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NCJ Number: NCJ 205909     Find in a Library
Title: Robust Spatial Analysis of Rare Crimes, Executive Summary
Author(s): Avinash Singh Bhati
Corporate Author: The Urban Institute
United States of America
Date Published: 04/2004
Page Count: 9
Sponsoring Agency: National Institute of Justice
US Department of Justice
Office of Justice Programs
United States of America
Grant Number: 2002-IJ-CX-0006
Sale Source: The Urban Institute
2100 M Street, N.W.
Washington, DC 20037
United States of America

NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF 
Dataset: DATASET 1
Type: Report (Study/Research) ; Report (Summary)
Language: English
Country: United States of America
Annotation: This is the executive summary of the report on a project that developed an analytical approach for incorporating spatial error structures in models of rare crimes.
Abstract: In their examinations of the causes of violence, researchers are often faced with applying spatial econometric methods to models with discrete outcomes. There are no appropriate methods for doing this when the outcomes are measured at intra-city areal units. This research aimed to fill this gap. The framework developed was applied to a real-world empirical problem. It examined the socioeconomic and demographic determinants of disaggregate homicide rates at two intra-city levels of areal aggregation and compared inferences derived from several sets of models. The analysis was performed on disaggregated homicide counts (1989-91) recorded in Chicago's census tracts and neighborhood clusters through the use of explanatory factors obtained from census sources. An extension of the Generalized Cross Entropy (GCE) methods was applied to the data in an effort to use their flexibility in allowing error structures across space. An information-based measure was developed and used in selecting the hypothesized error structure that best approximated the true underlying structure. The findings confirmed that ignoring spatial structures in the regression residuals often leads to severely biased inferences and, hence, a poor foundation on which to base policy. Also, evidence was found of homicide type-specific and areal units-specific models, thus exposing the need to disaggregate violence into distinct types. Resource deprivation was a reliable predictor of all types of violence analyzed and at both levels of areal aggregation. In addition, there was apparently a spill-over effect of resource deprivation on the amount of violence expected in neighboring areas. This indicates the importance of taking into account the spatial structure in a sample when planning and implementing policy measures. The GCE approach used in this project provides several paths for future research, particularly in the analysis of rare crimes.
Main Term(s): Criminology
Index Term(s): Economic influences ; Statistical analysis ; Research methods ; Models ; Geographic distribution of crime ; Homicide causes ; Violence causes ; NIJ grant-related documents ; Illinois
Note: See NCJ-205910 for the full report.
   
  To cite this abstract, use the following link:
https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=205909

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