Predspot: predicting crime hotspots with machine learning

• Ano de defesa: 2019
• Autor(a) principal: Araújo Júnior, Adelson Dias de
• Orientador(a): Cacho, Nélio Alessandro Azevedo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
• País: Brasil
• Palavras-chave em Português: Predictive policing; Hotspot prediction; Machine learning; Crime forecasting
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/29155

Smart cities are increasingly adopting data infrastructure and analysis to improve the decision-making process for public safety issues. Although traditional hotspot policing methods have shown benefits in reducing crime, previous studies suggest that the adoption of predictive techniques can produce more accurate estimates for future crime concentration. In previous work, we proposed a framework to generate near-future hotspots using spatiotemporal features. In this work, we redesign the framework to support (i) the widely used crime mapping method kernel density estimation (KDE); (ii) geographic feature extraction with data from OpenStreetMap; (iii) feature selection, and; (iv) gradient boosting regression. Furthermore, we have provided an open-source implementation of the framework to support efficient hotspot prediction for police departments that cannot afford proprietary solutions. To evaluate the framework, we consider data from two cities, namely Natal (Brazil) and Boston (US), comprising twelve crime scenarios. We take as baseline the common police prediction methodology also employed in Natal. Results indicate that our predictive approach estimates hotspots 1.6-3.1 times better than the baseline, depending on the crime mapping method and machine learning algorithm used. From a feature importance analysis, we found that features from trend and seasonality were the most essential components to achieve better predictions.