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Analyzing Crime Patterns and Trends in the Washington, DC Metropolitan Area: Documenting the Value of Cross-Agency and Cross-Jurisdictional Data Integration

Award Information

Awardee
Award #
2012-R2-CX-0001
Funding Category
Competitive
Location
Status
Closed
Funding First Awarded
2012
Total funding (to date)
$689,507

Description of original award (Fiscal Year 2012, $689,507)

The purpose of this research is to conduct a comprehensive, cross-agency, and inter-jurisdictional pilot study to collect, integrate, and analyze crime and related demographic, land use, service delivery, and operational data across the greater Washington, DC, metropolitan area. The Urban Institute (UI) will integrate data from the District of Columbia, Prince George's County , Maryland, and Montgomery County, Maryland, to examine the following overarching research question: Does collecting and integrating data across jurisdictional boundaries, disciplines, and agencies inform analyses of a variety of phenomena that affect public safety, yielding actionable decisions based on the results of those analyses?

UI will assess the utility of the data integration effort for criminal justice practices and will document the resources, relationships, and skills that are required to accomplish cross-agency data integration on a regional scale. The specific analysis task will involve five (5) research questions: (1) what is the impact of gentrification and suburban diversification on crime within and across jurisdictional boundaries? (2) How does crime cluster along and around transportation networks and hubs in relation to other characteristics of the social and physical environment? (3) What is the distribution of criminal justice-supervised populations in relation to services they must access to fulfill their conditions of supervision? (4) What are the relationships among offenders, victims, and crimes across jurisdictional boundaries? (5) And what is the increased predictive power of simulation models that employ cross-jurisdictional data? UI will use Geographic Weighted Regression, Generalized Maximum Entropy (semi-parametric) methods, and spatial naïve Bayes modeling techniques. For the question regarding the predictive power of simulation models, UI will develop four (4) types of neighborhood risk assessments using GeoEye's Signature Analyst. The risk assessments will see if predictive power is increased when shifting from jurisdictional to cross-jurisdictional data, and with or without the inclusion of offender supervision data.

Data from the District of Columbia (DC) will be comprised of child welfare, crime, demographic, education, health, housing assistance, physical geographic boundaries, prisoner reentry, property transactions, public assistance, and vital statics. Data from Prince George's County will be comprised of crime, demographic, property transactions, physical geographic boundaries and prisoner reentry. Data from Montgomery County will include crime, demographic, property transactions, physical geographic boundaries, and prisoner reentry. For the question regarding the influence of transit hubs on crime, additional crime, demographic, physical geographic boundaries, and prisoner reentry for jurisdictions in Northern Virginia that contain the DC metropolitan transit system (WMATA) will be included. All datasets are largely from 2000-2011, respectively. ca/ncf

Date Created: August 23, 2012