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Association of American Geographers April 2007. 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. A Question.

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Spatial clustering in juvenile delinquency and recidivism in philadelphia pennsylvania

Association of American Geographers April 2007

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


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…

  • Adolescent development and behavior can be supported and hampered by environmental forces.

  • 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.









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 Gi*.





Comparison of Clustering of Outcome Variables

Person Offense Recidivism

Removal from the Community

Any Recidivism


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.


Alan

Phil

Zoran

Jeremy

Heidi

Enjoy San Francisco!

This research is supported by a grant from the National Institute of Justice.

Jeremy Mennis [email protected]

astro.temple.edu/~jmennis


Program Design Inventory (PDI)43 programs for boys






Challenges

  • Data Integration

  • High-Dimensional and Noisy Data

  • Spatial Dependency and Heterogeneity


Research Design

  • Data Preparation

  • Data Exploration

  • Model Construction, Evaluation, and Interpretation


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