Abstract
State-of-the-art crime prediction models exploit the spatio-temporal clustering patterns and the self-exciting nature of criminality to predict vulnerable crime areas. However, omitting spatial covariates correlated with the occurrence of crimes potentially bias the estimated parameters. This research combines self-exciting point processes, generalized additive models and environmental attributes extracted through convolutional neural networks from street-level images to predict robbery hotspots across the locality of Chapinero in Bogota, Colombia. Our model using image features as covariates outperforms a standard self-exciting point process and shed light on the association between crime occurrence and the socioeconomic and environmental conditions of the city.
Results of the project “Diseño y validación de modelos de analítica predictiva de fenómenos de seguridad y convivencia para la toma de decisiones en Bogotá” funded by Colciencias with resources from the Sistema General de Regalías, BPIN 2016000100036. The opinions expressed are solely those of the authors.
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Notes
- 1.
We consider a robbery as the act of taking something from a person and using force, or the threat of force to do it. Hence, robbery is considered a theft with the use of violence.
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Dulce Rubio, M., Rodríguez Díaz, P., Moreno Pabón, J.S., Riascos, Á.J., Camargo, J.E. (2022). Self-exciting Point Processes with Image Features as Covariates for Robbery Modeling. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_58
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