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NCJ Number: NCJ 239861   Add to Shopping cart   Find in a Library
Title: Effects of Data Quality on Predictive Hotspot Mapping
Author(s): Timothy C. Hart Ph.D. ; Paul A. Zandbergen Ph.D.
Date Published: 09/2012
Page Count: 89
Sponsoring Agency: National Institute of Justice
US Department of Justice
Office of Justice Programs
United States of America
Grant Number: 2009-IJ-CX-0022
Sale Source: NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF 
Type: Research (Applied/Empirical)
Language: English
Country: United States of America
Annotation: This final NIJ technical report presents the results of a study examining the effects of data quality on predictive hotspot mapping.
Abstract: This study had three primary objectives: 1) determine the empirical descriptions of the quality of a range of typical geocoding techniques employed in crime mapping; 2) characterize the effects of data quality on the robustness of selected predictive crime hotspot mapping techniques; and 3) determine the effects of analysis method, crime type, urban morphology and parameter settings for predictive crime hotspot mapping techniques given a range of typical data quality parameters within the context of the accuracy and precision of hotspot prediction. The major finding from this study on the effects of data quality on predictive hotspot mapping was that geocoding quality was affected by variations in crime type as well as reference data used during the geocoding process. In addition, the study found that no single technique was more accurate than any other, and that the procedures used by analysts were highly influenced by many factors. Based on this finding, the authors of this study developed the following set of recommendations for use by researchers and analysts engaging in hotspot analysis: 1) assess the overall quality of input address information prior to geocoding; 2) disaggregate crime incidents and geocode like crime events separately; 3) tailor geocoding procedures to fit specific needs; 4) geocode to local street centerline reference data, if it is available; and 5) characterize positional accuracy prior to additional analysis. Data for this were obtained from the analysis of over 400,000 crime incident records from 6 large law enforcement jurisdictions in the United States. Study limitations are discussed. Figures, tables, references, and appendixes
Main Term(s): Crime Mapping
Index Term(s): Crime Rate ; Crime displacement ; Crime patterns ; Crime prediction ; Crime analysis ; Crime control theory ; Automated crime analysis
   
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https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=261934

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