Abstract
Crime prediction is challenging, especially homicide predictions due to the low-frequency and spatial sparsity of the occurrences. In this work, we made use of Laplacian of Gaussian spectral filtering on graph signals to create sequential features to predict weekly homicides. We also applied DeepWalk to take advantage of the graph structure. We compared several pairs of models where one included the graph sequential features and one that did not. We observed a significant improvement in the models that used said features. The best model was a logistic regression with L1 regularization (Lasso) and it captured up to 33% of the homicides when assigning 10% of the city as hotspots.
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.
Criminal, Contraventional and Operating Information System.
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Moreno, J., Quintero, S., Riascos, A., Nonato, L.G., Sanchez, C. (2021). Homicide Prediction Using Sequential Features from Graph Signal Processing. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-80129-8_54
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