Predicting crime reporting with decision trees and the national crime victimization survey

Juliette Gutierrez, Gondy Leroy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Crime reports are used by law enforcement to find criminals, prevent further violations, identify problems causing crimes and allocate government resources. Unfortunately, many crimes go unreported. This may lead to an incorrect crime picture and suboptimal responses to the existing situation. Our goal is to use a data mining approach to increase understanding of when crime is reported or not. An increased understanding could lead to new, more effective programs to fight crime or changes to existing programs. We use the National Crime Victimization Survey (NCVS) which comprises data collected from 45,000 households about incidents, victims, suspects and if the incident was reported or not. We use decision trees to predict when incidents are reported or not. We compare decision trees that are built based on domain knowledge with those automatically created. For the automatically created trees, we compare three variable selection methods: two filters, Chi-squared and Cramer's V Coefficient, and a forward selection wrapper. We found that the decision trees that are automatically constructed are as accurate as those based on domain knowledge while they show a different picture. We conclude that decision trees lead to several new hypotheses for criminologists while they are automatically constructed and easy to understand which makes them practical and useful.

Original languageEnglish (US)
Title of host publicationAssociation for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007
Subtitle of host publicationReaching New Heights
Pages586-595
Number of pages10
StatePublished - Dec 1 2007
Externally publishedYes
Event13th Americas Conference on Information Systems, AMCIS 2007 - Keystone, CO, United States
Duration: Aug 10 2007Aug 12 2007

Publication series

NameAssociation for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007: Reaching New Heights
Volume1

Other

Other13th Americas Conference on Information Systems, AMCIS 2007
CountryUnited States
CityKeystone, CO
Period8/10/078/12/07

Keywords

  • Crime reporting
  • Data mining
  • Decision trees
  • Filters
  • Law enforcement
  • National crime victimization survey
  • Wrappers

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems
  • Library and Information Sciences

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  • Cite this

    Gutierrez, J., & Leroy, G. (2007). Predicting crime reporting with decision trees and the national crime victimization survey. In Association for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007: Reaching New Heights (pp. 586-595). (Association for Information Systems - 13th Americas Conference on Information Systems, AMCIS 2007: Reaching New Heights; Vol. 1).