Geospatial-temporal analysis andclassification of criminal data in Manila

Maria Jeseca C. Baculo, Charlie S. Marzan, Remedios De Dios Bulos, Conrado Ruiz

Research output: Book chapterConference contributionpeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6-11
Number of pages6
ISBN (Electronic)9781538620304
DOIs
Publication statusPublished - 4 Dec 2017
Externally publishedYes
Event2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 - Beijing, China
Duration: 8 Sept 201711 Sept 2017

Publication series

Name2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Volume2017-January

Conference

Conference2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Country/TerritoryChina
CityBeijing
Period8/09/1711/09/17

Keywords

  • Classifiers
  • Crime analysis
  • Predictive methods

Fingerprint

Dive into the research topics of 'Geospatial-temporal analysis andclassification of criminal data in Manila'. Together they form a unique fingerprint.

Cite this