AUTHOR=Vega-Huerta Hugo , Vilca Velasquez Javier , Anicama Espinoza Nicolas , Maquen-Niño Gisella Luisa Elena , Guerra-Grados Luis , Pantoja-Collantes Jorge , Benito-Pacheco Oscar , Lázaro-Guillermo Juan Carlos , Camara-Figueroa Adegundo , Cabrera-Díaz Javier , Gil-Calvo Rubén , López-Córdova Frida 

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

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1585632

DOI=10.3389/fcomp.2025.1585632

ISSN=2624-9898

ABSTRACT=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 registered by users in the application, models capable of predicting the occurrence of crimes in real time are trained. The data mining process follows the KDD methodology, which includes the stages of selection, preprocessing, transformation, data mining, evaluation and knowledge consolidation. Machine learning algorithms, such as Random Forest and Gradient Boosting, were used to make these predictions. Visualization techniques, such as heat maps, were also used to represent crime events and facilitate their understanding by users. The results show an accuracy of 88% for the Random Forest algorithm and 91% for Gradient Boosting in predicting the occurrence of crimes, which demonstrates the effectiveness of machine learning models to improve citizen security in Metropolitan Lima, therefore these findings have significant implications for crime prevention and suggest that the application of these technologies can be fundamental to address security challenges in the city.