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NCJ Number: 155802 Add to Shopping cart Find in a Library
Title: Predicting Neighborhood Risk of Crime
Author(s): Adele Harrell; Caterina Gouvis
Corporate Author: The Urban Institute
United States of America
Date Published: 1994
Page Count: 51
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
The Urban Institute
Washington, DC 20037
US Dept of Justice NIJ Pub
Washington, DC 20531
Grant Number: 91-IJ-CX-K016
Sale Source: The Urban Institute
2100 M Street, N.W.
Washington, DC 20037
United States of America
Type: Survey
Language: English
Country: United States of America
Annotation: The predictive value of community decay indicators is explored.
Abstract: This study analyzes community decay indicators to assess whether records routinely maintained by public agencies can be used to identify areas of a city at risk of crime. Prediction models are based on a literature review which identified neighborhood characteristics associated with increased risk of crime. The models are tested using data from Washington, DC, and Cleveland, Ohio, selected because they could provide multiple indicators for multiple years between 1980 and 1990. Longitudinal models test the relationship of very high rates of aggravated assault, burglary, and robbery to neighborhood risk factors measured at earlier points in time. Because data were available for a number of time spans in the two cities, the results provide replicate tests to the extent to which the location of criminal activity can be predicted by these factors and provide comparisons of predictive models across periods of differing duration. The relationship of crime and neighborhood decay is discussed. The methodology of the study is provided in detail. The findings of the study underscore the need for continued work on identifying neighborhood risk of crime. The authors of this study urge researchers and law enforcement agencies to continue to develop and test procedures to locate areas at risk of crime and to make these findings accessible at the practical level to policymakers and planners. The models tested were limited by the indicators available at the Census tract level for Washington and Cleveland; therefore, future work should examine the effects of decisions and dynamics based outside of the Census tract on the nature of relational networks within the tract. Tables, references
Main Term(s): Crime prevention planning
Index Term(s): Crime patterns; Crime prediction; Criminology
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