Geospatial-temporal analysis and classification of criminal data in Manila

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017

Volume

2017-January

First Page

6

Last Page

11

Publication Date

12-4-2017

Abstract

The use of technology on criminal data has proven to be a valuable tool in forecasting criminal activity. Crime prediction is one of the approaches that help reduce and deter crimes. In this paper, we perform geospatial analysis using the kernel density estimation in ArcGIS 10 to identify the spatiotemporal hotspots in Manila, the most densely populated city in the Philippines. We also compared the performance measures of the BayesNet, Naïve Bayes, J48, Decision Stump, and Random Forest classifiers in predicting possible crime activities. The results presented in this paper aim to provide insights on crime patterns as well as help law enforcement agencies design and implement approaches to respond to criminal activities. © 2017 IEEE.

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Digitial Object Identifier (DOI)

10.1109/CIAPP.2017.8167050

Disciplines

Computer Sciences

Keywords

Crime analysis--Philippines; Crime forecasting--Philippines; Geospatial data—Computer processing

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