Mobile application based on KDD to predict high-crime areas and promote sustainability in citizen security in a district of Lima-Perú

Mobile application based on KDD to predict high-crime areas and promote sustainability in citizen security in a district of Lima-Perú

Metropolitan Lima faces a serious citizen security situation, reflected in high rates of crime and violence in several districts. The development of a mobile application to identify and predict areas of high crime incidence is proposed. Using historical data of criminal incidents and reports registe...

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Bibliographic Details
Main Authors: Hugo Vega-Huerta, Javier Vilca Velasquez, Nicolas Anicama Espinoza, Gisella Luisa Elena Maquen-Niño, Luis Guerra-Grados, Jorge Pantoja-Collantes, Oscar Benito-Pacheco, Juan Carlos Lázaro-Guillermo, Adegundo Camara-Figueroa, Javier Cabrera-Díaz, Rubén Gil-Calvo, Frida López-Córdova
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Computer Science
Subjects:
heat maps
machine learning algorithms
criminal acts
citizen security
CRISP-DM
Online Access:https://www.frontiersin.org/articles/10.3389/fcomp.2025.1585632/full
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https://www.frontiersin.org/articles/10.3389/fcomp.2025.1585632/full

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