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 !( !( !( !( !( !( !( !( !( !( !( !( Montgomery !( (! !( (! !( !( !( !( !( (! !( !( (!!( (! (! 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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