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Predictive Modeling Combining Short and Long-Term Crime Risk Potential, Final Report

NCJ Number
Date Published
June 2016
131 pages
This project developed a technology capable of predicting future crime-risk potential based on a number of theoretical approaches for understanding localized spatial crime patterns.
The project had three goals. First, it aimed to determine the way fundamental demographic correlates of crime are linked to next year's crime levels, even after controlling for this year's crime levels. Second, the study examined the role of near-repeat crime events that are indicative of a short-term change in relative risk of crime. Third, it developed a computer program that allows for crime predictions based on the theoretical approaches presented. The software is intended for use by cities and jurisdictions across the United States. The project used crime data and Census information for the City of Philadelphia, PA. For four crime types (robbery, aggravated assault, burglary, and motor vehicle theft), a model that included demographic structure and earlier crime from the previous year provided by far the strongest combination of accuracy and parsimony. Lower volume crime types (homicide and rape) were also predicted as well as, or better than, the other four crime types in using the demographics-only model. A model that combines community structural characteristics, crime counts from the previous year, and an estimate of near-repeat activity produced the best results overall. This indicates that small-scale, short-term crime occurrences reflect a complex mix of near-term crime continuities, ecological crime continuities, and ecological structure. Work remains to be done in identifying the processes that maintain these ecological crime continuities, as well as the processes that generate the unfolding ecological discontinuities. The authors note that the processes described ignore offender characteristics, such as race, while focusing on locations of criminal victimization. 7 figures, 6 tables, and approximately 100 references

Date Published: June 1, 2016