the geek and the gumshoe or can mathematics and computers really solve crimes?
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8/8/2019 The Geek and the Gumshoe or Can Mathematics and Computers Really Solve Crimes?
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³The Geek and the Gumshoe´or
³Can Mathematics and
Computers Really SolveCrimes?´
Michael ³theprez98´ Schearer Frank ³Thorn´ Thornton
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Introduction
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Who are we, and why are wehere?
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The Geek: Michael Schearer Fascinated by the application of
mathematics to real-world situations
Recently separated from nearly 9 years inthe U.S. Navy (flying aircraft) Currently working for a U.S. government
contractor in Maryland (flying a desk)
Contributing author to PenetrationTester's Open Source Toolkit (Volume 2)Netcat Power Tools (April 2008), andmaybe more!
Football coach and proud father of three
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When did
Frank start as a cop?
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Looking forward«
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The Gumshoe:F
rank Thornton Law Enforcement Officer, 1980 ± 2002. Served
in a variety of ranks and positions from PatrolOfficer to Chief of Police. Also worked in VT
Forensic Lab on Latent Fingerprints and crimescene investigations. Rated as a Class I (Homicide) Death Investigator by Vermont¶sOffice of the Chief Medical Examiner
Hacking computers since ~1973 Helped create ANSI Standard ³ANSI/NIST-CSL
1-1993 Data Format for the Interchange of Fingerprint Information´
Author and co-author of a half-dozen books oncomputer security. (Cheerfully blatant plug!)
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Agenda Introduction
± Explanation
± Videos ± Perceptions
Math, Computers & Crime ±Math in everyday life ±Math and crime-fighting
Conclusions
Questions & Answers
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Police Investigations Are ALL About Collecting Data
Who
What
When
WhereHow
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Investigations differ from other data collection in several areas
Everyone lies to the police. Fact has to be separated from:
-Lies.-Fiction.-Opinion.-Other false positives. (May be thousands)
Eye witnesses have a high credibility withprosecutors and juries, less so with cops.
Everyone lies to the police. Failure can be dangerous to the public. Did we mention that everyone lies to the police?
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Information = Data
This is sometimes recognized at some level.Joseph Wambaugh, ex-LAPD Detective,
award winning mystery novelist andscreenwriter wrote this in The Black Marble:
³ Clarence looked around at the roar of
activity, at the grinding paper mill. Paper everywhere. Take away my gun and car,but pl ease don't take my pencils.´
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Information = Data
³ You walked in withinformation and a
pretty face.
You can¶t leave
with both.´
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Doesn¶t it really work like on CSI?
The CSI Effect
Perception and Reality
± DNA Testing ± AFIS Searches ± School-Associated Violence ± Cops are always doing exciting things like
getting in fights or shooting bad guys
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So, knowing all that, what other tools are available to help
investigations?
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So let¶s explore how math andcomputer technology can helpwith investigations«
³Time for sc i ence!´
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Math is everywhere. Elections
± Voting, exit polls, voter identification/analysis
Sports ± Statistics, sabermetrics, betting/sports book
Lottery ± Probability (or perhaps improbability!)
Math in advertising ± frequency atlas, Google advertisements, British
two pound coin
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B
illboard say what?
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What is wrong with this picture?
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Can Mathematics andComputers Really Solve
Crimes?
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Crash Reconstruction Collision evidence
± positions of rest, skid marks, roadway markings,damage to vehicles, damage to property
Other evidence ± Witness recollections, traffic control devices,
weather conditions, lighting issues
Available specifications
± Newton¶s laws of motion Collision reconstruction techniques
± Damaged-based ± trajectory-based
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Image Deblurring
³Enhance«enhance«enhance«´
Blurring is typically caused by movement
during the capture process by the camera or by the subject, or an out of focus lens
Deblurring involves finding a mathematicaldescription of how the image was blurred
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Image Deblurring
Before« After
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Image Deblurring
A camera captured this image
Image deblurring produced this image
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Deblurring Fingerprints
A very touchy subject! By deblurring afingerprint, are non-existent details being
added to a latent print? Typically, any enhancement (fingerprint or
otherwise) must be verifiable and able to beduplicated by another expert
The risk in ³crossing the line´ is highlydependent upon use of tools
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Fingerprint Matching
Different vendors use different algorithms
10 different Fingerprint Individuality models
Minutiae matching vs. Pattern matching Speed and throughput vs. accuracy
Error rates
± TypeI(FP, FRR) vs. Type
II(FN, FAR) ± Crossover or Equal Error Rate
± Security vs. Forensic Science
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Receiver Operating Characteristics
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Fingerprint Classification
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Escape Math Variables
± Time, Method of travel, Achievable speeds,Traffic density, Traffic choke points
Dijkstra¶s algorithm ± Link-state routing protocols (OSPF), MapQuest,
Google Maps
Random walks ± Calculate distance escaped POWs could travel
in WW2
Trawler problem Drive-time calculations (MapPoint) Social network analysis (to be discussed
later)
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Dijkstra¶sAlgorithm
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Random walks
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Trawler problem
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Narrowing the Suspect Pool
Profiling ± Psychological/criminal
± Geographic (to be discussed later) Venn diagrams
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Social Networks Social network analysis
± Google¶s PageRank algorithm is an example of network analysis
± Organized crime, gangs, terrorist cells,
individuals, other organizations ± Social relationships in terms of nodes and ties ± Determine the social capital of individual actors
Things to consider
± Who are someone¶s closest friends/associates? ± Where might that person flee to? ± Structural cohesion: could you eliminate a
specific individual from a group which couldcause that group to collapse?
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Socialnetwork of aproject team
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Social network of 9 /11 terrorists
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Crime Mapping
Choropleths
Pin Mapping
Hot Spot Analysis Geographic Profiling
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France,1829
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London,September 1854
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Choropleths and Pin Mapping
NYPD has used traditional pin mapping sinceat least 1900
University of Chicago researchers mappedcrime in Chicago neighborhoods (1920-30s)
These methods of mapping helped to identifyrelationships between crime and
neighborhoods, social disorganization,poverty, and physical deterioration
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Automated Crime Mapping
Automated mapping began in the late 1960s ± Did not really ³take off´ until the 1990s
Hot Spot Analysis ± Finding geographic concentrations of types of crimes; finding causes for those hot spots;aggressive policing in those areas
Geographic profiling ± If psychological profiling tells you ³who´,geographic profiling tells you ³where´
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Hot Spot Analysis
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Hot Spot Analysis
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Geographic Profiling
If Psychological/criminal profiling tells you³who´, geographic profiling tell you ³where´
Suitable for serial crimes: murder, rape,robbery, arson, predatory crimes
Gives police a starting point from which tonarrow down lists of suspects
Does not replace traditional investigativetechniques, but supplements them to helpmanage the large volume of information
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Geographic Profiling
CrimeStat, Dragnet, Predator, Rigel
Theory is based upon ³journey to crime´ and
³principle of least effort´ ³Journey to crime´ varies among type of
crime, age, race, etc.
Includes a buffer zone around the offender¶shome or base of operations
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Saanich Serial Arsonist
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Jeopardy Surface
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Probability of Offender
Residence
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GeoProfile
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Some Other Examples
Spherical Trigonometry: Determiningposition on Earth based on two like
photographs (with a few caveats«) Prisoner¶s dilemma: Is it better to cooperate
or defect?
Steganography and covert channels:
Finding hidden information Predictive Analysis: Predicting the location
of a serial event
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The Future is Now: RTTC
26-member staff, on 24/7/365 15 workstations, divided among teams of
officers Each team has a particular assignment, suchas homicides or shootings
Satellite imaging
Precinct-by-precinct maps Ties together information to solve crimes 2-story tall projection screens
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NYPD Real Time Crime Center
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The Future is Now: RTTC
Cognos data warehouse, using IBM OmniFind 8.2on SUSE Linux blade servers
Link Analysis Capacity can call up all knownaddresses for a suspect and known associates
When a crime occurs, any number of searches of public records are then run: ± Over 5 million NYS criminal records, parole and
probation files ± Over 20 million New York City criminal complaints,911/311 calls and summonses spanning five years
± Over 31 million national crime records ± Over 33 billion public records.
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Conclusions
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References
Mark Bridger, Northeastern University Valdis Krebs @ orgnet.com David Weisburd and Tom McEwen, ³CrimeMapping and Crime Prevention´
Dr. Kim Rossmo, Texas State University Rob Gebeloff, NJ Star-Ledger Dr. Raymond Chan, CUHK
Mitsubishi Electronic Research Laboratories Zeno Geradts, Netherlands Forensic Institute Henry C. Lee and R.E. Gaensslen, Advances in
Fingerprint Technology, 2nd Ed.
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