Modeling Uncertainty in Crime Underreporting: An Indicator Kriging Approach - Jessé, Thiago Corrêa de Oliveira; Ranieri, Caeatano Mazzoni (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This study confronts crime underreporting in São Paulo, a critical challenge to public policy in emerging nations. We introduce a suitable geostatistical methodology to estimate crime probability from official police reports. The framework employs Indicator Kriging, a non-parametric method that excels with incomplete and missing data. It transforms crime counts into binary indicators based on hotspots, enabling robust probability estimation even in areas with sparse or no records. This approach effectively overcomes critical data gaps, generating detailed and accurate spatial probability maps of known criminal patterns. The resulting model offers a powerful analytical tool for targeted public security, revealing latent crime dynamics.