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

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.

Data ProDES: All cases of youths committed to community-base programs from 1996-2004. 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

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.

Race and Philadelphia

Juvenile Social Problems in Philadelphia

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)

Red = High Yellow = Medium Blue = Low

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

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

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

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

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

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

Major Program Locations Residence and Recidivism Hotspots

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

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.

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

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 1996-1998 first cases for six years Use more refined offense classifications