Spatial Clustering in Juvenile Delinquency and Recidivism in Philadelphia, Pennsylvania Jeremy Mennis, Phil Harris, Zoran Obradovic, Alan Izenman, Heidi.

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Presentation transcript:

Spatial Clustering in Juvenile Delinquency and Recidivism in Philadelphia, Pennsylvania Jeremy Mennis, Phil Harris, Zoran Obradovic, Alan Izenman, Heidi Grunwald, Yilian Qin, Joseph Jupin, Brian Lockwood Temple University Association of American GeographersApril 2007

A Question What happens when a criminologist, a geographer, a statistician, a computer scientist, and a higher education administrator get together to do interdisciplinary research on juvenile delinquency?

Observations… 1.Adolescent development and behavior can be supported and hampered by environmental forces. 2.Aftercare services must address youths’ developmental needs, which may be aggravated by external forces that compete with program effects.

Research Objectives To develop, apply, and evaluate improved techniques to investigate the simultaneous effects of individual, program, and neighborhood forces in preventing juvenile recidivism. A case study focusing on adjudicated juvenile delinquents assigned to court-ordered programs by the Family Court of Philadelphia, Pennsylvania, during the years 1996 to 2003.

Program Development and Evaluation System (ProDES) 11,659 male cases

Cases and Juveniles by Tract

Number of Cases Per 100 Juveniles by Tract

Recidivating Cases

Recidivism Rate by Tract

Combinations of Classed Delinquency and Recidivism

Cases: Recidivating and Non-Recidivating

Modeling Recidivism Rate as a Surface For each case, rate is calculated over 100 nearest neighbors. Calculation accounts for major barriers such as rivers and parks. Each case location is considered a sample of the surface and entered into Getis G i *.

Clustering of Any Recidivism

Clustering of Person Offense Recidivism

Clustering of Recidivism with Removal from the Community

Comparison of Clustering of Outcome Variables Any Recidivism Person Offense Recidivism Removal from the Community

Onward! Completed data cleaning, dimensionality reduction. Integrating data on programs, socio-economic status, neighborhood collective efficacy, and crime Investigate spatial non-stationarity in models of the target variables. Evaluate HLM and spatial econometric modeling for predicting target variables.

This research is supported by a grant from the National Institute of Justice. Jeremy astro.temple.edu/~jmennis Enjoy San Francisco! Jeremy Phil Zoran Alan Heidi

Program Design Inventory (PDI) 43 programs for boys

Recidivism by Program

Philadelphia Health Management Corporation (PHMC) 45 neighbor- hoods

U.S. Burueau of the Census 365 tracts

Challenges Data Integration High-Dimensional and Noisy Data Spatial Dependency and Heterogeneity

Research Design Data Preparation Data Exploration Model Construction, Evaluation, and Interpretation