Geospatial-temporal analysis andclassification of criminal data in Manila

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

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

14 Cites (Scopus)

Resum

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.

Idioma originalAnglès
Títol de la publicació2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
EditorInstitute of Electrical and Electronics Engineers Inc.
Pàgines6-11
Nombre de pàgines6
ISBN (electrònic)9781538620304
DOIs
Estat de la publicacióPublicada - 4 de des. 2017
Publicat externament
Esdeveniment2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 - Beijing, China
Durada: 8 de set. 201711 de set. 2017

Sèrie de publicacions

Nom2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Volum2017-January

Conferència

Conferència2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
País/TerritoriChina
CiutatBeijing
Període8/09/1711/09/17

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