Detection and Prediction of Geographical Changes in Crime Rates, Executive Summary

• NCJ Number: 202933
• Author(s): Peter Rogerson; Rajan Batta; Christopher M. Rump
• Date Published: 2003
• Length: 10 pages
• Grant Number(s): 98-IJ-CX-K008
• Sponsoring Agency: National Institute of Justice (NIJ)
• Corporate Author: New York State University at Buffalo
• Sale Source: National Institute of Justice/NCJRS; NCJRS Photocopy Services
• Publication Format: Document
• Publication Type: Report (Study/Research)
• Language: English
• Country: United States of America

Annotation

This is the executive summary of a study that developed statistical methods and monitoring models for the quick detection of emerging and declining geographic clusters of criminal activity; a second related objective was to develop prediction models that forecast crime patterns (geographic displacement) in response to the deployment of resources.

Abstract

The focus of the study was the detection of clusters of criminal activity that occur in relation to some pre-existing expectations (e.g., previous year's data). The study also focused on the monitoring of crime data as soon as it becomes available, such that changes in geographic crime patterns can be detected as quickly as possible. One chapter of this report presents the details of the researchers' socioeconomic model of geographical displacement and spatial concentration of crime. It notes that there is mounting evidence that earlier assumptions about the displacement of crimes to other locations bordering the targeted geographic area may have been overstated. At the same time, there is increasing evidence of diffusion effects, whereby the benefits of enforcement policies in one area spread to other areas. The researchers studied particular police departments' situations and used their data to develop an appropriately structured model for crime analysis. The two departments were in Camden, NJ, and Philadelphia, PA. The micro-level component of the research developed a sequential decisionmaking model for assisting law enforcement officials in allocating resources during a crackdown operation on illicit drug markets. Results showed that using maximum enforcement for a significant number of days during a crackdown may be optimal in neighborhoods with a severe drug problem. A cyclical crackdown-backoff strategy may be optimal where residual deterrence dominates financial hardship. For all markets, a much quicker and less costly effort could be implemented if the daily enforcement intensity is increased. The model also provides guidelines for identifying markets where crackdowns would be ineffective in eliminating a drug market. This report also provides brief descriptions of the various contributions made by this research. 19 references