Close-ups and the scale of ecology:

Land uses and neighborhood crime

Adam Boessen

Department of Criminology and Criminal JusticeUniversity of Missouri, St. Louis

This material is based on research supported by: NSF award BCS-0827027.

Outline of the Talk

Boessen, A and J. R. Hipp. (2015). Close-ups and the scale ofecology: Land uses and the geography of social context andcrime. Criminology, 53(3): 399-426.

What is the spatial scale of neighborhood processes?What role does land uses play in these processes?

Micro Focal Neighborhood

Familiarity and SocialInteraction

Proximity

Crime Hot Spot

Egohoods

Hipp, J. R. and Boessen, A. (2013). Egohoods as waves washing across the city:A new measure of “neighborhoods”. Criminology , 51(2):287-327.

Spatial Distribution of Social Networks

Hipp, J. R., Faris, R. W., and Boessen, A. (2012). Measuring neighborhood:Constructing network neighborhoods. Social Networks, 34:128-140.

Riverside County

Los Angeles County San Bernardino County

San Diego County

NEVADA

CALIFORNIA

Irvine

Santa Ana

Twin Communities

Network StudyLegend

Orange County

TCNS Tracts

City Area

Boessen et al. (2014). Networks, Space, and Residents’ Perception of Cohesion.American Journal of Community Psychology, 53:447-461.

LegendAlters

Important MattersTCNS City

Irvine

Santa Ana

Activity Patterns: Work CommutingLEHD − Southern California by Tract 2002

Edge Present When # Workers sent between two tracts is > 50

and When Distance (Miles) Traveled is > 2

Land use and Crime

Social and physicalcontext

Situates opportunity

What is the spatialscale of different landuses?

Copyright: ©2013 Esri, DeLorme, NAVTEQ

LegendLand UseINDUSTRIAL

< -0.50 Std. Dev.-0.50 - 0.50 Std. Dev.0.50 - 1.5 Std. Dev.1.5 - 2.5 Std. Dev. > 2.5 Std. Dev.

Current Study

The scale of ecological crime processes is often uncertain

Too large and too small?

The role of a variety of land uses

Data: Cities used in analysis

7 Cities in 2000 census blocks (73,010 total blocks)All around the year 2000

Chicago, ILCleveland, OHColumbus, OHDallas, TX

Los Angeles, CA

San Francisco, CA

Tucson, AZ

Data & Measures

Police Crime data

City police department dataAssault, Robbery, Homicide, Larceny, Motor Theft, Burglary

Land Use

City, county, and regional planning departmentsResidential, retail, industrial, office, other

Demographics

CensusRace/ethnicity, economic disadvantage, racial heterogeneity,residential instability, vacant units, population density, inequality, andyoung people

Analysis Plan

Multilevel count models for six crime types

Fixed effects included for cities

Micro, meso, and broader area

Two main models

Block group and spatial lagsAdd micro block information

-200

0

200

400

600

800

1000

1200

Retail Industrial Residential

Pe

rce

nt

Incr

eas

e/D

ecr

eas

e in

Cri

me

Assault

Robbery

Homicide

Burglary

Results for Demographic Measures

Micro Block: vacant units, residential stability, density

Meso/Broader Area: inequality, disadvantage, racial/ethnicheterogeneity

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Low block groupheterogeneity

Average block groupheterogeneity

High block groupheterogeneity

Agg

rava

ted

Ass

ault

Block Group Racial/Ethnic Heterogeneity

Effect of block and block group racial/ethnic heterogeneity and aggravated assault rate

Low block heterogeneity

Average block heterogeneity

High block heterogeneity

Discussion

Neighborhood processes operate at different spatial scales

Homogeneous blocks within heterogeneous block groups

Land uses crucial for ecology of crime

Summary and Implications

Need to understand different neighborhood processes at differentspatial temporal scales

Social and physical context

Interdependence

Acknowledgements & Thank You

Acknowledgments

John HippCharis KubrinCarroll SeronGeorge TitaCarter ButtsElizabeth CauffmanNicholas Nagle

Thank You!

Gary PainterRichard ParksStacia Fewox