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Using Decision Trees to Predict Crime Reporting

Using Decision Trees to Predict Crime Reporting

Juliette Gutierrez
Copyright: © 2009 |Pages: 14
ISBN13: 9781605661728|ISBN10: 1605661724|ISBN13 Softcover: 9781616926038|EISBN13: 9781605661735
DOI: 10.4018/978-1-60566-172-8.ch008
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MLA

Gutierrez, Juliette. "Using Decision Trees to Predict Crime Reporting." Advanced Principles for Improving Database Design, Systems Modeling, and Software Development, edited by Keng Siau and John Erickson, IGI Global, 2009, pp. 132-145. https://doi.org/10.4018/978-1-60566-172-8.ch008

APA

Gutierrez, J. (2009). Using Decision Trees to Predict Crime Reporting. In K. Siau & J. Erickson (Eds.), 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

Chicago

Gutierrez, Juliette. "Using Decision Trees to Predict Crime Reporting." In Advanced Principles for Improving Database Design, Systems Modeling, and Software Development, edited by Keng Siau and John Erickson, 132-145. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-172-8.ch008

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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.

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