Citation

Repeat Offenders and Events: Prediction and Analysis with Risk Terrain Modeling, Near Repeat Calculator, Social Network Analysis, and Neural Network Analysis

Abstract | Word Stems | Keywords | Association | Citation | Similar Titles



Abstract:

Using repeat offender, arrest, and incident data in three different U.S. cities, this presentation discusses the results of four different and useful analyses for predicting crime. Risk Terrain Modeling, developed by Caplan and Kennedy (2010), uses crime correlates and GIS to predict emerging micro hotspots of crime. Ratcliff’s Near Repeat Calculator relies on user defined spatial and temporal bandwidths to identify pairs of originating and repeating events and utilizes Monte Carlo Iterations to test for significant patterns. The Near Repeat Calculator is useful for analyzing certain types of crime including burglaries and robberies. Social Network Analysis can be utilized to better understand co-offending patterns in arrest data. Neural Networks, which are a type of ‘black box’ machine learning algorithms, can efficiently solve classification problems and have utility in analyzing crime. Findings suggest that these four techniques can paint a more complete picture of crime and can yield more accurate prediction models that can aid in improved resource deployment and ultimately increased crime prevention.
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Association:
Name: ACJS 55th Annual Meeting
URL:
http://http://www.acjs.org/


Citation:
URL: http://citation.allacademic.com/meta/p1347094_index.html
Direct Link:
HTML Code:

MLA Citation:

Paynich, Rebecca. "Repeat Offenders and Events: Prediction and Analysis with Risk Terrain Modeling, Near Repeat Calculator, Social Network Analysis, and Neural Network Analysis" Paper presented at the annual meeting of the ACJS 55th Annual Meeting, Hilton New Orleans Riverside, New Orleans, LA, Feb 13, 2018 <Not Available>. 2018-08-30 <http://citation.allacademic.com/meta/p1347094_index.html>

APA Citation:

Paynich, R. L. , 2018-02-13 "Repeat Offenders and Events: Prediction and Analysis with Risk Terrain Modeling, Near Repeat Calculator, Social Network Analysis, and Neural Network Analysis" Paper presented at the annual meeting of the ACJS 55th Annual Meeting, Hilton New Orleans Riverside, New Orleans, LA <Not Available>. 2018-08-30 from http://citation.allacademic.com/meta/p1347094_index.html

Publication Type: Paper Presentation
Review Method: Peer Reviewed
Abstract: Using repeat offender, arrest, and incident data in three different U.S. cities, this presentation discusses the results of four different and useful analyses for predicting crime. Risk Terrain Modeling, developed by Caplan and Kennedy (2010), uses crime correlates and GIS to predict emerging micro hotspots of crime. Ratcliff’s Near Repeat Calculator relies on user defined spatial and temporal bandwidths to identify pairs of originating and repeating events and utilizes Monte Carlo Iterations to test for significant patterns. The Near Repeat Calculator is useful for analyzing certain types of crime including burglaries and robberies. Social Network Analysis can be utilized to better understand co-offending patterns in arrest data. Neural Networks, which are a type of ‘black box’ machine learning algorithms, can efficiently solve classification problems and have utility in analyzing crime. Findings suggest that these four techniques can paint a more complete picture of crime and can yield more accurate prediction models that can aid in improved resource deployment and ultimately increased crime prevention.


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