Prediction of Crime Patterns using the Spatio-Temporal feature relations

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Primary Lattu_umbc_0434M_11694.pdf (740.78 KB)

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http://hdl.handle.net/11603/15513

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UMBC Theses and Dissertations

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Author/Creator ORCID

Date

2017-01-01

Type of Work

theses
Text

Department

Computer Science and Electrical Engineering

Program

Computer Science

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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
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Abstract

There are many openly available Crime datasets provided by the government agencies. This theses work attempts to learn the spatiotemporal relations between the different features from the Baltimore Crime and Arrests dataset respectively. The goal is to make a comparative study by exploring different Machine Learning algorithms and finding the patterns that emerge from these learning models. The features to be predicted from the Crime dataset are Crime Code and Premise and the features to be predicted from the Arrest dataset are the Incident Offence and the Arrest Location. These analyses can help us in understanding the effectiveness of using Machine Learning techniques when it comes to finding the patterns related to Criminology.