can be described geo-graphically, they can also be defined usingtime-related criteria. An important ques-tion is: How long is a hot spot “hot”? The answer requires defining an incidentaccrual rate within the spot, based on unitsof time. Related decisions are needed todetermine whether the hot spot’s “temper-ature” is measured according to all con-firmed crimes, all calls for service, specificcrimes, or other conditions. In a GISframework, hot spots (and/or incidentswithin hot spots) can be color coded orotherwise symbolized according to theirage.

An approach to monitoring hot spots overtime is shown in figure 4.16. This mapshows Devil’s Night arson hot spots inDetroit in 1994 and 1997, using the STACprogram developed by the Illinois CriminalJustice Information Authority. Althoughnot a mapping program itself, STAC can be used with most popular GIS packages.(For additional details on STAC, see theappendix and figure 4.20.)

Definition and measurement

What is a hot spot? Perceptions and defi-nitions vary widely. Some analysts maysee a hot spot as any cluster that looksinteresting. Others define hot spots usingrigid, detailed criteria. A study by Buerger,Cohn, and Petrosino (1995) found thatthe latter group initially used the followingrelatively formal criteria:

■ Not more than one standard linearstreet block (one side of the streetonly).

114 Chapter 4

■ Not more than half a block from anintersection.

■ No closer to another hot spot than oneblock.

The Buerger group further identified threeprincipal definition-related issues:

■ Public space. Hot spots were initiallylimited to one side of the street, raisingthe question of how street curtilage(public space in front of private proper-ties) would be treated. Common sensedictated that if a patrol car was acrossthe street, technically outside the hotspot, it should be considered in the hotspot, so the definition was modified toinclude both sides of the street.

■ Intersections. Ambiguities surroundedthe definition of an intersection. Theterm eventually came to include notonly the street, but also adjacent side-walks and buildings. Even when a hotspot did not technically include all fourcorners of an intersection, it was foundthat the best viewpoint for seeingaround a corner might be on the otherside of the street, outside the hot spot.Thus, all four corners of intersectionscame to be included in hot spots.

■ One-block exceptions. Irregularblocks with large open spaces con-tained some hot spots, making excep-tions to the one-block rule.

In practice, hot spots are defined innumerous ways, some with rigid criteria,like those above, and others with a moreflexible approach. None is right or wrong.Both approaches have pros and cons, andan informal cost-benefit analysis can

determine the ideal criteria in individuallocations. The sharply defined criteria mayomit many commonsense exceptions (butallow greater comparability in space andtime); softer rules permit easy adaptationto local variation (but make comparisonsdifficult).

Hot spot mapping

A detailed presentation of hot spot map-ping methods is beyond the scope of thisguide. However, an investigation spon-sored by the Crime Mapping ResearchCenter at the National Institute of Justicein 1998 can offer some tips. This assess-ment found that most hot spot analysismethods fall into one of five categories:visual interpretation, choroplethmapping, grid cell analysis, clusteranalysis, and spatial autocorrelation(Jefferis, 1999; see also Canter, 1995).

■ Visual interpretation. The surveyshowed that, of the police departmentsthat do computer mapping, 77 percentconducted hot spot analyses. Of these,86 percent identified hot spots visually,and 25 percent used a program to per-form this task (Mamalian and La Vigneet al., 1999). The problems presentedby the visual approach include over-lapping points, points stacked on top of one another, making it impossible tosee how many incidents are represent-ed5 (that is, only one appears at anygiven location). Most serious, perhaps,is that readers’ interpretations of pointdata vary, resulting in different inter-pretations of the same patterns.

■ Choropleth mapping. Areas areshaded according to their data values,by either rate or frequency. The

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caveats mentioned in chapter 2 stillapply—class interval selection methodswill affect the appearance and interpre-tation of the map (see figures 2.15 and2.16), as will color choices, shadinglevels, and size variation among thepolygons. The latter elements tend todraw attention to the largest areas,particularly when they have higherdata values.

■ Grid cell analysis. A grid is superim-posed over a map. Points within cells,or within a designated radius from thecenters of the cells, are assigned to thecells. The size of cells is variable andaffects the outcome of the analysis.Small cells present higher resolution, at the cost of more computer resources.With larger cells, resolution suffers, but computation is easier. What is theadvantage of grid cell analysis over apin map? First, adding points to thegrid solves the problem of “stacked”data points, which occurs when multi-ple incidents occur at the same locationor nearby locations. Second, the points

are transformed into a smooth surface,generalizing the data. (For relatedmethods, see the appendix: ArcViewSpatial Analyst Extension, Idrisi,Vertical Mapper, the U.S. Departmentof Justice Criminal Division Hot SpotSlider.) Several examples of grid cellanalysis have been illustrated in fig-ures 4.11, 4.12, and 4.13. Anothermap of this type is shown in figure4.17, which depicts hot spots in theUnited Kingdom city of Nottinghamand police perceptions of hot spots.This map was produced using the cus-tom program known as SPAM (SpatialPattern Analysis Machine), which linksto MapInfo for the finished map (seeappendix). Variations of surface mappinginclude three-dimensional renditions,as noted in chapter 1. One key to read-ers’ perception of three-dimensionalmaps is the degree of vertical exagger-ation in the map. In the examplesshown in figures 4.18 and 4.19 ofSalinas, California, quite different typesof data (firearm crimes and gangs) areshown in three dimensions.

116 Chapter 4

Figure 4.17

A map showingdomestic burglary

hot spots and policeperception in theMeadows area of

Nottingham, UnitedKingdom.

Source: Ratcliffe andMcCullagh, 1998,

figure 1, p. 48.Reproduced by

permission.

■ Cluster analysis. Cluster analysismethods depend on the proximity ofincident points. Typically, an arbitrarystarting point (“seed”) is established.This seed point could be the center ofthe map. The program then finds thedata point statistically farthest fromthere and makes that point the secondseed, thus dividing the data points intotwo groups. Then distances from eachseed to other points are repeatedly cal-

culated, and clusters based on newseeds are developed so that the sumsof within-cluster distances are mini-mized. (For related methods, see theappendix: STAC, SaTScan, SpaceStat,and Geographic Analysis Machine(GAM.) Figure 4.20 illustrates hot spotsderived from the STAC method, whichperforms the functions of radial searchand identification of events concentratedin a given area (Levine, 1996).

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Figure 4.18

A map showingfirearm incidentsin Salinas,California.

Source: Salinas,California, Police Department.Reproduced bypermission.

Figure 4.19

A 5-year analysisof incident datawith an overlay of identified gangturfs in Salinas,California.

Source: Salinas,California, Police Department.Reproduced bypermission.


■ Spatial autocorrelation. This conceptrelies on the idea that events that hap-pen in different locations may be relat-ed. In a crime hot spot, for example,underlying social and environmentalprocesses generate crimes in a smallarea. Multiple events, such as the pres-ence of drug markets, may have simi-lar causes. This means that statisticalmeasures of this condition, known asautocorrelation, can serve as hot spotindicators (Roncek and Montgomery,1995).

All methods of hot spot mapping shouldproduce similar maps if there are underly-

ing and recognizable point clusters.Something is wrong if a method producesclusters where visual inspection indicatesthere are none. However, analysis shouldrecognize that some methods involveuser-defined search criteria, and variationsin those criteria, such as differences in cellsizes or search radii, can affect outcomes.

Buffering: Meaning and applications

A buffer is a zone around an object, suchas a school or intersection, that has someinvestigative or analytical significance. For

118 Chapter 4

Figure 4.20

A map showing robbery hot spots,using STAC, WestBronx, New York.

Source: Block andBlock, 1997,

figure 7.Reproduced by

permission.

example, drug-free school zones may bedefined using a 1,000-yard radius. Suchbuffers can be drawn around schools andoverlaid on large-scale aerial photographsso that field officers can easily recognizethe zone’s boundaries, even withoutdemarcating signs. Hardcopy maps can begiven to patrol officers as an aid in recog-nizing the zones. Buffering tools in GISprograms make this a relatively simpletask (figure 4.21).

Techniques for selecting objects can beused to identify certain types of events.For example, what are the characteristicsof calls for service within 1 mile of highschools? Calls for service can be identifiedand separated into a new data set if theyare within the 1-mile buffer.

Buffers are shown as circles if the locationbuffered is a point or street address, butbuffers do not have to be circles. For large

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Figure 4.21

An example of buffer-ing used in conjunctionwith the mapping ofrelated data. Red areasrepresent 1,000-footdrug-free school zonebuffers around schools,large polygons repre-sent police beats, andpoint data representdrug offenses.

Sources: J. Stith andthe Wilson, NorthCarolina, PoliceDepartment.Reproduced by permission.

Figure 4.22

A map showingpolygon buffersaround public housing inBaltimore, Maryland.

Source: Hyatt andHolzman, 1999.Reproduced by permission.


polygons like school campuses, parks,apartment complexes, or industrial plants,buffers can mirror the shape of the polygon. In figure 4.22, public housingproperties were buffered to evaluate therelationship between public housing andthe surrounding neighborhood. Theunderlying question was whether crime in public housing was committed mostlyby residents or by persons from the surrounding community. Analysis of incidents in the buffer zones can helpdetermine the answer, using data on the residential addresses of victims andoffenders. The same areas could be repre-sented either by a circle buffer (if it is represented as a point or address) or as apolygon buffer (if the area is mapped tomatch its actual footprint).

In a community policing example, ques-tions may arise about the quality of streetor neighborhood lighting. Analysts canconsult with city engineers to learn aboutthe illuminated radius of various street-

lights and their coordinates in the commu-nity. Then, using the buffer tool in GIS,circles of the appropriate radii can bedrawn around each light location to createa basis for assessing community concernsabout lighting quality (figure 4.23).

Also in the context of communitypolicing, an extremely disruptive phenom-enon is house fires. This is especially truein older neighborhoods with substandardrow houses, where the likelihood of firespreading from one home to adjacenthouses is great. In one study, buffers were drawn around residences where fire-related injuries occurred in the previous 2 years. This, along with census dataindicating risk, helped establish zones thatwere appropriate for the distribution ofsmoke alarms. (This may seem like a firedepartment function, but economic andsocial disruptions can contribute to condi-tions in which crime flourishes, makingboth fire and police functions relevant, aswith arson cases. This helps demonstrate

120 Chapter 4

Figure 4.23

The Grier Heightsneighborhood in

Charlotte, North Carolina,

showing street lightbuffers and housing

tenure in 1996.

Source: E. Groff and Charlotte-

Mecklenberg PoliceDepartment.


the breadth of data that crime analystsneed access to.)

In Tornado Alley, the high-frequency tornado region in the Plains States, a community safety concern is the audibilityof warning sirens. Because the decibeloutput of sirens is known and varies withweather conditions, a GIS can be used tomap the audible zone of each siren. Areaswhere sirens cannot be heard can be tar-geted by public safety agencies for specialaction during tornado warnings. This typeof map could also serve as a blueprint forlocating new sirens.

Data, data, everywhere:What’s an analyst to do?

Mapping outside data

Although most data used in crime analy-sis are generated and used within onedepartment, the need to integrate informa-tion from other agencies is becoming moreimportant. Unfortunately, outside data areoften in an incompatible format. What doyou do when you get a delimited or fixedfield ASCII file, for example? Table 4.1,“Census data in ASCII,” shows dataalmost exactly as they are presented onthe U.S. Census Bureau Web site, withonly slight editing to adjust the spacing.The Federal Information ProcessingStandard (FIPS) codes attached to all census geographic areas6 make up the first two columns. Starting in the left-handcolumn, the State of Missouri FIPS code is 29. In the next column, the city of St.Louis FIPS code is 510. Next are the tractnumbers, literal tract numbers, tract popu-lations (P00010001), tract median family

incomes in 1989 (P107A001), and tractper capita incomes in 1989 (P114A001).

Most GIS software can handle data com-piled in a variety of formats, althoughsome variations may generate headaches.MapInfo will open the following formats:dBase, Lotus®, Microsoft Excel®, delimitedASCII, some raster files (.tif, .pcx, and so forth.), AutoCAD® (.dxf), and others,using either the Open or Import com-mand. ArcView expects tabular data to be in dBase (.dbf), Info, or delimited text(.txt) format. One solution to the some-what limited data conversion repertoires of some GIS programs is to launder filesthrough a more versatile spreadsheet pro-gram and then transfer them to the GIS ina more compatible format.

To do this, the user imports the foreigndata spreadsheet into Lotus, MicrosoftExcel, or Microsoft Access®, and thenexports it in a GIS-compatible format. Thisway, a fixed-field ASCII file can be con-verted into a delimited ASCII or dBase format. This is done by parsing the fixed-field file in the conversion program, andthen outputting it in a delimited format.Parsing is a process of instructing the pro-gram how to read the fixed-field data byidentifying the variables in each field anddragging field delimiters to appropriatelocations. For example, the analystinstructs the program that the case num-ber is in columns 1–10, the address is incolumns 11–30, and so forth. Delimitedmeans that each data field is separatedfrom the next by a character such as acomma or a tab. With delimiters, it doesnot matter if data values have differentwidths, as in the sequence 3.5, 14.276.When the program recognizes the

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122 Chapter 4

Table 4.1. Census Data in ASCII Tab-Delimited Format asAcquired Directly From U.S. Census Bureau Web Site

(http://www.census.gov) See text for explanation

(Selected census tracts for St. Louis, MO)

FIPS. FIPS. FIPS. STUB.GEO P0010001 P107A001 P114A001ST CO. TRACT90

29 510 1011 Tract 1011 000002867 000032281 14262

29 510 1012 Tract 1012 000003287 000041559 15743

29 510 1013 Tract 1013 000004446 000036234 15401

29 510 1014 Tract 1014 000003075 000028322 11859

29 510 1015 Tract 1015 000003763 000021519 9787

29 510 1018 Tract 1018 000003773 000025197 9027

29 510 101899 Tract 1018.99 000000028 000000000 13929

29 510 1021 Tract 1021 000002820 000032625 15883

29 510 1022 Tract 1022 000006608 000040897 17131

29 510 1023 Tract 1023 000001834 000031157 14262

29 510 1024 Tract 1024 000002550 000034545 13226

29 510 1025 Tract 1025 000002253 000034911 13158

29 510 1031 Tract 1031 000003429 000039677 19250

29 510 1034 Tract 1034 000002200 000037222 13316

29 510 1036 Tract 1036 000001617 000033105 13240

29 510 1037 Tract 1037 000002883 000035461 14024

29 510 1038 Tract 1038 000004213 000039194 15466

29 510 1039 Tract 1039 000001229 000028375 11771

29 510 1041 Tract 1041 000003153 000030313 12649

29 510 1042 Tract 1042 000004076 000030660 14218

delimiter as a cue, it moves to the nextvalue.

You may receive data that consist of x-y coordinates, without the points them-

selves. In such situations, the coordinatesare used to generate the points in the GIS,using a Create Points command that allowsusers to select a preferred symbol and anappropriate projection. (For information

about map projections, see chapter 1.)After the points are generated, they canbe imported as a new layer on the map.Data generated in the field, perhaps frompatrol cars using global positioning systemtechnology, can be treated in the samemanner.

Any database with an address or geo-graphic reference included can be mapped,provided the corresponding digital basemap is available. For example, you maywant to map census tract data. The cen-sus data are available, but the map oftracts is not. In this case, the map is readi-ly available to download off the Web, butnon-census tract maps must be acquired elsewhere.

Data warehousingand data mining

Police departments generate volumes ofinformation. A single call for service ulti-mately results in its own pile of paper, andcomputer files tracking all calls for servicegrow rapidly. Data warehousing and datamining provide sophisticated ways of stor-ing and accessing information.

A data warehouse is a megadatabase thatstores data in a single place instead ofstoring them in project files or throughoutthe local government or governmentagency. Government agencies have beenslow to do this because agency politicstend to create an attitude oriented moretoward defending departmental turf thantoward sharing data. A data warehousecould assist with crime analysis efforts,which often demand data from diversesources, such as the health, housing, traf-fic, fire protection, liquor licensing, andplanning agencies.

Law enforcement is primarily a local gov-ernment activity, which often leaves policeagencies at the mercy of data managersoverseeing city or county informationtechnology functions. Ideally, data ware-houses consolidate all jurisdictional data-bases and permit use of data from anyagency according to quality control stan-dards. Data mining, as the label suggests,involves digging nuggets of informationout of vast amounts of data with special-ized tools. These tools are typically calledexploratory data analysis (EDA), which, in the context of mapping, can becomeexploratory spatial data analysis (ESDA)tools. An IBM software engineer (Owen,1998) identified these as the factors thatbrought data mining to the attention ofthe business community:

■ The value of large databases in providing new insights is recognized.

■ Records can be consolidated with aspecific audience or objective in mind.

■ Cost reductions are achieved withlarge-scale database operations.

■ Analysis is being demassified (futuristAlvin Toffler’s term) meaning that theinformation revolution permits the cre-ation of specialized custom maps forspecific audiences.

Chapter 2 discussed hypothesis testing.That discussion now comes full circlebecause data warehousing and data min-ing make hypothesis testing even morepractical. Queries can be addressed to largearrays of data, increasing the reliability ofresponses. However, this is truer for histor-ical questions than for current data.

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Cautions

Although GIS makes mapmaking relative-ly easy, it is not necessarily easy to makegood maps. The fundamental problem isthat fancy fonts, tables, maps, or dia-grams can dress up almost any data.However, just because data look gooddoes not mean they are.

As a rule of thumb, do not blindly acceptthe default settings in GIS programs.Defaults apply to such steps as the selec-tion of class intervals in choropleth maps(see “Classifying map information” inchapter 2), as well as the colors, symboltypes, and sizes.

Boilerplate maps, produced regularly toshow specific needs such as weeklyprecinct or division crime patterns, can befine tuned so they consistently convey the intended message. Problems are morelikely to arise with specialized maps. Achecklist may be a useful reminder of themost important map elements and criteria:

■ Need. Is a map needed for this mes-sage or analysis? Could the job bedone as well or better with anotherapproach, such as a table, a narrative,a chart, or conversation?

■ Data source. Are the data reliable? Ifthere are questions about data quality,how can the audience be alerted? (Byusing a map subtitle or map footnote?)

Are data so poor that mapping andanalysis should not be done? Whodecides if this is the case?

■ Scale. Is the appropriate area shown?Can the map be enlarged without compromising the message?

■ Scope. Is the map trying to show toomuch? Too little? Can more context beadded to better inform the reader?

■ Symbols. Would icons convey themessage more convincingly thanabstract default symbols? Note thatsome icons are awkward shapes andmay have a minimum size, belowwhich they become meaningless.

■ Color. Misused color detracts from anotherwise excellent map. Excessive useof bright colors may hurt the eye andrepel the reader. Illogical color grada-tions may be confusing. Think in termsof drawing the eye to important areas(normally higher data values) by usingmore intense color tones. Think abouthow the map will be used. Color maybe irrelevant if the map is to be distrib-uted by fax or printed in a documentthat will not be reproduced in color. In such cases, color can be counter-productive. Even black-and-white (gray-scale) shades should be chosencarefully if the document is to befaxed. Gray-scales should not be toosubtle (variations will be lost), andcross-hatched shading should becoarse (lines relatively far apart) orthey will not hold up through the faxprocess.

■ Lettering. Are the default font styleand size appropriate? Consider, for

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Rule of Thumb

Default settings in GIS programsshould not be accepted blindly.

example, whether the user may decideto make the map into an overheadtransparency. Will the lettering be legible in that medium?

■ Methodology. What opportunitiesdoes your software offer? (Hot spotidentification? Buffers? Filtering?)Have those opportunities been takenadvantage of? Or have unnecessarilyglitzy methods only created confusion?

■ Privacy. Will this map reveal informa-tion about individuals who may besubject to privacy restrictions? Mostdata are in the public domain, includ-ing arrest records and court docu-ments. Exceptions to this include thepractice of protecting the identities ofrape or sexual assault victims, and theidentities of juveniles.

Summary


■ The impact GIS has had on crime mapping.

■ How GIS is used in law enforcementagencies.

■ How GIS affects what we can do andhow we do it.

■ The difference between vector andraster formats.

■ What geocoding is.

■ How we can filter data and make use-ful maps according to specific criteria.

■ How GIS can be used to measure information on maps.

■ The meaning of derivative measuresand how they are created and used.

■ What hot spots are and how they aredefined, measured, and mapped.

■ What buffering is in the context of GIS.

■ How large databases can help withmapping and analysis.

■ What data warehousing and data mining are.

■ Some factors that demand the exerciseof caution in the mapping process.

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Notes

1. Data transformations using squareroots or logarithms smooth data andreduce problems associated with havingboth very large and very small values in a distribution The effect may change thedistribution to a more normal, or bell-shaped, curve.

2. The need for more integration of statisticaltools is gradually receiving more recogni-tion, and the problem should diminishover time. For example, a new packageknown as Regional Crime Analysis GIS(RCAGIS) developed for the Baltimore-Washington, D.C., region has an embed-ded statistical package, CRIMESTAT. Also, the S+ package by MathSoft®

(http://www.mathsoft.com/splus/) interfaces with ArcView.

3. Hot spots may be split by jurisdictionboundaries in such a way that they fail to meet hot spot criteria on either side ofthe line.

4. Reminder: Small-scale maps show largeareas, large-scale maps show small areas.

5. This problem of overlapping points canbe fixed with software manipulation.

6. Regions of all nations have FIPS codes.Greater London, U.K., for example, isUK17. For details, see: http://webcentral.bts.gov:80/itt/T%26T/resource/fipscode.pdf.

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■ Current events in crime mapping.

■ How to apply analytical crime mapping.

■ Criminal intelligence.

■ Crime prevention.

■ Courts and corrections.

■ Public information.

■ Resource allocation and planning.

■ Census geography and analysis.

■ Crime mapping applications and improved effectiveness.

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Chapter 5:Synthesis andApplications

This chapter brings together concepts discussed previously and gives an overview of crime mappingapplications in selected areas of criminal justice.

Synthesis 2000

What are the most obvious characteristics of crimemapping at the beginning of the new millennium?Several come to mind:

Technology imbalance—most police departments,except large ones (more than 100 sworn officers), do not use crime mapping technology.

Urban, suburban, and rural differences—in termsof crime analysis and mapping, the perspectives andneeds of urban, suburban, and rural police depart-ments greatly differ.

Incomplete geocoding—efforts to geocode ruraladdresses are under way.

Geographic information system (GIS)functions—GIS is used for descriptive, analytical,and interactive purposes.

GIS mapping—GIS is used to represent information inthe form of points, areas (polygons), and lines.

Synthesis and Applications

Decreasing costs—more capabilitiesbecome available as the cost of mappingtechnology declines.

Crossjurisdiction alliances—these relationships recognize that criminalbehavior pays little heed to departmentalboundaries.

Theory and practice—the link remainsweak.

Data sharing—collaboration among State and local agencies is becoming more common.

Context—the “backdrop” is increasinglybeing recognized as critical to understand-ing crime patterns and crime prevention.

Privacy issues—these issues becomemore urgent as the ability of law enforce-ment agencies (and others) to link infor-mation to addresses and individualsincreases.

Public access—access to geographicinformation increases with the use of the Web and systems such as ICAM(Information Collection for AutomatedMapping) in Chicago.

Let’s consider each point in turn.

Technology imbalance. Why are mostpolice departments not using computerizedcrime mapping? (Only 13 percent reportedusing the technology in the 1997–98 survey by Mamalian and La Vigne et al.,1999.) Part of the answer lies in the verydifferent needs and capabilities of urban,suburban, and rural law enforcementagencies. The needs and resources of large

urban departments make computer crimemapping practical. Crime rates, particularlyin so-called inner cities with larger policedepartments, are generally higher than insuburban or rural areas. A larger policeforce also means that tasks can be morespecialized and that the probability ofhaving a staff dedicated to crime analysisis greater. The political map is extremelyfragmented in some areas, such as LosAngeles and Chicago, with numeroussmall, incorporated communities outsidethe central city. While the total populationin such metropolitan areas may be largeand the crime problems significant, politi-cal fragmentation decreases the likelihoodthat police departments are, individually,able to deploy specialized crime analysisunits.

Urban, suburban, and rural differ-ences. Reasons for variations in the useof automated mapping go beyond the sizeof police departments and questions oflabor force specialization. Urban, suburban,and rural environments differ fundamen-tally in a number of ways that relate to thedistribution of crime (Mazerolle, Bellucci,and Gajewski, 1997). Among the obviousdifferences are those relating to populationdensity, racial and ethnic diversity, socialcohesion, and economic health. Higherpopulation density means more potentialfor crime in a given area. This does notnecessarily mean higher crime rates, how-ever. A rural county may have a higherpopulation-based crime rate comparedwith a city, but the city has more crimesdue to its having a larger population.

Urban law enforcement differs markedlyfrom that in rural areas, where communitiesand residences may be widely separated.

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Rural policing may be just as fragmentedas urban policing. Each small town mayhave its own police department with ahandful of officers, while the surroundingarea may be the responsibility of the sher-iff or State police. (To add to the confusion,in large cities sheriffs are not necessarilyinvolved in street-level criminal lawenforcement but may act as officers of the court, dealing with court orders, evictions, repossessions, and, perhaps, jail administration.)

Paradoxically, perhaps, response times tocalls for service (CFS) for the responsibleagency in a low-density rural area may be much longer than from its neighbor,depending on the locations of police facilities. Concepts like patrol that are the bedrock of urban and suburban lawenforcement mean little in an environ-ment like that illustrated in figure 5.1.Response time, too, must be thoughtabout differently. It is easy to overlooklaw enforcement environments that arenonmetropolitan, since the problems thatattract most attention are in urban locations,

as are the media. Furthermore, the litera-ture on GIS in policing is almost exclu-sively dedicated to urban case studies.Because the application of GIS to low-density suburbs and rural environments is a new frontier and has been extremelylimited, hard questions need to be asked:Does rural law enforcement really needcomputer mapping? Can GIS improve ruralpolicing?

Incomplete geocoding. Historically,geocoding efforts have had a distinctlyurban bias, for obvious reasons. Themajority of people lived in the cities, andthe census enshrined urban geocoding bylinking early address matching initiativesto the areas that we know today asMetropolitan Areas. Rural areas wereessentially impossible to geocode becausetheir rural route and post office (P.O.) boxaddresses inhibited the construction oflinks to the locations of housing units orbusinesses. Preparations for Census 2000include an aggressive effort to geocoderural addresses. In some cases, 911 streetaddresses are already available (typically

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Figure 5.1.

A photograph of a low-density environment inthe Oklahoma panhan-dle. In this rectangularland survey grid area,directions refer to milesand compass direc-tions. Privacy has adifferent meaning thanin high-density urbanareas. On a map repre-senting this low-density area, a dotcould easily be linkedto a specific household,in sharp contrast tohigh-density urbanenvironments.



in the rural parts of metropolitan coun-ties). In other cases, field workers go outand record the locations of structures andreconcile them to their rural route or P.O.box number, if possible. This effort isessentially a cooperative venture amonglocal governments, the U.S. CensusBureau and the U.S. Postal Service.1

GIS functions. The uses of GIS in policingcan be characterized as descriptive, ana-lytical, and interactive. Descriptive mapscan be equated with traditional cartogra-phy in that they tend to be a static pictureof information, albeit a very useful one, asexemplified by the styles shown in figures5.2, 5.3, and 5.4. Descriptive is not apejorative term—it is the foundation ofscientific investigation. Accurate descrip-tion is an extremely valuable commodityeasily capable of providing answers toquestions. Accurate and timely descriptionis the foundation on which GIS rests.

Analytical maps go a step further andconsider relationships among mapelements or use methods based on spatialstatistics software, such as STAC (Spatialand Temporal Analysis of Crime) or theArcView® Spatial Analyst. In interactivemapping, the analyst enters and changesmap parameters “on the fly,” perhaps test-ing theories about fluctuating drug-marketboundaries, studying the displacementeffects of a crackdown, or examining howbuffering features may affect outcomes.(For examples of analytical maps, see fig-ures 5.7 and 5.9. For an example of whatwas originally an interactive animatedmap, see figure 6.8.)

GIS mapping. The predominant applica-tions of GIS have involved the representa-tion of point, area (polygon), and linedata for reasons that have been outlinedpreviously. However, as databases becomemore integrated and newer technologies

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Figure 5.2

A map showing a burglary patternfor a specific time

period in a specificneighborhood inTempe, Arizona.

Source: RachelBoba and the

Tempe, Arizona,Police Department.


like global positioning systems (GPS)become more commonplace, we mightexpect the “traditional trio” of point, area,and line to be supplemented by an ever-richer array of raster data in the form ofdigital orthophotography and other photo-graphic images. The wide availability oforthophotography (often commissioned

by local governments) combined with theadvent of inexpensive digital cameras and(still somewhat expensive) digital videorecorders presages the fuller integration ofall georeferenced “visual” data. Most like-ly, traditional point, area, and line mapswill begin to be embedded with usefulraster elements, so that the user will have

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Figure 5.3

A map showing a scan of the robbery problem in specific neigh-borhoods in SanDiego, California.

Source: Julie Wartelland the San Diego,California, PoliceDepartment.Reproduced by permission.

Figure 5.4

A map representingthe regular crimereview process inCambridge,Massachusetts, withhousebreaks classi-fied according tovarious time criteria.

Source: Cambridge,Massachusetts,Police Department.Reproduced by permission.


the choice of seeing a map or bringing upan orthophoto and/or a ground-levelphoto of the location of interest. (Seechapter 6 for more on orthophotographyapplications.)

Other nontraditional applications include,for example, public safety events, inwhich police, fire, and other agencies arebrought together in a common effort toalleviate a natural disaster. A map con-structed for this purpose (figure 5.5)details data that provided generallocations of police and fire units during a catastrophic flood in Dallas, Texas.

Ever-decreasing costs. Decreasing costshave brought mapping technology withinthe reach of agencies that would not otherwise have been able to acquire it. In the early days of the Information Age,equipment and software often cost morethan the operator; now that situation isreversed. But the impact of less expensivetechnology goes deeper. Not only canagencies put computers on their workers’desks, but those computers are increasingly

powerful and thus provide additionalopportunities that slower or memory-challenged devices could not have accom-modated. For example, very large rasterfiles, unmanageable until recently, nolonger pose as much of an access problemas they once did.

Crossjurisdiction alliances. The beliefthat “all politics are local” has inhibitedcollaboration between police agencies. The mantra has been that local problemsshould be solved locally, although the useof State or Federal dollars was acceptableprovided not too many strings wereattached. Unfortunately, offenders havenot subscribed to the notion that theyshould recognize city, county, and Statelines.

The following inconsistencies among lawenforcement agencies have acted to makepolitical boundaries “law enforcementopaque” rather than transparent or invisible, as criminals perceive them:

■ Different radio frequencies.

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Figure 5.5

A map showing locational

information relating to a flood

in Dallas, Texas.

Source: MarkStallo and theDallas, Texas,


permission.

■ Different protocols for hot pursuits.

■ Agencies competing with one anotherin hot pursuits and investigations.

■ Lack of information sharing and coor-dination between administrative unitsand/or agencies (referred to as “linkageblindness” by Rossmo, 1995).

Mapping has helped break down such bar-riers due to its power to provide visual evi-dence that criminal groups or individualsmove across boundaries at will, sometimesintelligently exploiting the lack of commu-nication between agencies. The increasedinterlinking of databases needs to be cou-pled with jurisdictional interlinking so thatthe official view of crime more closely corresponds to regional reality. Politicallybounded areas, whether cities, counties, orStates, rarely conform to social areas. Untilpolicing areas more closely conform to orrecognize social areas, crime analysis andprevention will be stymied to some degree.Crime mapping has tended to focus ontechnical issues, but the bigger picture—Are we mapping and analyzing the mostappropriate areas?—also deserves atten-tion. Crime can be a regional phenome-non. Regional problems call for regional solutions.

Theory and practice. Crime mapping istypically based on experience and obser-vation, with a weak or nonexistent theoretical foundation. Empirical and the-oretical perspectives battle each other inthe here-and-now world of law enforce-ment. When tangible flesh-and-bloodproblems need to be taken care of immedi-ately, theory is at best abstract, at worstcompletely irrelevant. But theory doeshave a place as a conceptual backdrop—

a reference base that can provide consis-tency to our analyses and tactical respons-es. Eck (1997) pointed out that as thetheoretical content of maps increases, itbecomes easier to make sense of the data(see also Eck and Weisburd, 1995). Inother words, if a map shows only crimelocations, indepth explanation of patternis next to impossible. But if other ele-ments of context are included, such as thesocioeconomic environment or locations of drug markets or of abandoned housing,(any elements that theory would lead usto expect to be linked to the crime patternin some way), then understanding is likely to be enriched. For example, a CFSmap, such as in figure 5.6, may be sub-stantially more meaningful when the CFSare displayed along with housing codeviolations. “Negative” information show-ing where the anticipated link betweenconditions fails to apply may be just asuseful as “positive” information showingwhere the link does apply.

Data sharing. Local and State govern-ments are finding that data gathered byone agency might be valuable to others.This is not surprising, given the bafflingcomplexity of social problems, includingcrime. Social scientists have long recog-nized that social systems are in someways like natural ecosystems: changesomething and everything else changes to some degree. Land use patterns andchanges, demographics, transportationsystem configurations, the weather, andthe strength of social controls embeddedin the culture—these and many more fac-tors influence patterns of crime. It makessense to give crime analysts access to the rich variety of data they need to helpthem answer the widely varied questionsthat come their way.

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Gradually, and inconsistently in terms ofgeography, “community” approaches toproblem solving dictating collaborationand data sharing among agencies aregaining popularity.

Context. A relatively recent trend incrime analysis emerging from the commu-nity policing movement has been theincreasing tendency to see crime “in con-text” rather than as a self-contained phe-nomenon with self-contained solutions. A broad view is needed, one that incorpo-rates understanding of social backgroundand crime patterns. Complete preoccupa-tion with patterns and their immediatecauses is a kind of professional shortsight-edness that may miss fundamental causalprocesses (see Sherman et al., 1998, formore on the broad view of crime preven-tion). Crime analysis that ignores thiswider perspective, or backdrop, runs therisk of ignoring important connections tocommunity-based processes. This againreinforces the importance of making a

variety of data available to police officersand crime analysts alike.

Privacy issues. Privacy questions aretouched on elsewhere in this guide, butthey also deserve a mention in any dis-cussion of synthesis. In marked contrastto the cultures of many other developednations, U.S. culture is quite resistant togovernment intrusion, even when thatintrusion seems to be well intentioned and in the apparent best interest of themajority. Along with this goes a degree of mistrust of law enforcement, a posturereinforced by high-profile events such asthe Rodney King affair in Los Angeles,the Abner Louima incident in New YorkCity, and the Branch Davidian standoff inWaco, Texas. Fears of privacy violationsby law enforcement are only part ofbroader societal concerns about privacyand the inherent mistrust of authority.

These concerns were exemplified by thecontroversy surrounding the activities of

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Figure 5.6

A map showing substandard housingand calls for servicein the Grier Heights

neighborhood,Charlotte-

Mecklenberg, NorthCarolina.

Source: ElizabethGroff and Charlotte-

Mecklenberg, North Carolina,


permission.

Image Data, LLC, a small New Hampshirecompany that in 1999 attempted to builda national database of driver’s licensephotographs, ostensibly to combat checkand credit card fraud. After Colorado,Florida, and South Carolina had contractedto supply their license pictures, it was dis-covered that the photo file was also to beused to combat immigration abuses, ter-rorism, and other identity-related crimes.It then became known that Congress hadcontributed $1.5 million to the effort andthat the U.S. Secret Service had lent ahand, elevating the controversy to newheights and provoking initiatives from the three States to cancel their contracts(O’Harrow and Leyden, 1999; for a moredetailed analysis, see Paisner, 1999).

This type of data gathering is one ofmany ways in which data relating to indi-vidual persons, families, or householdscan be accessed and compiled. The use ofbranded credit cards and discount cards in stores enables retailers and marketingcompanies to track every purchase anddevelop spending profiles of consumers.When information from this kind of data-base is added to information that can beaccessed via directories, maps, remotelysensed imagery (e.g., aerial photos, satel-lite images), and various and progressive-ly more integrated public records, anxietyabout privacy violations, real or imagined,understandably escalates.

Crime mapping can be seen as a piece ofthis privacy issue, although whether oneelects to put a sinister spin on the tech-nology is obviously a personal decision. As yet, no conspicuous backlash againstcrime mapping has occurred (with thepossible exception of Megan’s Law), but

whether this type of public reaction willoccur in the future is an open question.

Public access. Conflict over privacy andaccess is inevitable. For every person orgroup that lobbies to restrict the dissemi-nation of crime-related information,another lobbies to gain more access.Surprisingly, access to crime data is stilldenied to researchers on occasion.Denying bona fide researchers access forprojects that are ostensibly in the publicinterest clouds confidence in the data-gathering process, since it inevitably rais-es questions about the reasons for denialof access.2 As noted elsewhere, it wouldseem that most crime data eventually arereleased to the public through court pro-ceedings and so on. What could be dam-aging and should obviously be avoided ismaking unfounded data accessible to thepublic (e.g., on the Web), since it carriesthe accompanying risk of defaming theinnocent.

A controversial development in recentyears has been the tendency of someStates to put lists of sex offenders on theWeb, Virginia being the 10th State to doso in late 1998. The names of more than4,600 violent sex offenders were madeavailable to the public, with the effect of“giving parents a new tool to protect theirchildren but also renewing fears about theerosion of privacy in the Information Age”(Timberg, 1998).

The names, ages, home addresses, conviction records, and photographs ofVirginia offenders are listed athttp://www.vsp.state.va.us, a Web sitethat recorded more than 650,000 visitorsin less than 2 months after it was

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launched. Predictably, State officials wereenthusiastic about the site, while civil lib-ertarians suggested that it amounted todouble punishment, with the additionalpossibility that offenders’ children wouldbe put in jeopardy. Other potential risksinclude the possibility of disseminatingfalse information (from computer hackersor errors in the original files) and vigilan-tism. To date, registries have not beenevaluated adequately, but they are greetedwith enthusiasm by parents anxious toprotect their children.

Many law enforcement agencies havedeveloped Web sites displaying crimemaps (see appendix and various examplesthroughout this guide). Some elect toshow crimes in the form of point data,while others use choropleth or shademaps. Sharp differences of opinion havebeen voiced over the appropriateness ofsuch maps. Opponents fear negative con-sequences for high-crime communities;under the worst case scenario, crime maps may provoke full-scale flight fromneighborhoods. Proponents tend to becommunity-policing advocates who seefull disclosure of information as a tool incrime prevention. They see Web-basedmaps as community resources providingcitizens with unbiased, ready access tocrime patterns. If citizens can see wherecrime is, the argument goes, they will bebetter collaborators in crime prevention.

Mapping applicationsfor the millennium

Crime mapping can be applied to the following criminal justice areas: criminalintelligence, crime prevention, courts and

corrections, public information, andresource allocation and planning.

Criminal intelligence

Building criminal cases can be a painstak-ing process involving the compilation of information from diverse sources. Allthe data have a geographic dimension,although the importance of the locationalinformation varies from vital to unimpor-tant. Spatial data may provide answers to obvious questions about the crime scene,such as, Where is it?, but they also pro-vide information for less obvious ques-tions, such as, Where is this crime scenein relation to other relevant persons orobjects? What locational information canbe derived from victims, offenders, and witnesses? Can the geographic data becodified in the form of a map, and, if so,would such a map be useful to investiga-tors? Investigators might map linksbetween a suspect and his or her associ-ates’ home addresses to identify wherethe suspect’s daily or weekly activitiestake place—the activity space. Anotherpossibility would involve mapping ganggraffiti as a way of defining gang territory.

Geographic analysis may be helpful onradically differing scales. For example,very large scale, or high-resolution, GIScould help with representing spatial dataat crime scenes, such as the arrangementof objects in various rooms in a house orthe pattern of crime in a highrise buildingin three dimensions (for more on this, seechapter 6). Other data may be better rep-resented on a small scale—for example,facts relating to suspect movements in other cities or states. Depending on the

136 Chapter 5

importance of spatial information, a mini-atlas could be compiled, bringing togetherall the known geographic information andputting it in the context of other data.

Moland (1998) describes a case study incrime mapping in which a “cellular phonetrail” was left by one suspect, providingan opportunity to plot spatial and temporal paths of two suspects. Detectivesworked with telephone company techni-cians to verify that various antenna locations were working and to establishvarious aspects of cell phone signal pat-terns. One of the key pieces of evidencepresented in court was figure 5.7, whichshows cell phone antenna locations asso-ciated with the suspects’ activities. Thisexample illustrates not only the use ofmapping to develop criminal intelligence,but also the use of mapping to developcourtroom evidence. The two men ulti-mately were convicted and sentenced tolife in prison for a gruesome murder. Mapsin the courtroom helped the prosecutionwalk the jury through a complex set of

events by illustrating the collateral spatialrelationships.

The information shown in figure 5.8 couldalso be used to document a pattern show-ing where and when incidents occurredbased on the times of the related calls forservice, as well as to provide informationto corroborate information from witnesses.

As with any new technology, the risk ofoverselling GIS may lead to unrealisticexpectations and dashed hopes. The bestcase scenario would include GIS as a use-ful contributor to the investigative process(see also the section titled, “Geographicprofiling,” in chapter 6).

Crime prevention

In a publication titled Preventing Crime:What Works, What Doesn’t, What’sPromising, Sherman et al. (1998) document “what works” for 11 crime problems. They identify approximately 30 actions or conditions as “promising.”

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Figure 5.7

A map showing keytelephone antennalocations delineatingtwo suspects’ activity.

Source: Robert S. Moland, St.Petersburg, Florida,Police Department.Originally publishedin Moland, 1998,figure 3. Reproducedby permission.


What is interesting about these lists is thatmany of the actions had little or nothingto do with policing. Many were amenableto the application of mapping tools,whether they were related to crime orother phenomena. This finding under-scores that the label “crime mapping” ismuch too narrow a designation for whatpolice departments do.

While some crime prevention actions werein the domain of schools or social serviceagencies, such as frequent visits by nursesto the homes of mothers and infants atrisk, some were more directly connectedto policing and had locational componentsthat lent themselves to the application ofgeographic tools:

■ Nuisance abatement action onlandlords of rental housingplagued by drug dealing. Where arethe landlords? Where are the subjectproperties? What properties does a cer-tain landlord own across a city? Oncemapped, how can nuisance abatementaction be planned to make the best useof both time and space resources? Cana network analysis program be used tooptimize visits to properties?

■ Extra police patrols for high-crimehot spots. Where are the hot spots?How can patrol resources be optimizedso that the patrol presence is propor-tional to the severity of crime in thehot spot? (Hot spots could be evaluat-ed in terms of responses that allocate a

138 Chapter 5

Figure 5.8

A map showing a pattern ofcriminal damage to property,

Overland Park, Kansas.

Source: Gerald G. Tallman,Manager, Crime Analysis

Unit, Overland Park,Kansas, Police Department.Reproduced by permission.

fair share of the resources available ona given shift.)

■ Monitoring of high-risk repeatoffenders by specialized policepatrols. Where do released repeatoffenders live? What does this patternlook like? Can repeat offenders be cate-gorized or prioritized by the degree ofrisk they present, including the risk ofrepeating and the potential severity ofthe crimes they are likely to commitbased on their histories?

■ On-scene arrests for domesticabusers who are employed or wholive in areas where most house-holds have an employed adult.Where are the salient locations? Whatare the home and work addresses ofoffenders? Can information on the current whereabouts of offenders andother locational data be maintained ina periodically updated registry?

■ Therapeutic community treatmentprograms for drug-using offendersin prison. This action recommendationreflects the work of Harris, Huenke,and O’Connell (1998). They used map-ping to increase released offenders’access to services in Delaware, plottinglocations of substance abuse, mentalhealth, employment, and other socialservices, and relating them to the mostrecent addresses of probationers orparolees.

An example of a crime prevention pro-gram using geographic data in creativeways is the autodialer system employedby the Baltimore County Police Departmentin Maryland (Canter, 1997, 1998).

Analysts subjectively identify clusters ofincidents that have similar modus operan-di and occur relatively close to one anoth-er (figure 5.9). Then a GIS is used first torandomly select telephone numbers in theZIP Code area coinciding with the areaunder analysis, and second to further nar-row down the numbers to the general areaof the crime hot spot. The autodialer thencalls all the telephone numbers on the listwith a message from the police depart-ment relaying precautions to take andasking for reports of suspicious behavior.Experience shows that because some dis-placement of crime seems to occur, targetpolygons or areas must be expanded totake this displacement into account.Evaluation of the system suggests that itinfluences the actions of the community,the offender, and the police. Communityawareness appears to heighten (911 callsreporting suspicious persons and vehiclesincrease significantly), and crime appearsreduced, in spite of the moderate displace-ment effect.

Courts and corrections

Crime mapping can be applied to the following courts and corrections areas.

■ Probation officer service areas.Caseload distributions can be managedby looking at the geographic locationof probationers and parolees, and thenusing a mapping program, such as theArcView Network Analyst, to deter-mine the most efficient route forsequencing visits to homes, should vis-its be part of the designated probationofficer’s duties. If visits to individualshave to be made at specific times, aroute can be designed to minimize time

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spent traveling from one address to thenext. If time is not a factor, a route tominimize overall travel time can bedetermined (figure 5.10).

With the development of communityprobation, officers are now more likelyto be assigned to small communityareas so that they have closer contactwith probationers and communityresources, including police. A program,such as MapInfo® Redistricter, could beused to draw (and periodically redraw)districts based on the distribution ofcaseloads. Similarly, districts could bedrawn to equalize workloads for serv-ing warrants. Other applications in the

probation field could include mappingdangerous areas where probation officers should be accompanied by apolice officer or mapping areas thatprobationers are to avoid (figure 5.11).

■ Prison location analysis. Prisonbuilding, a booming industry in recentdecades, frequently runs into theNIMBY (not in my back yard) problem.Almost everyone agrees that prisonshave to be built, but few communities(with the exception of rural locationsdesperate for jobs) want them nextdoor. Prison location analysis is a spe-cialized application of GIS. Locationmodeling has actually been in use

140 Chapter 5

Figure 5.9

A map showing aresidential burglary

target area beforeand after autodialing.

Source: Philip Canter,Baltimore County,

Maryland, Police Department.


for some time. The basic approachinvolves mapping vacant and redevel-opable land as possible sites for build-ing, then adding layers for exclusions(due to factors such as terrain, prohib-ited land uses, lack of utilities, and lackof adequate transportation links) toidentify the sites with the greatestpotential for development. These sitescan then be reviewed and prioritizedwith further map analysis or fieldinvestigation.

Another locational criterion could bethe site’s distance from communitiessupplying the majority of inmates.Prisons far from their feeder communi-ties make visitation difficult and mayfragment families. Minimizing traveltime may be a consideration not onlyfor families, but also for the depart-ment of corrections and the court sys-tem, because transporting prisoners to and from remote court offices will increase travel time and cost. Theaccessibility of a prison to a courthouse

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Figure 5.10

A map showing aroute designed tominimize travel timebetween points determined by theArcView® NetworkAnalyst mapping program. The pro-gram can also printstreet directions.


Figure 5.11

A map showing community probationdistricts mapped toequalize workloadsusing MapInfo®

Redistricter.



can be evaluated using ArcViewNetwork Analyst (figure 5.12).

■ Probationer and parolee locations.The mapping of home addresses ofparolees and probationers, along withmodus operandi data, enable lawenforcement to quickly identify localparolees as potential perpetrators ofparticular crimes occurring in a neigh-borhood. This type of analysis is anintegral part of the increasingly popularComStat (computerized statistics)process that originated with the NewYork City Police Department, in whichprecinct commanders are quizzed regularly about crime patterns in theirareas. A big screen monitor linked to a GIS can immediately show paroleelocations in conjunction with offenselocations (see chapter 3). Recidivismcould also be mapped to identify hotspots and mobilize resources necessaryto deal with the issue. Parole and pro-bation agencies are rich in locationalinformation that can be used to man-age caseloads, including sex offenderregistries (figures 5.13 and 5.14).

■ Halfway house locations. Much likeprisons or group homes for mentallydisabled people, halfway houses pro-voke NIMBY reactions. Mapping demo-graphics and housing types may helpwith site selection. In an example citedby Westerfeld (1999), a Baltimorecommunity needed to be persuadedthat halfway houses were appropriatein its neighborhoods. The strategy thatwas developed involved geocoding thehome locations of all Maryland prisoninmates originally from the communityin question. Maps then showed whereeach inmate had lived. During commu-nity presentations, it became increas-ingly difficult for residents to opposereceiving people who originally residedin their neighborhoods.

However, publicity about the high num-ber of escapes from halfway houses insome jurisdictions compounds the diffi-culty of locating such facilities. Onesolution is to seek a zoning variance to permit residential facilities in whatare otherwise industrial or commercialareas. Presumably, this is a last resort.

142 Chapter 5

Figure 5.12

A map showingaccessibility to a

central point. Three zones, each

representing a specific travel timeare mapped usingArcView® Network

Analyst.


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Chapter 5

Figure 5.13

An offender profile anda draft map of offendersin various categories.(Details intentionallyobscured.)

Source: Eric Kim andthe Wisconsin StateDepartment ofCorrections. Reproduced by permission.

Figure 5.14

A draft map ofbuffers around thelocations of sex registrants.

Source: Eric Kimand the WisconsinState Department of Corrections.Reproduced by permission.


A cynical solution suggests that poor,dysfunctional communities are likely to be incapable of mobilizing themselvespolitically so as to make the locating of halfway houses relatively free ofopposition.

■ Offender access to services. ADelaware study (Harris, Huenke, andO’Connell, 1998) analyzed the avail-ability of rehabilitative services forreleased offenders, looking at the spa-tial relationship between former inmateaddresses, substance abuse treatmentfacilities, social service centers, mentalhealth services, and unemploymentoffices. Nearly half of all released pris-oners in Delaware return to prison within3 years, leading to the conclusion thatpostprison rehabilitation services areinadequate, or at least inaccessible.Maps were used to justify the imple-mentation of drug rehabilitation services in Kent County and the city of Dover.

■ Sentence mapping. A mapping appli-cation used occasionally by court sys-tems deals with the need to ensure fairsentencing. Although judicial discretionhas been reduced by mandatory sen-tencing, it has not been eliminated(and never will be, of course), andsubstantial disparity remains in thesentencing of similar people for similarcrimes. Like police agencies, courtsin both the State and Federal systemshave territories. The mapping of sen-tences is more technically challengingthan mapping crime incident data.However, it can be done using, forexample, a weighting system thatassigns higher weights to more severesentences, making sure that analysis

of offender characteristics and crimetypes is properly controlled for. (Fordetails on this concept and examples ofgeographic analyses of sentencing, seeHarries and Lura, 1974, and Harriesand Brunn, 1978. For a discussion ofgeographic issues specifically relatingto capital sentencing, see Harries andCheatwood, 1997.)

■ Courtroom presentation. As noted in the discussion of figure 5.7, mapsshowing sequences of events and pat-terns can be used in court to providean easily comprehended visual rendi-tion of a criminal process. Such presen-tations may be constructed on anyscale, from international drug traffick-ing to the layout of a murder scene inone room of a building. In figure 5.15,the information on a sequence ofevents involving sexual torture andhostage taking was mapped to providethe prosecution with a plausiblechronology of events that could bereadily communicated to judge andjury.

Public information

“Public information” is a phrase thatevokes mixed emotions in police depart-ments. Some make vast amounts of datareadily accessible to the public throughthe Web, while others are reluctant tohave a Web site at all, let alone includecrime data on it. At the risk of stereotyp-ing, it could be suggested that the moredefensive posture tends to reflect “oldschool” thinking, whereas willingness toshare data represents a more contemporary,“let the light shine in” approach, consis-tent with community policing values.

144 Chapter 5

Even the most avid proponents of dataaccess would concede that cases withongoing investigations should be exemptfor obvious reasons. However, beyondactive cases and cases involving rape vic-tims and juveniles, active concealment isdifficult to justify. From a public policyperspective, one of the strongest justifica-tions for open data access is that it makesputting a “political spin” on things some-what more difficult. Every agency, atevery level of government, wants to makeitself look good, and to this end informa-tion is power. Limit information dissemi-nation and the power of the opposition iscrimped.

Some public constituencies also prefer tosuppress crime data, and particularly crimemapping data, from public view for fearthat their investments will be negativelyaffected. Such considerations seem of particular concern to realtors and some

property owner’s associations. The ration-ale is that any publicity about crime tendsto depress property values. But what isoverlooked is that publicity about crimemay draw more attention to the problemwith the possibility of remedial action. Inthis context, the possibility of long-termgain is sacrificed for short-term expediency.

The more prevalent perspective today isthat an informed public can best assistlaw enforcement, given that police cannotbe in all places at all times, and that polic-ing will be most effective when it is per-formed in an environment in which thepublic offers active support. Clearly, mapsare tools that can assist in this effortthrough their use at community meetingsand their distribution to the media, citizen patrols, and neighborhood groups.However, maps must be tailored to eachgroup, and a poorly designed or poorlypresented map may do more harm than

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Chapter 5

Figure 5.15

An example of a mapused in court inOverland Park, Kansas,to substantiate thetimes the victim report-ed she was in certainplaces. The victim’saddress and otherdetails have been modified or erased toprotect the privacy of the victim in this case.

Source: SusanWernicke, OverlandPark, Kansas, Police Department.Adapted by permission.


good by creating confusion where nonemay have existed before.

Internet media offer public informationopportunities that go beyond merely hav-ing a Web site or providing maps. TheWeb also offers opportunities for feedbackfrom the public via survey forms thatsolicit information about specific crimes orproblem areas and neighborhood-specificbulletins. A review of police departmentWeb sites linked through the CrimeMapping Research Center Web site (seeappendix) demonstrates some of themany possibilities.

Resource allocationand planning

As noted in chapter 3, the fundamentalprinciple behind resource allocation is theefficient assignment of valuable resources.In theory, at least, more resources shouldbe assigned to areas where crime is mostprevalent. It was suggested in chapter 3that calls for service be used as either acrude or weighted index to help determinethe allocation of patrol resources on a per-shift basis. A choropleth map of neighbor-hoods classified by their levels of CFScould be the tool used for planningresource allocation.

In a community policing context, thisapproach can be expanded by looking notonly at the calls for service themselves,but at the local social conditions associat-ed with high levels of CFS. This not onlyforms a foundation for planning direct lawenforcement strategies, but also sets thescene for cooperation with other criminaljustice system agencies and social serviceagencies. An example of this approach,

applied in Cincinnati, is shown in figure5.16. The choropleth map of calls forservice by neighborhood and analysis ofsocial indicators showed that the preva-lence of nonowner-occupied housing wasthe strongest social correlate of CFS levels.Models that relate social conditions to levels of crime or CFS take many differentforms, and examples can be found in theliterature on the social ecology and geog-raphy of crime (Felson, 1998; Byrne andSampson, 1986; Harries and Powell,1994; Harries, 1995; Taub, Taylor, andDunham, 1984; Bursik and Grasmick,1993; and Rose and McClain, 1990).

Crime analysis and the census

Postings on the Crime Mapping ResearchCenter’s listserv indicate that the manipu-lation of census data is a confusing obstaclefor many analysts. This is not surprising,since census data and census geographyare somewhat complex, or can be,depending on what you are trying to do.Among the most problematic areas arelinking census geographies to data andmisunderstandings relating to what canreasonably be, and not be, done with specific census “counts” and variables.

For example, what measure of incomeshould be used? The census contains dataon family income and household income.Families are subsets of households, sothese data for families exclude single persons living alone or unrelated personsliving together (who constitute house-holds). Family income will be higher thanhousehold income because the averagefamily has more wage earners than the average household. Normally, because

146 Chapter 5

it is more inclusive, the preferred measureis household income.

A second question arises: Is it best to usemedian income or mean income? Themean has the advantage of being able tobe manipulated mathematically, but in askewed distribution, such as is found withincome, the mean may be “pulled” to the right by the very high values of theextremely rich (the so-called Bill GatesEffect!). For such skewed distributions,the median is usually preferred since itgives a more accurate description.

Detailed treatment of these questions isbeyond the scope of this guide. An excel-lent review of issues in census geographyand the analysis of change using censusdata can be found in Myers (1992).

Conclusion

Mapping criminal justice elements is welladvanced, although unevenly distributedamong police agencies. Most applicationsare horizontal, which is to say within thevarious levels of the criminal justice sys-tem. The real challenge is to integratemapping applications vertically, so thatagencies can be linked according to specif-ic problems and be regionally integrated.This is a tall order given the political insu-larity of agencies and turf and controlissues. The perfect world of analysis andmapping might look something like table5.1. Ideally, mapping applications wouldbe integrated to permit automated multi-step analyses. Quite apart from politicalimpediments to data integration, technicalobstacles can make integrated analysis

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Figure 5.16

(Top) A map showingcalls for service in1997 per 1,000 peopleby neighborhood inCincinnati, Ohio.(Bottom) A mapshowing nonowner-occupied housing,which can be used asan index of possiblesocial disorganization.

Sources: Shape filesand analysis by A. Ball, COPS Regional Institute, and M. Neumann,Cincinnati, Ohio,Police Department.Adapted by permission.


quite cumbersome because many spatialjoins, which link databases according totheir geography, are needed to indicatehow records relate to geographic areas.

Summary


■ The characteristics of crime mappingfor the new millennium.

■ How crime mapping can be applied to:

■ Criminal intelligence.

■ Crime prevention.

■ Courts and corrections.

■ Public information.

■ Resource allocation and planning.

■ Where to obtain a better understandingof census geography and analysis.

■ How crime mapping applications could be broadened to improve theireffectiveness.

Notes

1. Personal communication from MichaelRatcliffe, U.S. Census Bureau, PopulationDivision, January 25, 1999.

2. While most law enforcement-academiccollaborations are amicable and produc-tive, this is not always the case owing tothe vagaries of human nature that occa-sionally leave either or both parties feelingused and abused. As in other relation-ships, the key to success seems to be theunsurprising revelation that all partiesshould be sensitive to the others andavoid exploitive actions.

148 Chapter 5

Table 5.1. Horizontal and vertical linkages in analysis and mapping among criminal justice entities

Agencies Current Mode Ideal Mode + Region

Law Independent enforcement analysis

Courts Independent analysis

Corrections Independent analysis


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Chapter 5


■ Types of changes that may be expected in the next decade.

■ Geographic profiling.

■ High-resolution GIS.

■ The use of complex statistical methods in spatial forecasting.

■ Aerial photography.

■ How various kinds of data visualization can be integrated productively.

■ GIS and global positioning systems (GPS).

■ The possibility of limits to data access.

■ Applications of multimedia and integration technologies.

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Chapter 6

Chapter 6:Crime MappingFutures

Applications in crime mapping are becoming increas-ingly sophisticated and integrated. The hallmark of thefirst decade or so of the modern era of crime mappingwas the use of geographic information systems (GIS).Perhaps the next decade will see the integration ofpreviously separate technologies such as global posi-tioning systems (GPS), orthophotography, digital pho-tography, digital videography, and a wide range oflocal databases with relevance to policing—and theWorld Wide Web. Another realm of potential progresslies in forecasting. A remarkable indication of the levelof interest in crime mapping and related technologieswas contained in President Clinton’s State of theUnion Address on January 19, 1999, in which he said:

Tonight, I propose a 21st century Crime Bill todeploy the latest technologies and tactics . . . [to] put up to 50,000 more police on the street in the areas hardest hit by crime, and then toequip them with new tools—from crime-mapping computers to digital mug shots.[Emphasis added.]

While it is not possible to explain or even identify allthe innovative crime mapping applications in the fieldor under development, the following are outlined belowto provide a sense of the kinds of methods and tech-nologies that are likely to find increasing acceptance

Crime Mapping Futures

in the years ahead: geographic profiling,which has already found wide recognitionand is representative of a method that cre-atively combines various tools of spatialanalysis; high-resolution GIS; statisticalmethods and forecasting; digital aerialphotography; and the integration of GISand GPS.

Geographic profiling1

Geographic profiling is an investigativemethodology that uses the locations of a connected series of crimes to determinethe most probable area that an offenderlives in. Although it is generally applied in serial murder, rape, arson, robbery, andbombing cases, geographic profiling alsocan be used in single crimes that involvemultiple scenes or other significant geo-graphic characteristics.

Developed from research conducted atSimon Fraser University’s School ofCriminology and rooted in the pathbreakingwork of Brantingham and Brantingham(1981), the methodology is based on amodel that describes the hunting behaviorof the offender. The criminal geographictargeting (CGT) program uses overlappingdistance-decay functions centered on each crime location to produce jeopardysurfaces—three-dimensional probability surfaces that indicate the area where the offender probably lives. The distance-decay concept (see chapter 1) conveys theidea that people, including criminals, gen-erally take more short trips and fewer longtrips in the course of their daily lives, whichmay include criminal activities. Thus overlapping distance-decay functions aresets of curves expressing this phenome-non and suggesting, for example, that it

is more likely that offenders live close to the sites of their crimes than far away.Probability surfaces can be displayed onboth two- and three-dimensional colorisopleth maps, which then provide a focusfor investigative efforts (see chapter 1 for adescription of isopleth maps).

This research has led to the developmentof Rigel, a computerized geographic profil-ing workstation that incorporates an ana-lytic engine, GIS capability, databasemanagement, and powerful visualizationtools. Crime locations, which are brokendown by type (e.g., victim encounter,murder, and body dump sites for a murder),are entered by address, latitude/longitude,or digitization. Scenarios wherein crimelocations are weighted based on certaintheoretical and methodological principlesare created next and examined. Theaddresses of suspects can then be evaluat-ed according to their “hit” percentage on a probability chart known as a z-scorehistogram, which can prioritize registeredsex offenders, other known criminals, taskforce tips, and other information containedin databases.

Geographic profiling can be used as thebasis for several investigative strategies.Some of the more common ones include:

■ Suspect and tip prioritization.

■ Address-based searches of policerecord systems.

■ Patrol saturation and surveillance.

■ Canvasses and searches.

■ Mass DNA screening prioritization.

152 Chapter 6

■ Department of motor vehicles searches.

■ ZIP Code prioritization.

■ Information request mailouts.

Figure 6.1 displays the geoprofile producedfrom the analysis of 32 armed robberiesthat occurred over a period of approximately12 months in the city of Vancouver,British Columbia, Canada. The purpleareas around the periphery are less likelyto include offenders’ residences, and theyellow and orange areas in the center are more likely to include offenders’ resi-dences. Three strategies were predicatedon the geographic profile in this case.First, a search was conducted of theVancouver Police Department’s RecordsManagement System for known robberyoffenders who matched the criminals’descriptions and resided within the top 5percent of geographic areas identified inthe geoprofile. This did not produce anyviable matches, as neither offender hadhad a previous conviction for robbery.

Second, a simplified geoprofile, displayingonly the top 2 percent (0.7 square miles)of potential offender residences, was pro-duced for patrol officers. Previous researchdetermined that robbery offenders usuallyreturn home after committing a crime. Itwas therefore suggested that, in additionto responding to a crime scene after thereport of a new robbery, patrol membersshould also search the most likely areaof offender residence, paying particularattention to logical routes of travel. Thisalso was unsuccessful as the offenderswere using stolen cars and no reliablevehicle descriptions were ever obtained(even though the geoprofile was used bypolice units to search for stolen automo-biles that might be abandoned prior to anew robbery).

Third, the results of the geoprofilewere released on the television show“CrimeStoppers.” This approach was suc-cessful, producing results that allowed thedetectives to identify the offenders respon-sible for the series of robberies. The primary

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Chapter 6

Figure 6.1

A geoprofile of a series of armed

robberies inVancouver, BritishColumbia, Canada.

Source: Det. Insp. D. Kim Rossmo,

Vancouver, British Columbia,


permission.


offender’s address was located within the top 1 percent of the peak area of the geoprofile.

It is important to stress that geographicprofiling does not solve cases, but ratherprovides a means for managing the largevolume of information typically generatedin major crime investigations. It should beregarded as one of several powerful deci-sion support tools available to the detec-tive and is best employed in conjunctionwith other police methods. Geographiccrime patterns are clues that, when prop-erly decoded, can be used to point in thedirection of the offender.2

Currently, geographic profiling services are available from the Vancouver PoliceDepartment, Geographic Profiling Section,Vancouver, British Columbia, Canada; theRoyal Canadian Mounted Police, PacificRegion ViCLAS Section, also in Vancouver;and the Ontario Provincial Police,Behavioral Sciences Section, Orillia,

Ontario, Canada. It will be available in the near future from the British NationalCrime Faculty, Bramshill, United Kingdom.

Concerned about the mapping and predic-tion of serial crimes, Geggie (1998)reported on the work of Officer TimothyMeicher of the Los Angeles PoliceDepartment. The case related to robberiesinvolving a perpetrator who rode a motor-cycle and snatched purses from elderlyvictims in shopping center parking lots—hence the title “the Los AngelesMotorcycle Bandit.” Employing basic statistical concepts—mean and standarddeviation—Meicher produced a map withboundaries indicating two probability levels for the next bandit strike, one at 68 percent and the other at 95 percent(figure 6.2). Meicher then collaboratedwith Geggie to automate the process ofproducing the probability map. Theirmodel was subsequently distributed bothinside and outside the Los Angeles PoliceDepartment.

154 Chapter 6

Figure 6.2

A map showingprobability bound-

aries in the Los Angeles,

California,Motorcycle Bandit

case.

Source: Geggie,1998. Reproduced

by permission.

High-resolution GIS

We often lose sight of the fact that GIScan be useful on any geographic scale,from global (small scale) to small (largescale), such as a room. Most crime analy-sis is conducted on what could be labeledmedium scale, typically representing a cityor neighborhoods within a city. Rengert,Mattson, and Henderson (1998) havereported on GIS applied to individualbuildings or other small areas, such asstreet segments, terming this approachhigh-resolution GIS. The four panels offigure 6.3 contain several views represen-tative of this approach. In the upper leftpanel, a “plan” view shows the footprintof a highrise building on the TempleUniversity campus in Philadelphia. Crimeincidents have been compressed so thatthey are all seen as if at one level. Thiscompression enables law enforcement todetermine whether incidents might be

clustered around elevator shafts, forexample, or restrooms, which tend to beat identical locations on each floor of ahighrise. The upper right panel provides aperspective of the building with incidentlocations in their three-dimensional posi-tions. At lower left, a technique for delin-eating clusters uses spheres to provide asense of what might be called “highrisehot spots.” Then at lower right, a clusterwithin a cluster defines the limits of thelarger pattern of incidents and then focus-es on the denser pattern within.

Forecasting: Complexstatistical methods andcrime mapping

In cooperation with the Pittsburgh PoliceDepartment, Olligschlaeger (1997) employedadvanced statistical methods in an attemptto identify emerging drug markets, which

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Chapter 6

Figure 6.3

A map using high-resolution GIS toshow crime in ahighrise building.

Source: Rengert,Mattson, andHenderson, 1998.Reproduced by permission.


are relatively difficult to detect by conven-tional means. This is because they oftenare revealed only indirectly through thecommission of other crimes, such as rob-beries or burglaries, and then only after adelay. Given the importance of drug mar-kets as crime generators, early warning is useful. Three types of calls for service(CFS) were used to develop models:weapon-related, robbery, and assault.Commercial land use and seasonalitywere also included as model criteria basedon evidence in the literature. A grid ofcells measuring 2,150 square feet wasthen superimposed on the city, with thesize of the cells determined by the need tohave cells big enough to represent a rea-sonable number of CFS, but small enoughto supply an adequate number to satisfystatistical modeling requirements. The gridswere then used as the framework for choro-pleth maps.

Following rules based on gaming simula-tion, a statistical model estimated the con-sequences of the various types of CFS at

certain levels. (For details of the model’sarchitecture and specifications, seeOlligschlaeger, 1997). Then forecasts from different methods were comparedwith actual patterns to provide a basis forevaluation.

Experience showed that a type of gamingsimulation model known as the neuralmodel did better than other types tested.A difficulty with the analysis—namely, the use of large quantities of computerresources—becomes less of a problem asthe power of personal computer processorsincreases. Overall, the analysis suggestedthat advanced methods of the type test-ed—the neural model—could be usefultools in spatial forecasting.

Mapping change: From pins to grids

Also employing a grid mode of representa-tion for Pittsburgh, Gorr used 750-footcells approximating four city blocks. Basedon a pin map, a grid map (figure 6.4)

156 Chapter 6

Figure 6.4

A map of gridcells. Reproduced

by permission.

Source: WilpenGorr, Heinz

School of PublicPolicy and

Management,Carnegie Mellon

University,Pittsburgh,

Pennsylvania.Reproduced by

permission.

displays the Part I crimes from the pinmap in each grid cell, suppressing cellswith only one crime. The grids show thetotal demand for serious crime suppres-sion, in combination with an indicator ofcrime-prone land uses. The latter indicator came from the PhoneDisc®3 yellow pagesand includes the total number of restau-rants, fast-food stands, bars, drug stores,retail stores, pawn shops, jewelry stores,etc., by grid cell, suppressing cells withonly one such establishment. This exam-ple shows how the reformulation of infor-mation and the introduction of a relatedlayer (or layers) of data can provide newand more useful interpretations than theoriginal data alone.

As shown in figure 6.5, this methodologywas used to analyze change by convert-ing the two pin maps into grids and thensubtracting one from the other to get themeasure of change. Grid cells have severaladvantages:

■ They clearly show crime intensity inplaces with many overlapping pointmarkers.

■ Their data are in the form needed fortime-series plots, bar charts, and statis-tical analyses, which are examples ofcrime space/time series, with one“slice” shown in figure 6.5.

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Figure 6.5

Two pin maps and achange map for August–September 1998, based onpin maps of Part I crimesfor Pittsburgh Police Zone2 (the equivalent to aprecinct). The pin mapsinclude streets and carbeats for the zone, as wellas downtown Pittsburgh,Pennsylvania, (the twobeats on the left) and several neighborhoods.

Source: Wilpen Gorr,Heinz School of PublicPolicy and Management,Carnegie MellonUniversity, Pittsburgh,Pennsylvania. Reproducedby permission.


■ The grids can be used to producechange maps that are more legiblethan pin maps.

Making maps come to life4

Application of Virtual Reality ModelingLanguage (VRML) to crime data allows

the user to change a viewpoint by rotat-ing, translating, zooming in and out, andtilting maps, providing a dynamic way ofviewing crime. The images shown in figures6.6 and 6.7 are part of an animationthat depicts different crime types reportedto police for various time periods inVancouver, British Columbia, Canada. In

158 Chapter 6

Figure 6.6

A rotated view ofsix types of crimes

on a map ofVancouver, BritishColumbia, Canada.

Different crimetypes are mappedto different colors

and stacked inthree dimensions.

Source: Lodha andVerma, 1999.


Figure 6.7

A zoomed-in viewof six types of

crimes on a map ofVancouver, BritishColumbia, Canada.

Source: Lodha andVerma, 1999.


its original context, this animation couldbe activated by clicking the start buttonshown in the images. The process of cre-ating the images involves first rasterizingthe data, then developing a color code keyfor the crime types, and, finally, designinga system for displaying the crime—in thiscase, as a histogram.

In figures 6.6 and 6.7, six different typesof crime are illustrated: assault (ASLT),breaking and entering (BNE), family trou-ble (FAMTRB), mischief (MSCHF), autotheft (TFAUTO), and theft (THEFT). Theheight of the stack is proportional to thetotal number of crimes in an area, so hotspots can be recognized as “highrises.”The two figures display the same data—the same map—from different viewpointsafter rotating and zooming in.

Another approach that is easy and yetquite effective as a means of visualizingchange involves animating a two-dimen-sional map. In figure 6.8, calls for servicein Mesa, Arizona, were mapped and (in the

original) animated. This map uses isolinemapping (joining points of equal value—in this case, equal CFS counts5). The ani-mation is a rapid-sequence display of aseries of maps of successive arbitrary timeintervals giving the visual impression ofmovement, much like an animated cartoon.

These applications illustrate how we canexpect maps to become more dynamic,more maneuverable, in the years ahead.Not only is it likely that the flexibility of maps will improve, but a more user-friendly environment will likely evolve inparallel. The average analyst will simplynot have time to do the programming thatLodha and Verma (1999) did to producetheir maps, and tools of this sort will, ofnecessity, become easier to use.

Digital aerial photography in policing

As noted throughout this guide, policedepartments employ GIS technology invarious applications, including criminal

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Chapter 6

Figure 6.8

A map showing thedensity of policecalls for service inthe first quarter of1999 in Mesa,Arizona.

Source: J. Wernerand the CrimeAnalysis Unit, Mesa, Arizona,Police Department.Reproduced by permission.


intelligence and crime analysis, crime pre-vention, public information, and commu-nity policing. Typical GIS applicationsinvolve taking a georeferenced crime database, filtering the data as needed, andmapping it over a street database to put thecrime data in its spatial context. Other datalayers may be used, such as census tracts,ZIP Codes, or council districts, but themost frequent underlying context is citystreets.

Digital street maps typically provideextremely limited contextual information.Streets are represented as single lines andsometimes color coded to indicate theirtype (e.g., freeway, arterial, collector).They can be labeled, if necessary, toenhance the level of detail, but the infor-mation provided by the street database isquite limited. In recent years it has becomeclear that the visualization of crime dataneeds to be improved to provide opportu-nities for better communication within andbetween the internal and external con-stituencies of police departments.

We can portray the general context ofvisualization along a continuum fromcomplete abstraction, at one extreme, to reality at the other (figure 6.9). Mostexisting crime maps would be located leftof center on the continuum. The crimemapping future will see increasing move-ment to the right, toward more realisticpresentations.

Confidentiality and privacy considerationswill probably set limits on how far to theright the process moves. Ultimately, theselimits will be culturally conditioned, withmore detail where public information andgeographic precision are prized and lessdetail where values put more emphasis onprivacy. This variation will be seen, and is already seen, from city to city, State toState, and nation to nation.

Increasingly, police departments are usinglarge-scale (representative fraction1:2,400) digital aerial orthophotos, alsoknown as DOQ, for digital orthophotoquadrangles. The “ortho” part means

160 Chapter 6

Figure 6.9

A diagramillustrating the

abstraction-reality

continuum.


perpendicular to the Earth’s surface, justas orthodontics corrects teeth so that theyare perpendicular to gums. Photos are pro-duced in “tiles,” or rectangles, such thateach tile typically represents about asquare mile (4,000 feet by 6,000 feet or24 million square feet6). The tiles are rectified for errors due to edge distortion(which increases away from the cameralens), other distortions due to the attitude(or position) of the airplane, and terrain.The edges of the tiles must match perfect-ly. The raster images (see chapter 4) are registered to the center lines of the appro-priate digital street-based maps so thatgeocoded crime data can be accuratelyportrayed in their “real” spatial context, permitting the identification of land uses,landmarks, and virtually any relevantlandscape features.

Furthermore, other coverages are typicallydigitized and made available as part of the orthophoto package, including spotheights, building footprints, topography,street boundaries, water bodies, and open

space. These can be quite useful with orwithout the associated orthophoto. Thedata within the photos and the associateddigitized coverages are so rich that theycould find applications in tactical teamoperations, for example, where land ele-vation, terrain, building heights, buildingfootprints, fences, and water bodies can beused to plan where to place officers anddetermine what their sight lines will be.

While maps have progressed from twodimensions to three, so have aerial pho-tos. Just as three-dimensional maps havelimited specialized applications, the appli-cations of three-dimensional photos arelikewise limited at present, but potentialuses are numerous and ultimately limitedonly by the user’s imagination. As theexample in figure 6.10 shows, images canbe quite striking in their depth and realismand add to the crime analyst’s arsenal ofenvironmental data.

Potential applications of aerial photogra-phy are numerous. Any geocoded

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Chapter 6

Figure 6.10

A three-dimensionalorthophoto cityscape.

Source: Orthophotoimagery and three-dimensionalmodel courtesy of ASDI Technologies, Colorado Springs,Colorado. Reproduced by permission.


information can be superimposed on thephotos, including census data, liquorlicense locations, drug-market data, injurylocations, probationer addresses, housingand zoning code violations, and otherdata that may be relevant to the needs of community policing (see, for example,figure 4.4 in chapter 4). Eventually, wemay see orthophotos supplanting “tradi-tional” street-based maps in some, if notall, of the applications where such mapshave typically been used.

Integration of orthophotos with conven-tional data is by no means the only possi-bility for enhanced visual display. Forexample, other raster images may bekeyed to the orthophotos. One possibilityexplored in a pilot project7 in BaltimoreCounty, Maryland, was the developmentof an archive of ground-level digital photosintended to characterize neighborhoodsand landmarks, precinct by precinct.

These ground-level images were linked toa land use map database, and symbolswere placed on the land use map at alllocations where a ground-level photo hadbeen taken. In ArcView®, hot links wereestablished between the symbols and thedatabase; when the symbols were clickedon, the ground-level picture would popup, as shown in figure 6.11 (for specificdirections, see ESRI, Inc., 1997, chapter16, “Creating Hot Links”). Yet anotherpossibility is the use of virtual reality toenhance the visualization of scenes in away that provides somewhat more flexi-bility than an analog videotape. A set ofdigital photos is taken in a circle using aspecial tripod, with the number of picturescalibrated to the focal length of the cam-era lens. Then a program is used to splicethe pictures together at the edges. A viewer with pan and zoom capabilitiesenables re-creation of the 360-degreepanorama. Free viewers are available for

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Figure 6.11

A ground-level photograph that pops

up when linked to aland use map in

ArcView®.


downloading (see appendix). This tech-nology could be used for training purpos-es, for documenting a crime scene thatcould serve as evidence in court, or forcommunity meetings when a specific loca-tion may be the focal point of interest (figure 6.12).

The 360-degree view provides a sense ofthe local environment that may not beconveyed by either a ground-level stillpicture or an orthophoto. Virtual realitypresentations could be linked to maps ororthos, too. To get a sense of what virtualreality is like, go the National Gallery of ArtWeb site (http://www.nga.gov/home.htm),where virtual reality tours are available(see appendix).

Until recently, the costs of storage andrandom access memory (RAM) limited the use of memory-intensive applications,such as the display of orthophotos. Eachtile, with its associated coverages, requiresabout 30 megabytes of hard drive space. A city like Baltimore or Washington, D.C.,each somewhat less than 100 squaremiles, would need more than 3 gigabytes(GB) dedicated to it. But with hard drives

of 20+ GB readily available at modestcost, this limitation no longer poses aproblem. Likewise, the relatively low costof RAM permits the fast manipulation oflarge graphics files.

Anticipated applications of orthophotos incommunity policing include:

■ Community residents being able toselect their homes or apartment build-ings and seeing how the locations ofcrime incidents or other illicit activitiesrelate to their own locations even ifthey have no firsthand experience ofthe events.

■ Officers more easily recognizing andcontextualizing information, whetherthat information involves crime loca-tions or various social or environmen-tal problems.

■ Police dispatchers (who are increasing-ly likely to be civilians relatively unfa-miliar with the detailed communitygeography of the city) having thecapability to enrich dispatching infor-mation with landmarks as reference

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Figure 6.12

A 360-degree panorama(top), and a selectedpart (bottom), ofinterest owing tothe potential conflictbetween public housingand owner-occupiedhousing residents. A wall was built toseparate the two“neighborhoods.”

Source: Keith Harriesand ThomasRabenhorst.


points provided by photos that pop upon their computer screens. Dispatchingtechnology could be enhanced dramati-cally if both dispatchers and officers inthe field could simultaneously seeorthophotographic images displayingthe origins of calls.

■ Crime prevention officers, analysts,commanders, and administrators beingable to present more persuasive visualevidence of problems and communicatebetter with legislative bodies, communitygroups, and their professional colleagues.

■ Planning being enhanced by realisticand accurate perceptions of the sizeand scope of proposed actions.

The integrationof GIS and GPS

The integration of data and technologieswill be pushed to the limit to extract asmuch value as possible. The possibilitiesare limited only by our imaginations.Whereas GIS offers a powerful toolbox,GPS, another technology with untappedpotential in law enforcement, permitsaccurate location finding in field settings.Triangulating from 24 satellites (put inorbit at a cost of $12 billion by the U.S.Air Force), GPS, in its more advancedmodes, can provide accuracy to one centimeter, or about half an inch. (For a tutorial explaining how GPS works, go tohttp://www.trimble.com.)

In reality, however, structural and envi-ronmental limitations, such as tall build-ings, a forest canopy, or operations inmountainous terrain, and the ability of the Air Force to manipulate the accuracy

available to civilians may mean an errorof up to 100 meters. These problems canbe overcome by using GPS base stationswith established locations and manipula-tions that have come to be referred to asdifferential positioning GPS or DGPS. Thisis defined by GPS World Magazine as:

A technique used to improve position-ing or navigation accuracy by deter-mining the positioning error at aknown location and subsequentlyincorporating a corrective factor (byreal-time transmission of correctionsor by postprocessing) into the position calculations of another receiver operating in the same areaand simultaneously tracking the same satellites.

The bottom line, however, is that usersshould generally be prepared for consider-ably lower resolution than some numbersquoted in promotional materials.

Another possibility for integrating tech-nologies involves the combined use ofGIS, GPS, and management informationsystems (MIS). This would allow close to real-time crime mapping, since thegeocoding step would be eliminated(Sorensen, 1997). However, some opera-tional questions need to be examined. Forexample, with how much certainty willincident locations be reported? A patrolofficer may report “arrived” status prema-turely, sending what in theory should bethe incident location, but what in practicemay be erroneous. On the other hand,GPS offers the possibility of accuratelyreporting places that have no meaningfulstreet address. A shopping mall covering100 acres may have a conventional street

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address that is meaningless in terms ofconveying locational precision. With GPS,the precise spot of an auto theft in theparking lot could be pinpointed, providingpotentially useful information for protect-ing areas of the parking lot that are proneto theft. Crime inside the mall buildingcould be reported with greater precision,whether in stores or in public spaces, pro-vided a reasonably close line-of-sight GPSreading could be obtained. This type of“precision mapping” is already being per-formed in Charlotte, North Carolina, whereGPS is used to plot exactly where crimesoccur, even for locations without streetaddresses.

An application of GPS that is in develop-ment is in the area of probation andparole—tracking probationers and paroleesif the terms of their release require limita-tions on their mobility. Indeed, futurecrime mapping applications will go beyondcrime mapping per se and into other com-ponents of the criminal justice system,including corrections. One can expect tosee the manipulation of spatial informa-tion and the application of new tools andtechnologies evolving in the comingdecade. While developments in otherbranches of criminal justice will likely parallel spatial analysis developments inpolicing, we also can expect to see thedevelopment of specialized methodsadapted to particular needs.

Postscript: The future of GIS in policing

When asked, “Why bother to discuss thefuture of GIS?,” Clarke (1997) respondedwith three reasons that might applyequally to crime mapping:

■ The need to plan equipment purchasesin the most efficient way, anticipatingtrends.

■ The need to stay abreast of the newfield spawned by GIS called geographicinformation science.

■ The need to be prepared for cross-fertilization of different criminal justicefields using GIS.

Clarke suggested that we can classifyspeculation into two types:

■ The forward extension of current trends.

■ Pure speculation, with the likelihoodthat some ideas will become realitywhile others will fade away.

Clarke also noted that data will becomemore complete, more detailed, more time-ly, and more varied than ever before. Thiswill be helpful to GIS as an “end user” of data—if the core data get richer, thenpossibilities for enhanced analysis alsoincrease. But Clarke was referring to GISin general. Can we have the same expec-tations for crime mapping?

Given that most crime data are alreadypoint defined (address-type), could they be improved in terms of spatial definition?Yes, at least marginally, through the useof GPS technology to remove addressambiguities, of which there are many.Whether locational data can be improvedacross the board may depend on the GPSprotocols that develop, one police depart-ment at a time. Data quality will probablyimprove, and addresses will probablybecome more precise as more agencies useautomated field reporting and integrated

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computer-assisted dispatching and recordsmanagement systems.

More on GPS and policing

Equipping patrol units with GPS wouldmean that their locations could be knownas often as each unit is “polled,” or auto-matically asked to respond, perhaps everyfew minutes. This would be an excellentsecurity device for officers, since theirlocation could be determined at any time,and, if an officer were down or neededhelp, knowing his or her location wouldsave valuable time.

But will GPS units typically be located in patrol cars? If so, some ambiguities inincident location will remain, since the carwill not always be parked precisely wherethe incident takes place. If GPS units arehand held, or incorporated into officers’uniforms, accuracy may improve signifi-cantly. The caveat on GPS data, however,is that the physical environment may notpermit a satellite reading owing to thepresence of tall buildings or other obsta-cles. Such difficulties aside, the era of real-time access to spatially enabled crime datais rapidly approaching—a developmentthat will force us to reconsider what weare doing and why and how we are doingit. This development is not without risks,such as the temptation to jump to prema-ture conclusions on the basis of real-time,but possibly unconfirmed, information.

The Web: Already a force in the dissemination of police information

A development that may have the great-est impact on the manipulation of crime

data may be the use of the Web for accessto data that have already been geocodedand are in the public domain, such as census data, including census geography. A corollary development will likely bedemands for local data on Web sites.Currently, police departments are split onthis issue, with some routinely puttingtheir less sensitive data on the Web, andothers refraining from doing so. Thepower of the Web to facilitate data sharingwill invite more open data disseminationprotocols, but these will be offset to someextent by security and privacy concerns.

Public information revisited:How much public access toallow

A pervasive force at work in the back-ground is the historical culture of policingthat has generally frowned on easy publicaccess to crime data. This reluctance isborne of several factors, including fear ofmisuse and misinterpretation; the indis-putable need for confidentiality for somecrimes, such as rape and juvenile offenses;fear of political reprisals when crime ratesare increasing; and a reluctance to expendscarce resources on data dissemination. In the background was, and is, a certaindegree of proprietorship regarding data anda somewhat natural reluctance to simplygive information away, even though itmight be in the public domain already, atleast in theory. A subtext to this is theview that crime data should not be madeavailable to the public if they do not haveto be, since public disclosure only consti-tutes another potential source of problems.

On the other hand, undermining nondis-closure is the fact that many city and

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neighborhood newspapers routinely pub-lish lists of local crimes, complete withaddresses. The only missing link is thecodification necessary to produce a city-wide map. Criminal matters most oftencome before the courts and, in so doing,jump squarely into the public domain. Inthe long term, it is increasingly difficult tosee how most crime data could be keptprivate, particularly data related to inci-dents for which there is no active inves-tigative interest and no need to protectvictims. Indeed, some departments thatwere secretive a decade ago are nowbeginning to put their data out on CD-ROMs or at least are talking about doingso. Others refuse to release data, even forlegitimate apolitical research.

One certainty is that, in addition to tech-nological and methodological innovationsin crime mapping and in collateral fieldsthat can be productively integrated intomapping methods, a debate about the dis-closure and exposure of crime data willcontinue into the foreseeable future.

Multimedia and integration

Multimedia and integration are likely to beamong the most prominent themes thatevolve in the crime mapping arena overthe next decade. Crime mappers will certainly take advantage of all the newtechnological advances, often using multi-media in ways that their developers mightnot have anticipated. Integration of vari-ous technologies, with data archiving (orwarehousing) and data mining becomingmore prominent, will be inevitable. Crimemapping may find itself merging into anenterprisewide GIS, as governments net-work access to data and standardize GIS

platforms across entire jurisdictions toensure compatibility and reduce costs.Change is certain. The only uncertainty is how the rate of change will vary fromplace to place.

Conclusion

Rapid change is the order of the day incrime mapping. It’s tempting to hold outand ride the next wave of innovation,skipping the present phase that is doomedto obsolescence as soon as the shrink-wrap comes off the software package orhardware box. This seems to have twoadvantages in the short term: minimizingcosts and reducing the need for training.The problem with this approach is that thetime is never right because another waveof innovation is always on its way.

Policing is undergoing a paradigm shiftresembling the kind of change that Kuhn(1962) described three decades ago in his book The Structure of ScientificRevolutions. A paradigm shift is the ideathat, from time to time, various realms ofhuman endeavor experience dramatic—almost revolutionary—changes. Kuhnapplied the term to changes in science,such as the transition in biology fromthinking in terms of whole organisms to amolecular or genetic perspective. Althoughit is pretentious to put changes in crimemapping on the same level with majorchanges in natural science, it is realistic to use the notion of a paradigm shift tounderstand the nature of changes in thetechnology of policing.

It is easy to underestimate or overestimatethe rate of change and the long-termimpact of technological changes in

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policing—including crime mapping. Whilecurrent and future advances promise tolend substantial support to law enforce-ment, we should remember that technolo-gies such as crime mapping are only toolsand, like other tools, their benefits to soci-ety depend on the human agents whowield them.

Summary


■ What types of changes may be expected in crime mapping in the next decade.

■ What geographic profiling is and howit can be useful.

■ High-resolution GIS and how it can be applied.

■ How complex statistical methods canbe used in spatial forecasting.

■ Why digital aerial photography is finding applications in crime mapping.

■ How various kinds of data visualiza-tion can be productively integrated.

■ How GIS and GPS can work together.

■ How the World Wide Web may bothincrease and limit data access.

■ Why we can expect continuing debateon public access to crime data and theresults of crime analysis.

■ What to expect in terms of applicationsof multimedia and integrationtechnologies.

Notes

1. This interpretation and the accompany-ing map were provided by Det. Insp. D.Kim Rossmo of the Geographic ProfilingSection, Vancouver Police Department,Vancouver, British Columbia, Canada.

2. According to a reviewer with profilingexperience, however, profiling is anextremely specialized and expensiveprocess demanding a skill level and finan-cial commitment that few agencies canrealistically afford, at least not with the currently available technology.

3. For more information on yellow pagesand phone books on CD-ROM, seehttp://www.phonedisc.com/.

4. I am indebted to Dr. S.K. Lodha(University of California, Santa Cruz) andDr. Armind Verma (Indiana University) forthe maps and accompanying interpreta-tion. The maps shown are from Lodhaand Verma, 1999. Available online athttp://wcr.sonoma.edu/v1n2/lodha.html.

5. See chapter 2 for additional explanation.

6. A square mile is 1,760 yards x 3 =5,280 feet

2or 27,878,400 square feet.

7. This pilot project was known as OPRA,for Orthophotographic Representation andAnalysis.

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Appendix: Internet Resources for Crime Mapping


Crime Mapping Research Center (CMRC) http://www.ojp.usdoj.gov/cmrcWeb siteOffice of Justice ProgramsNational Institute of Justice

Provides links to police departments, software vendors, and government agencies and is a good starting point for an exploration of crime mapping.

Crimemap listserv [email protected]

Connects interested subscribers to one In the body of the message, type: another and to CMRC and broadcasts SUBSCRIBE CRIMEMAP Your Namemessages to subscribers, such as announcements of upcoming conferences and solicitations. Participants can share knowledge and questions through this e-mail address.

Spatial Crime Analysis Section, http://www.usdoj.gov/criminal/gis/Criminal Division, Web site scashome.htm

Crime Mapping and Analysis Program, http://www.nlectc.org/nlectcrm/National Law Enforcement and cmap.htmlCorrections Technology Center

Web Sites Displaying Crime Maps

Berkeley, CA, Police Department http://inberkeley3.ci.berkeley.ca.us/annual_rpts/annual.html

Cambridge, MA, Police Department http://www.ci.cambridge.ma.us/~CPD


Charlotte-Mecklenburg, NC, http://www.ci.charlotte.nc.us/cipolice/Police Department spab/gis/cmpd_gis.htm

Clearwater-Pinellas County, FL, http://www.co.pinellas.fl.us/bcc/juscoord/Police Department enforcer.htm

Detroit (Tri-County), MI, Police Department http://www.cus.wayne.edu/U_safety/pdfs/handbook.pdf

Evansville Courier & Press, Evansville, IN http://www.evansville.net/courier/crime

Portland, OR, Police Department http://www.worldstar.com/~carltown/crmemap.htm

Sacramento, CA, Police Department http://www.sacpd.org/templates/f-nstart.html

Salinas, CA, Police Department http://www.salinaspd.com/maps.html

San Antonio, TX, Police Department http://www.ci.sat.tx.us/sapd/maps.htm

San Diego, CA, Police Department http://www.sannet.gov/police/crime-facts/crimanal.shtml

Tempe, AZ, Police Department http://www.tempe.gov/cau

Note: This current list can be found at http://www.ojp.usdoj.gov/cmrc/weblinks/welcome.html.

GIS and Analysis Software and Other Resources

Environmental Systems Research Institute http://www.esri.com(ESRI): Arc/Info®, ArcView®

MapInfo® http://www.mapinfo.com

Idrisi http://www.clarklabs.org

Illinois Criminal Justice Information http://www.icjia.state.il.usAuthority and Spatial and Temporal Analysis of Crime (STAC) User’s Forum

Guidebook for Measuring Crime in Public http://huduser.org/publications/Housing with Geographic Information pubasst/gis/htmlSystems, $5.

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GIS Tutorial

GISTutorial Workbook http://www.heinz.cmu.edu/gistutorial

Hot Spot Identification Methods (see Jefferis, 1999)

Geographic Analysis Machine (GAM) http://www.ccg.leeds.ac.uk/smart/gam/gamin.html

Spatial and Space-Time Scan Statistic http://www.dcpc.nci.nih.gov/bb/(SaTScan) software.html

Spatial Pattern Analysis Machine (SPAM) http://athene.csu.edu.au/~jratclif/ware/spam1.htm

Spatial and Temporal Analysis of Crime http://www.icjia.org/(STAC) (PDF format)

Vertical Mapper™ http://www.northwoodgeo.com

Spatial Analyst http://www.esri.com/software/arcview/extensions/spatext.html

Idrisi http://www.clarklabs.org

CrimeStat http://www.icpsr.umich.edu/NACJD/crimestat.html

SpaceStat http://www.bruton.utdallas.edu/anselin/anselin.htm

Note: This current list can be found at http://www.ojp.usdoj.gov/cmrc/weblinks/welcome.html

Research Organization

Police Executive Research Forum: http://www.policeforum.org/Crime Mapping Case Studies, Volumes I and II, and other issues relating to law enforcement


Computer Mapping Laboratory

Police Foundation: Publishes Crime http://www.policefoundation.org Mapping News, a quarterly bulletin on crime mapping, which emphasizes successful implementation of computer mapping in law enforcement

Free Sources of Census Data

CIESIN http://www.ciesin.org

U.S. Census Bureau http://www.census.gov

Proprietary Source of Census Data

GeoLytics: Sells CensusCD+Maps™ directly http://www.censuscd.com/cdmaps/for $249.95 for a single user license; censuscd_maps.htm$750 for LAN/CD-Tower license.

Global Positioning Systems (GPS)

Tutorial http://www.trimble.com

Overview http://www.utexas.edu/depts/grg/gcraft/notes/gps/gps.html

Global Positioning Systems World Magazine http://www.gpsworld.com

Virtual Reality (VR) Viewers

Sample VR tour http://www.nga.gov/exhibitions/webtours.htm

Quick-time viewer http://www.apple.com

Live-picture viewer http://www.livepicture.com

Examples of VR applications in a http://www.nashville.net/~police/police department VRMovies/index.htm

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Other Sources

Coordinate Systems http://www.utexas.edu/depts/grg/gcraft/notes/coordsys/coordsys.html

Hunter College, New York, NY http://everest.hunter.cuny.edu/capse/projects/nij/crime.html

International Association of Crime Analysts http://www.iaca.net/

Metadata Standards and Information http://fgdc.gov/standards/standards.htmlhttp://www.blm.gov/gis/nsdi.htmlhttp://www.blm.gov/gis/meta/minimum.html

The Omega Group (CrimeView® geographic http://www.theomegagroup.com/analysis product) Map Gallery crimevw.htm

University Consortium for Geographic http://www.ucgis.orgInformation Science

To contact the author, e-mail Keith Harries at [email protected].

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Indexabstraction 10, 13, 22, 30, 32, 38-40,49, 50, 61-63, 160

activity space 29, 136

aggregate 40, 74, 109

aggregating data 105

area 3, 4, 8, 10-14, 16, 18-20, 22-26,29, 30, 33, 36, 40, 42, 43, 45-51, 53,54, 56, 57, 59, 60, 62-64, 68, 71-73,75, 77-79, 81-83, 89, 92, 96, 97, 102,105, 107-109, 112, 113, 115-121, 124,126-131, 133, 136, 139, 140, 142, 146,148, 151-155, 159, 164, 165, 169, 179

ASCII file 121

autodialer 139, 171

balance 31, 59, 63

base map 2, 25, 89, 91, 99, 103, 104

batch matching 99

box plot 56, 57

breakpoints in crime data 50, 51

buffering 90, 118, 119, 125, 130

calls for service 42, 45, 68, 71, 72, 86,94, 102, 103, 114, 119, 123, 129, 134,137, 146, 147, 156, 159

cartogram 30, 32

cartographic school 4

cartography 3, 4, 6, 8, 13, 14, 17, 30,31, 39, 52, 57, 64, 84, 130, 171, 175,177-179

census 5, 8, 15, 20, 24, 40, 43, 47, 72,74, 77, 78, 92, 96-98, 102, 108, 112,120-123, 126, 129, 130, 146-148, 160,162, 166, 171, 176, 177, 180, 184

centroid 102, 108, 109

choropleth map 24, 40, 42, 45-48, 50,51, 55, 57, 64, 65, 92, 115, 124, 146,156

chroma 60, 61

classes of data 50

classifying map information 124

cluster analysis 115, 117

CMRC, see Crime Mapping ResearchCenter

color 1, 3, 7, 8, 9, 17, 31, 32, 41, 47,50, 54, 57, 60-63, 69, 87, 88, 100, 114,116, 124, 152, 158-160

community organizations 88, 89

community oriented policing iii, 64, 80,82, 90, 147

community policing 8, 77, 82, 120, 134,144, 146, 160, 162, 163, 171

Index

computational model 39

ComStat 78-80, 142

continuous distribution 20

coordinate system 14-17, 31, 59, 65, 82, 185

COP, see community oriented policing

corrections vii, 64, 67, 84, 90, 126, 136,139, 141, 148, 164, 165

costs 10, 31, 92, 94, 128, 132, 163, 167

courtroom presentation 144

courts vii, 64, 84, 90, 126, 136, 139,144, 148, 167

Crime Mapping Research Center iii, iv, v,3, 40, 115, 146, 174, 175, 180, 181

crime prevention 5, 8, 29, 75, 126, 128,134, 136-139, 148, 160, 164, 170-173,175-177, 179, 180

criminal geographic targeting 152

criminal intelligence 126, 136, 137, 148, 159

criminal spatial behavior 28, 29, 178

cue 29, 39, 122

curtilage, see street curtilage

custom 30, 52, 61, 64, 94, 116, 123

dasymetric mapping 57, 64

data exploration 112

data integration 110, 147

data mining 90, 123, 125, 167, 177

data sharing 128, 133, 134, 166

data visualization 6, 149, 168

data warehousing 90, 123, 125, 177

demographic change 67, 71, 76

density 4, 20, 23, 25, 28, 42, 45, 46, 48,57, 64, 73, 74, 81, 105-108, 113, 128,129, 159

density analysis 105

derivative measure 53, 90, 109, 125

desktop mapping 2, 3, 6, 42, 50, 51

DGPS, see differential positioning GPS

differential positioning GPS 164

digital aerial photography 152, 168

digital orthophoto quadrangle 160

direction 15, 17, 18, 32, 33, 98, 100,112, 129, 141, 154, 162

discrete distribution 20, 24

displacement 67, 71, 75, 76, 105, 130,139, 169, 180

distance 11, 15, 17-19, 21, 23, 27, 28,30, 38, 54, 108, 109, 112, 117, 141,152

distance decay 27, 28

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DOQ, see digital orthophoto quadrangle

dot map 40

EDA, see exploratory data analysis

equal area 51

equal count 51, 54

equal range 51, 54

ESDA, see exploratory spatial data analysis

exploratory data analysis 123

exploratory spatial data analysis36, 53, 123

Federal Geographic Data Committee 110,111, 185

Federal Information Processing Standardcode 121

FGDC, see Federal Geographic DataCommittee

filtering 38, 90, 100, 101, 103, 104,107, 125, 160

flow map 48, 49

focus of attention 9, 59, 60, 63

forecasting 149, 151, 152, 155, 156, 168

function 64, 69, 76, 79, 108-110, 113,117, 120, 123, 127, 130, 152

geocoding 90, 92, 97-100, 125, 127,129, 142, 164, 169

geographic information system v, 4, 6,10, 12, 17, 33, 38, 49, 57, 60, 61, 83,84, 87, 90-92, 94, 95, 97, 98, 100, 105,107-112, 114, 119-122, 124-127, 129,130, 136, 137, 139, 140, 142, 149, 151,152, 155, 159, 160, 164, 165, 167, 168,170-176, 178-183, 185

geographic profiling 137, 149, 152,154, 168

geographic school 4

geoprofile 153, 154

GIS, see geographic information system

GIS software 17, 61, 94, 100, 107,109, 121

global-local precedence 22

global positioning system 64, 78, 94,123, 131, 149, 151, 152, 164-166,168, 179, 184

GPS, see global positioning system

grid cell analysis 115, 116

halfway house location 142

high-resolution GIS 149, 152, 155, 168

histogram 8, 54, 56, 58, 152, 159

hit rate 99, 100

hot spot 11, 19, 56, 74, 75, 94, 112-116, 118, 125, 126, 138, 139, 169, 183

hot spot mapping 71, 74, 115, 118

Index

hue 60, 61

hypothesis 7, 8, 36, 37, 123

hypothetic-deductive method 37

hypothetic-deductive process 8

ICAM, see Information Collection forAutomated Mapping

immigrant group 77

incident 4, 11, 20, 23-26, 28, 30, 36, 40,42, 62, 67, 69, 71-73, 75, 84, 87, 92,94, 100, 102, 103, 105, 107, 108, 113-117, 120, 134, 137, 139, 144, 155, 163,164, 166, 167, 169

inclusion 31, 59, 63, 108

Information Collection for AutomatedMapping 69, 128, 178

integration 110, 126, 131, 149, 151,152, 162, 164, 167, 168

intermediate scale 26

internal organization 59, 60, 63

interval measurement 8, 12, 23, 24, 32,51, 52, 54, 55, 63, 116, 124, 159

investigator 18, 36, 37, 64, 67, 70, 90, 136

isoline map 24, 64, 159

Jenks’ Optimization 51

jeopardy surface 152

journey to crime 28

latitude 14-17, 32, 33, 40, 59, 98, 152

layering 92, 110

legibility 10, 20, 32, 61-63

line data 19, 20, 130

linear map 25

linear symbol 25, 48, 49

linkage blindness 133

location 8, 11, 14-18, 20, 24, 28, 33, 36,40, 42, 43, 45, 62, 65, 69, 71, 74, 77,80, 83, 84, 86, 87, 96, 98, 99, 103-105,112, 115, 116, 118-121, 129, 130, 132,133, 136-143, 152, 162-166, 170, 174,180

longitude 4, 14-17, 32, 33, 40, 59, 98,152, 179

macro scale 26

map composition 59

map design 6, 8, 18, 31, 32, 45, 57, 61-63, 67, 89, 171, 177

map element 8, 17, 18, 60, 62, 124, 130

map projection 13, 14, 16, 31, 123

mapping space 102

mapping time 101, 102, 180

match score 100

meridian, see longitude

metadata 110, 111, 185

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micro scale 22, 26, 29

mimetics 63, 65

minorities 77, 78

modus operandi 37, 103, 104, 139, 142

MO, see modus operandi

multimedia 149, 167, 168

NAD, see North American Datum

National Spatial Data Infrastructure110, 185

natural break 54

nominal measurement 23

North American Datum 16

NSDI, see National Spatial DataInfrastructure

offender access to services 144

operator 109, 132

ordinal measurement 23

orthophoto 10, 97, 131, 132, 151, 160-164, 168

overlap 1, 26, 38, 45, 80, 84, 94, 108,115, 126, 152, 157

parallel 14-16, 32, 59, 159, 165

parolee location 142

patrol area 11, 20, 23, 24, 40, 67, 72,96, 102, 108

patrol officer 64, 67, 69, 72, 90, 94, 119,153, 164

pattern 1, 3, 4, 18, 19, 25, 26, 28, 29,31, 32, 36, 37, 39, 46, 56, 60, 68, 70,74, 76, 80, 89, 99, 104, 115, 116, 124,128, 130, 133, 134, 136-139, 142, 144,154-156, 169-173, 175, 183

pattern recognition theory 39

Pauly Areas 92

perspective symbol landmark 63

pin map v, 1-3, 20, 24, 40, 42, 49, 79,87, 94, 116, 156-158

place v, 3, 7, 11, 16-20, 24, 25, 28, 31,33, 38, 40, 42, 46, 54, 60, 67, 71, 74,75, 77, 96, 112, 123, 133, 145, 157,164, 167, 169, 170, 172, 176, 178-180

point map, see pin map

police manager 67, 71, 90

POP, see problem oriented policing

presentation vii, 20, 22, 25, 31, 35, 38,42, 48, 50, 51, 54, 56, 62, 80, 87-89,115, 142, 144, 160, 163

principle of least effort 27

prison location 140

privacy issues 128, 134

probation 82, 104, 105, 139-142,162, 165

probationer location 139, 140, 142, 162, 165

Index

problem oriented policing 64, 80, 82, 90

public access 128, 135, 166, 168

public information 126, 136, 144, 146,148, 160, 166

qualitative map 23

RAM, see repeat address mapping

raster 90, 95-97, 121, 125, 131, 132,159, 161, 162, 172

ratio 21, 23, 40, 50, 109

RCAGIS, see Regional Crime Analysis GIS

Regional Crime Analysis GIS 126

repeat address mapping 6, 74, 163

representative fraction 21, 22, 62, 160

resource allocation 67, 72, 73, 87, 92,126, 136, 146, 148

RF, see representative fraction

Rigel 152

risk and protective focused prevention83, 174

routine activities 28, 29, 171

scale 1, 10, 15, 17, 18, 21-24, 26, 30,31, 33, 38, 45, 46, 48-50, 56, 59, 62,63, 84, 87, 88, 96, 97, 109-111, 113,119, 123, 124, 126, 136, 144, 155, 160

selecting 12, 52, 83, 100, 101, 119

sentence mapping 144

situational crime prevention 29, 171

social ecologist 4

social ecology school 4

space 1, 4, 11-15, 19, 28, 29, 31, 46,56, 59, 60, 79, 96, 104, 112, 113, 115,138, 157, 161, 163, 165, 169, 179, 183

spatial autocorrelation 115, 118, 178

spatial experience 38

stage model 39

standard deviation 8, 9, 51, 54, 58, 154

State Plane Coordinate System 16

statistical map 24, 42, 53

statistical symbol 24, 42, 45, 108

street curtilage 115

surface map 13, 25, 40, 48, 64, 116

survey 38, 46, 78, 86, 94, 113, 115,128, 129, 146, 175, 180

SYMAP 92

SYMVU 21, 92

synthesis 38, 50, 134

technology imbalance 127, 128

template 3, 29, 182

192

193


thematic map 23, 24, 32, 40, 42, 45, 48-50, 57, 59, 63

TIGER, see Topologically IntegratedGeographic Encoding and Referencing

time vii, 1, 6, 7, 11-13, 18, 30, 31, 33,37, 39, 42, 45, 49, 52, 56, 57, 59, 62,67, 69, 71, 75, 76, 79, 80, 96, 101-104,111-115, 129-131, 137-139, 141, 142,145, 157-159, 164, 166, 167, 178, 179,183

TIN, see triangulated irregular network

Topologically Integrated GeographicEncoding and Referencing 97, 98, 169,171, 176, 180

triangulated irregular network 96

typological school 4

Universal Transverse Mercator CoordinateSystem 16, 17

value 1, 8, 9, 15, 23-25, 33, 34, 45, 47,48, 52-54, 56-58, 60, 61, 64, 72, 77,89, 95, 96, 102, 105, 108, 109, 115,116, 121, 123, 124, 126, 144, 145, 147,159, 160, 164

vector 90, 95, 96, 97, 125

virtual reality 158, 162, 163, 175, 184

Virtual Reality Modeling Language 158

visual communication 8, 10, 35-38

visual thinking 35-38, 50

VRML, see Virtual Reality ModelingLanguage

Web, see World Wide Web

World Wide Web v, 3, 17, 40, 59, 64, 78,121-123, 128, 135, 136, 144, 146, 151,163, 166, 168, 181

For more information on the National Institute of Justice, please contact:

National Criminal Justice Reference ServiceP.O. Box 6000

Rockville, MD 20849–6000800–851–3420

E-mail: [email protected]

To access the World Wide Web site, go tohttp://www.ncjrs.org

If you have any questions, call or e-mail NCJRS.

About the National Institute of Justice

The National Institute of Justice (NIJ), a component of the Office of Justice Programs, is the research agency of the U.S. Department of Justice. Created by the Omnibus Crime Control and Safe Streets Act of 1968, as amended,NIJ is authorized to support research, evaluation, and demonstration programs, development of technology, and both national and international information dissemination. Specific mandates of the Act direct NIJ to:

● Sponsor special projects, and research and development programs, that will improve and strengthen the criminal justice system and reduce or prevent crime.

● Conduct national demonstration projects that employ innovative or promising approaches for improving criminal justice.

● Develop new technologies to fight crime and improve criminal justice.

● Evaluate the effectiveness of criminal justice programs and identify programs that promise to be successful if continued or repeated.

● Recommend actions that can be taken by Federal, State, and local governments as well as by private organizationsto improve criminal justice.

● Carry out research on criminal behavior.

● Develop new methods of crime prevention and reduction of crime and delinquency.

In recent years, NIJ has greatly expanded its initiatives, the result of the Violent Crime Control and Law EnforcementAct of 1994 (the Crime Act), partnerships with other Federal agencies and private foundations, advances in technology, and a new international focus. Some examples of these new initiatives:

● New research and evaluation are exploring key issues in community policing, violence against women, sentencingreforms, and specialized courts such as drug courts.

● Dual-use technologies are being developed to support national defense and local law enforcement needs.

● The causes, treatment, and prevention of violence against women and violence within the family are being investigated in cooperation with several agencies of the U.S. Department of Health and Human Services.

● NIJ’s links with the international community are being strengthened through membership in the United Nations network of criminological institutes; participation in developing the U.N. Criminal Justice Information Network;initiation of UNOJUST (U.N. Online Justice Clearinghouse), which electronically links the institutes to the U.N. network; and establishment of an NIJ International Center.

● The NIJ-administered criminal justice information clearinghouse, the world’s largest, has improved its online capability.

● The Institute’s Drug Use Forecasting (DUF) program has been expanded and enhanced. Renamed ADAM (Arrestee Drug Abuse Monitoring), the program will increase the number of drug-testing sites, and its role as a “platform” for studying drug-related crime will grow.

● NIJ’s new Crime Mapping Research Center will provide training in computer mapping technology, collect andarchive geocoded crime data, and develop analytic software.

● The Institute’s program of intramural research has been expanded and enhanced.

The Institute Director, who is appointed by the President and confirmed by the Senate, establishes the Institute’s objectives, guided by the priorities of the Office of Justice Programs, the Department of Justice, and the needs of the criminal justice field. The Institute actively solicits the views of criminal justice professionals and researchers in the continuing search for answers that inform public policymaking in crime and justice.

crime mapping principles & practice

Documents