Mapping & Analysis for
Public Safety Program
U.S. Department of Justice
National Institute of Justice
Office of Research and Evaluation
A program to advance applied and
basic research involving the spatial
analysis of crime...
Mission and Purpose
…to enhance the application of spatial analysis in
criminology and policing through...
RESEARCH
EVALUATION
DEVELOPMENT
DISSEMINATION
…and bridge the gap between research & practice.
RESEARCH
• Fellowship program
– Building an interdisciplinary knowledge base.
• Intramural research
– Spatial Analysis of Homicides in Washington DC
– A Multi-Method Exploration of Crime “Hot Spots”
– Using GIS to Map Crime Victim Services
– Spatial Effects of Religious Institutions & Homicide
• Extramural research
– Over 30 grants in basic and applied research on
spatial analysis of crime.
Research Premise & Questions
for Philadelphia, PA
Wilson (1987) and others (Krivo and Peterson, 1996), consistent with social
disorganization theory, suggest that violence in urban areas result from weak
institutional bases. Deindustrialization of communities is followed by loss of
other ins titutions of social control. Religion, as one such institution, has
been studied as an element of social control and has been shown to have
moderate effects on crime rates (Baier and Wright, 2001). Seldom, however,
has the impact of density of religious institutions on violence been studied.
Furthermore, few studies have specifically examined the relative impacts of
loss of manufacturing jobs and availability of religious institutions.
Hypothesis 1: High density of religious institutions in census tracts will
curtail prevalence of homicide in those tracts.
Hypothesis 2: Census tracts with a high density of religious institutions will
have lower homicide counts even when experiencing loss of manufacturing
establishments.
Technique & Models
Table 2. Negative Binomial Regression Models Explaining Homicides Across 361
Philadelphia Census Tracts, 1980 & 1990
300
COUNT
200
100
0
.00
2.00
1.00
4.00
3.00
6.00
5.00
8.00
7.00
10.00
9.00
13.00
HOMICIDE 1980
Variable
RELINST
MANFCT
CMANFCT
RELINST x MANFCT
RELINST x CMANFCT
BAR
TEEN
BLACK
HISPANIC
DISTRESS
RENT
POPULATION (offset)
Constant
Model Fit (LRχ2)
300
COUNT
200
100
0
.00
2.00
1.00
4.00
3.00
6.00
5.00
8.00
7.00
HOMICIDE 1990
10.00
9.00
13.00
11.00
16.00
1980
Model 1
0.996
1.0453
---------0.949
0.9183
1.0113
1.0273
1.8653
1.006.051
----9.1873
180.9;
p = .0000
.174
.417
Model 2
1.025
1.0613
---0.9941
---0.950
0.9193
1.0113
1.0283
1.8243
1.0061
----9.3283
186.1;
p =.0000
.179
.427
Model 3
1.021
1.014
---0.996
---1.1521
1.011
1.0193
1.0183
1.7133
1.0082
----10.2313
175.1;
p =.0000
.157
.403
1990
Model 4
1.004
0.9461
1.1483
------1.084
1.016
1.0173
1.0163
1.7703
1.006.053
----9.9063
189.6;
p =.0000
.170
.428
Model 5
1.025
0.9402
1.2103
---0.9891
1.111
1.016
1.0173
1.0163
1.7883
1.005
----9.9633
195.0;
p =.0000
.175
.437
McFadden's R2
Cragg & Uhler’s R2
Notes:
Model 1 excludes interaction term between # religious institutions & # manufacturing establishments;
Model 2 includes interaction term between # religious institutions & # manufacturing establishments;
Model 3 includes interaction term between # religious institutions & # manufacturing establishments;
Model 4 includes change in # manufacturing establishments but no interaction term;
Model 5 includes change in # manufacturing establishments & interaction term between change in # of
manufacturing establishments and # of religious institutions;
Model 6 includes change in # manufacturing establishments & interaction term between change in # of
manufacturing establishments and # of religious institutions.
Except for constants, all coefficients are exponentiated.
Significance levels indicated by 1 = p = .05; 2 = p = .01; 3 = p = .001
Direction of effect of change in # of manufacturing establishments (CMANFCT) reversed so that larger
values indicate greater loss between 1980 & 1990.
Pooled model uses robust standard errors to adjust for clustering within tracts.
Tests for overdispersion indicate Poisson regressions fail to meet mean-variance equality assumption.
Collinearity, residual, and influential case diagnostics indicate no major problems.
Pooled
Model 6
1.0241
0.978
1.1893
---0.9883
1.034
0.9641
1.0183
1.0213
1.7333
1.005
----9.6023
370.1;
p =.0000
.171
.422
Mapping & Spatial Analysis
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Camden
Camden
Manufacturing Loss.
Distribution of African Americans.
Levels of Distress.
Gloucester
t-Values for # Religious Institutions
Residuals for Homicides
EVALUATION
• Survey of Law Enforcement Agencies
– Crime Mapping Needs, Software Applications
• Evaluation of GIS & other Spatial
Technologies
– What has made the greatest difference?
– What is the level of impact on law enforcement
organizations and communities?
– What & which software works best?
• Spatial Analysis of Policy Implementaion
Violent Crime in Wilmington, NC
• Crime Conditions
– Increase in level of crime during a time when crime in most
other areas was in decline.
– High levels of violent crime concentrated in and around
public housing communities. Approximately 2.7% of the
Wilmington’s population live in public housing communities.
– During six month period, late 1989 to early 1999, over 60%
of assaults, robberies and homicides occurred in public
housing or within a one block radius.
– The use and distribution of drugs have produced several
open-air corner drug markets on the periphery, in particular
with two communities near the central downtown area.
Strategy in Wilmington, NC
• Identify a specific problem.
– Violent crime in and around public housing communities.
• Gather data & information on the problem.
– Use of crime reports, police investigation reports and
observations.
– Police and other criminal justice officials assume that much of
problem is due to a few serious violent offenders.
– Systematic violence is associated with drug distribution.
• Form interagency task force.
– Includes local, state and federal law enforcement, parole &
probation, state & federal prosecutors, social services, and
community leaders.
Target Areas in Wilmington, N.C.
t-Test Results
Negative direction.
Paired Samples Test for Violent Crimes
Paired Differences
Pair 1
Pair 2
Pair 3
Pair 4
PREPROP - POSTPROP
PREONE - POSTONE
PRETWO - POSTTWO
PRETHREE - POSTTHRE
Mean
-11.6459
15.0321
-4.0305
1.7940
Std. Deviation
13.15226
34.76465
12.32184
8.56482
Std. Error
Mean
5.88187
15.54723
5.51050
3.83030
95% Confidence
Interval of the
Difference
Lower
Upper
-27.9766
4.6848
-28.1340
58.1981
-19.3300
11.2691
-8.8407
12.4286
t
-1.980
.967
-.731
.468
df
4
4
4
4
Sig. (2-tailed)
.119
.388
.505
.664
Paired Samples Test for Drug Crimes
Paired Differences
Pair 1
Pair 2
Pair 3
Pair 4
PREPROP - POSTPROP
PREONE - POSTONE
PRETWO - POSTTWO
PRETHREE - POSTTHRE
Mean
6.6460
11.8100
1.9400
3.7340
Std. Deviation
12.39243
37.36317
5.24679
8.64567
Std. Error
Mean
5.54207
16.70932
2.34644
3.86646
95% Confidence
Interval of the
Difference
Lower
Upper
-8.7412
22.0332
-34.5825
58.2025
-4.5748
8.4548
-7.0010
14.4690
Nothing is significant.
t
1.199
.707
.827
.966
df
4
4
4
4
Sig. (2-tailed)
.297
.519
.455
.389
Pre-Intervention Violent Crime
Post-Intervention Violent Crime
Change at Community Scale
Percent Change In Violent Crime in Public
Housing & Surrounding Area
0.20
0.15
Property
0.05
Ring One
Ring Two
n
la
tio
ry
W
ea
p
D
om
on
sV
io
be
Ro
b
e
Ra
p
se
es
t
ic
Fe
m
n
to
A
ss
au
l
-0.10
A
bu
al
e
t
A
ss
au
l
-0.05
de
r
0.00
M
ur
Percent Change
0.10
-0.15
-0.20
Type
Ring Three
GW Regression Models
Significance of Renting
Model 3
Parameter
Intercept
PubHous
PCT_MALE
PCT_BLAC
PCT_22/27
PCT_RENT
PCT_FFH_
Model 4
Estimate
-6.154
-0.102
0.021
0.02
0
0.017
-0.009
Std Err
0.16
0.103
0.003
0.001
0.003
0.002
0.006
T-Value
-38.482
-0.994
6.913
22.687
0.113
10.911
-1.468
Exp(B)
0.002
0.903
1.022
1.02
1
1.017
0.991
Sd(Exp(B))
0
0.093
0.003
0.001
0.003
0.002
0.006
Parameter
Intercept
PubHous
PCT_MALE
PCT_BLAC
PCT_22/27
PCT_RENT
PCT_FFH_
UnivProp
Estimate
-6.159
-0.124
0.021
0.019
0.004
0.018
-0.009
-0.489
Std Err
0.158
0.103
0.003
0.001
0.003
0.002
0.006
0.1
T-Value
-38.873
-1.204
6.996
22.245
1.321
11.558
-1.569
-4.878
Exp(B)
0.002
0.884
1.021
1.019
1.004
1.018
0.991
0.613
Sd(Exp(B))
0
0.091
0.003
0.001
0.003
0.002
0.006
0.061
Local Spatial Variation; Model 4
Controlling for:
- Public Housing
- % Renting
- % Female Headed
Household with
1 or More Children
- % African American
- % Age 22 to 27
- % Male
- University Property
DEVELOPMENT
• Archive of geocoded crime statistics.
• Training programs for mapping and spatial
analysis of crime.
• Mapping for corrections and other criminal
justice applications .
• Analytic and visualization software.
• Spatial Data Warehousing.
A Spatial Data Warehousing
• Pools data into one source.
• Data has been converted to share coordinate space
with neighbors.
• Data has been preserved in original format and space.
• Requests for data can be controlled and streamlined.
• Access to data by all organizations can be made easier.
• Data is available for analysis of regional events.
• Provides very small jurisdictions a place to deposit
their data due to very limited budgets.
GRASP Pilot Project
• Geospatial Repository for Analysis & Safety Planning
• Is a web-based, spatial data repository for the purpose
of storing data relevant to public safety.
• Is a central repository that focuses on specific regions
and issues surrounding the use of regional spatial data
for analysis purposes.
• Reduce the amount of time and effort to amass spatial
data for responding to needs.
• Developed in conjunction with the Systems & Info
Engineering Dept. of the University of Virginia.
GRASP Pilot Project
• Mission of GRASP is to provide for access to regional sets of
data for local & state, both public & private, authorities to
spatial data for analyzing and responding to regional events.
• Repository for regional spatial data and other data.
• Is for public, private and academic organizations:
– Public: Responding to regional events
– Private: Seeing beyond their environment.
– Academic: Research on regional phenomenon.
GRASP Pilot Project
Browsing Data
Visual Inspection of Data
Downloading Data
Adding & Updating Data
Adding & Updating Data
Adding & Updating Data
Data for Virginia is now available.
DISSEMINATION
• Have an annual Conference that draws
over 350 academics & practitioners
• Workshops for law enforcement agencies
• Produce hard & electronic publications
• Store both spatial & non-spatial data for
research & practical purposes
• Maintain a Web-Site for resources
• Run a listserv for users to discuss
pressing problems or ask for help
Conferences & Workshops
•
•
•
•
•
Crime Mapping Research Conference
Thematic Mapping Principals
Spatial Databases & Warehouses
Crime Mapping Tutorial
National Law Enforcement & Corrections
Training Centers
– Crime Mapping
– Geographic Profiling
– Mapping for Managers
Publications & Data
• Publications
– Privacy in the Information Age
– Crime Mapping Case Studies
– Forthcoming Report on Software Issues
– Forthcoming Book on ‘Hot Spots’
• Data
– Regional Public Safety Spatial Data
– Corrected Uniform Crime Reports Data
– Data Used in Grants at ICPSR
MAPS Web Site @ www.ojp.usdoj.gov/nij/maps
CrimeMap Listserv
• Over 600 Subscribers
• Topics Include:
– Technical
•
•
•
CrimeStat II Data Export Problems
Crime Mapping on the Web
Advice on GIS Structure and editing
– Analytical
•
•
•
Estimating Population for Police Beats
Plotting Time/Date Series
Runaways and Auto Theft
http://puborder.ncjrs.org/listservs/subscribe_Crimemap.asp
Conference This Year
For More Information...
Mapping & Analysis for Public Safety
National Institute of Justice
810 7th Street, NW
Washington, DC 20531
(202) 616-4531
fax (202) 616-0275
email: maps@ojp.usdoj.gov
URL: www.ojp.usdoj.gov/nij/maps