Community Monitoring for Youth Violence Surveillance: Testing a Prediction Model

Abstract

Predictive epidemiology is an embryonic field that involves developing informative signatures for disorder and tracking them using surveillance methods. Through such efforts assistance can be provided to the planning and implementation of preventive interventions. Believing that certain minor crimes indicative of gang activity are informative signatures for the emergence of serious youth violence in communities, in this study we aim to predict outbreaks of violence in neighborhoods from pre-existing levels and changes in reports of minor offenses. We develop a prediction equation that uses publicly available neighborhood-level data on disorderly conduct, vandalism, and weapons violations to predict neighborhoods likely to have increases in serious violent crime. Data for this study were taken from the Chicago Police Department ClearMap reporting system, which provided data on index and non-index crimes for each of the 844 Chicago census tracts. Data were available in three month segments for a single year (fall 2009, winter, spring, and summer 2010). Predicted change in aggravated battery and overall violent crime correlated significantly with actual change. The model was evaluated by comparing alternative models using randomly selected training and test samples, producing favorable results with reference to overfitting, seasonal variation, and spatial autocorrelation. A prediction equation based on winter and spring levels of the predictors had area under the curve ranging from .65 to .71 for aggravated battery, and .58 to .69 for overall violent crime. We discuss future development of such a model and its potential usefulness in violence prevention and community policing.