1. Juvenile Recidivism: Kids, Environments and Programs
Phil Harris, Dept. of Criminal Justice, Temple University

2. Investigating the Simultaneous Effects of Individual, Program and Neighborhood Attributes on Juvenile Recidivism Using GIS and Spatial Data Mining
Who: Philip Harris, Jeremy Mennis, Zoran Obradovic, Alan Izenman, Heidi Grunwald, Brian Lockwood, Joe Jupin and Laura Chisholm (all from Temple)
Research Question: How do individual, spatial and program attributes jointly predict juvenile re-offending?
Methods?: Hot Spot Analysis, Cross-classified HLM, Neural Network Analysis, Co-Clustering (PLAID), Geographically- Weighted Regression, Spatial Regression, Mapping of Results
Presentation will focus on findings, with emphasis on usefulness of spatial analysis.

3. Data
ProDES: All cases of youths committed to community-base programs from Data derived from juvenile court records, programs and kids. All residents geocoded.
PHMC Data: Resident survey data subdivided by natural neighborhoods
Police Data: Locations of Part I offenses
Probation and Parole Data: Residences of person on probation or parole
Census Data: Economic and Social attributes at block level

4. Philadelphia High levels of racial and ethnic segregation.
Our findings will emphasize the neighborhood you see highlighted in dark gray. This is a Hispanic neighborhood that comprises a well-developed population largely from Puerto Rico. It is isolated from the rest of the city culturally and in terms of participation in the city’s economy. It sits in the center of an area that has been called the Badlands and that is often the focus when social problems are discussed by policy makers.

5. Race and Philadelphia

6. Juvenile Social Problems in Philadelphia

7. Home Addresses of All Delinquent Youths in Community-Based Programs, 1996 - 2004
Recidivism Rate* within 100 m of Residence
Red = High
Yellow = Medium
Green = Low
Black = Recidivist
* (#of instances with recidivism)/(total # of instances)

9. Red = High
Yellow = Medium
Blue = Low

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

11. Local Probability Clustering: Drug Offending and Ethnicity
To further investigate the influence of the contagion variables, we compared maps of the local spatial clustering of probability of recidivism from models 2 and 4 for each outcome variable.
Gettis –Ord i statistic used to generate Local spatial clusters of probability from the models produced by the logistic regression analyses.
Based on Model 2, which is derived from individual-level attributes, drug offending appears to be almost perfectly matched to a largely Hispanic neighborhood.
Model with Individual Predictors Only

12. Comparison of Clustering of Outcome Variables
Person Offense Recidivism
Drug Offense Recidivism
Individual AND Spatial Predictors

13. Program Predictors of Non-Recidivism
Strongest Predictors
Drug Offense Recidivism
Person Offense Recidivism
Older than average
Hispanic
Prior residential placement
Prior drug offense
Number of prior arrests
High area drug recidivism rate
Younger than average
Non-Hispanic
Parental crime
Number of prior arrests
Prior person offense
High area person recidivism rate
Program Predictors of Non-Recidivism
Small program client capacity
Cognitive-behavioral program
goals
Small program client capacity
Substance Abuse Treatment
Group Counseling

14. Race Effect? Co-Cluster 1 Over 90% Black; Avg. for city is 65%
Red=High Prevalence
Blue=Low Prevalence
Black=Average
Co-Cluster 1
Over 90% Black; Avg. for city is 65%
62% Completed High School, compared to Philadelphia average of 59%
Avg. age of Juvenile is 15.5, same as rest of City
Recidivism rate is 35% compared to average of 42%, i.e. low probability

15. Race Effect? Co-Cluster 1 Over 90% Black; Avg. for city is 65%
Red=High Prevalence
Blue=Low Prevalence
Black=Average
Co-Cluster 1
Over 90% Black; Avg. for city is 65%
62% Completed High School, compared to Philadelphia average of 59%
Avg. age of Juvenile is 15.5, same as rest of City
Recidivism rate is 35% compared to average of 42%, i.e. low probability

16. Major Program Locations
Residence and Recidivism Hotspots

17. Major Recidivism Findings
High and low rates of recidivism are concentrated in certain areas of the city
Type of recidivism offense is also spatially concentrated
Predictors of re-offense type differ, with neighborhood having greatest effect on drug selling
Strongest predictor of program failure was program size – large programs associated with high recidivism
Drug offenders receiving cognitive-behavioral interventions had low recidivism rates; substance abuse treatment did not predict recidivism
Substance abuse programs associated with reduced recidivism among violent offenders. Focus on self control and self efficacy. Similar finding reported by Reclaiming Futures.
Impact of Race on recidivism may be countered by education level of neighborhood residents

18. Conclusions
Neighborhood forces not only contribute to recidivism, they appear to influence offense type
Drug sellers likely to specialize and to be located in areas where drug selling is encouraged and rewarded
Violence is not necessarily related to drug markets; look for subcultures of violence
The nature of risk and protective factors can vary from neighborhood to neighborhood
Don’t assume that black neighborhoods are high crime neighborhoods. Found one such area with a below average recidivism rate. The difference? A very high rate of residents with high school diplomas. Implies effect of prosocial adults.

19. Implications for Programs
Programs should be located where concentrations of repeat offenders are found – maximize family and community engagement
Programs should be tailored to the needs of kids, patterns of juvenile and adult offending found in neighborhoods, economic pressures in neighborhoods, informal methods of social controls in neighborhoods, and dominant cultures of neighborhoods
Program intervention methods should be matched to likely causes of offending. Causal analysis should include environmental forces such as isolation and deprivation, opportunity and reward, and disorganization and lack of collective efficacy

20. Future Research
Social Networks: How does behavior of youths in network affect individual behavior?
ProDES kids can be linked in four ways:
Siblings, co-offenders, neighbors, participants in same program at same time
Impact of co-offender attributes, including distance between residences
Test hypothesis of offense specialization with longitudinal data – expect for drug sellers but not others
Follow first cases for six years
Use more refined offense classifications