Geospatial-temporal analysis andclassification of criminal data in Manila

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

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

14 Citas (Scopus)

Resumen

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 originalInglés
Título de la publicación alojada2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas6-11
Número de páginas6
ISBN (versión digital)9781538620304
DOI
EstadoPublicada - 4 dic 2017
Publicado de forma externa
Evento2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017 - Beijing, China
Duración: 8 sept 201711 sept 2017

Serie de la publicación

Nombre2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Volumen2017-January

Conferencia

Conferencia2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
País/TerritorioChina
CiudadBeijing
Período8/09/1711/09/17

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