Graph Restrictions for Signal Processing of Homicides Data

Soto, Sebastian Quintero; Pabón, Juan S. Moreno; Rubio, Mateo Dulce; Riascos, Álvaro; Nonato, Luís Gustavo

doi:10.1109/icapai49758.2021.9462063

2021 International Conference on Applied Artificial Intelligence (ICAPAI) 2021

DOI: 10.1109/icapai49758.2021.9462063

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Graph Restrictions for Signal Processing of Homicides Data

Sebastian Quintero Soto¹,

Juan S. Moreno Pabón²,

Mateo Dulce Rubio³

et al.

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2022

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Spatio Temporal Sparsity in Homicide Prediction Models

Riascos

Pabón²,

Rubio³

et al. 2022

IEEE Access

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Homicide prediction is a challenging task due to the spatio-temporal sparsity of these crime events. In this paper we report the results of using several approaches to mitigate this sparsity condition in machine learning models specially tailored towards modeling homicides events. Since spatial resolution is a direct determinant of sparsity, we focus on the performance of these models across different resolutions of interest to police authorities. We use a simple count model as benchmark and propose some enhancements of it directed towards improving prediction performance. We then compare the results to more complex models motivated by manifold learning and graph signal processing methods. We found that the simple benchmark models are as good as state of the art models for low resolution, but, as resolution increases, the performance of machine learning models outperform the benchmark. These results provide a rationality for the use of state of the art machine learning models for homicide prediction at the high resolution of interest for the deployment of police resources.

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Spatio Temporal Sparsity in Homicide Prediction Models

Riascos

Pabón²,

Rubio³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

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Graph Restrictions for Signal Processing of Homicides Data

Cited by 1 publication

References 8 publications

Spatio Temporal Sparsity in Homicide Prediction Models

Spatio Temporal Sparsity in Homicide Prediction Models

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