Using decision trees to predict crime reporting

Juliette Gutierrez, Gondy Leroy

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

Crime reports are used to find criminals, prevent further violations, identify problems causing crimes and allocate government resources. Unfortunately, many crimes go unreported. The National Crime Victimization Survey (NCVS) comprises data about incidents, victims, suspects and if the incident was reported or not. Current research using the NCVS is limited to statistical techniques resulting in a limited 'view' of the data. Our goal is to use decision trees to predict when crime is reported or not. We compare decision trees that are built based on domain knowledge with those created with three variable selection methods. We conclude that using decision trees leads to the discovery of several new variables to research further.

Original languageEnglish (US)
Title of host publicationAdvanced Principles for Improving Database Design, Systems Modeling, and Software Development
PublisherIGI Global
Pages132-145
Number of pages14
ISBN (Print)9781605661728
DOIs
StatePublished - Dec 1 2008
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences(all)

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  • Cite this

    Gutierrez, J., & Leroy, G. (2008). Using decision trees to predict crime reporting. In Advanced Principles for Improving Database Design, Systems Modeling, and Software Development (pp. 132-145). IGI Global. https://doi.org/10.4018/978-1-60566-172-8.ch008