Using Decision Trees to Predict Crime Reporting

Authors

Gondy Leroy, Claremont Graduate UniversityFollow
Juliette Gutierrez '13, Claremont Graduate University

Student Co-author

CGU Graduate

Document Type

Book Chapter

Department

Information Systems and Technology (CGU)

Publication Date

2008

Disciplines

Databases and Information Systems | Management Information Systems

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.

Rights Information

© 2008 IGI Global

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

DOI

10.4018/978-1-60566-172-8.ch008

Recommended Citation

J. Gutierez and G. Leroy, "Using Decision Trees to Predict Crime Reporting," in Advanced Principles for Improving Database Design, Systems Modeling, and Software Development, Eds, J. Erickson and K. Siau, IGI Global, p. 132 - 145, November, 2008. (ISBN-13: 9781605661728)