Crime prediction with graph neural networks and multivariate normal distributions

Date
2022-07-01
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Source Title
Signal, Image and Video Processing
Print ISSN
1863-1703
Electronic ISSN
1863-1711
Publisher
Springer UK
Volume
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Pages
1 - 7
Language
English
Type
Article
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Abstract

We study high-resolution crime prediction and introduce a new generative model applicable to any spatiotemporal data with graph convolutional gated recurrent units (Graph-ConvGRU) and multivariate Gaussian distributions. We introduce a subdivision algorithm and create a graph representation to tackle the sparsity and complexity problem in high-resolution spatiotemporal data. By leveraging the flexible structure of graph representation, we model the spatial, temporal, and categorical relations of crime events and produce state vectors for each region. We create a multivariate probability distribution from the state vectors and train the distributions by minimizing the KL divergence between the generated and the actual distribution of the crime events. After creating the distributions, crime can be predicted in any resolution as the first time in the literature. In our experiments on real-life and synthetic datasets, our model obtains the best score with respect to the state-of-the-art models with statistically significant improvements. Hence, our model is not only generative but also precise. We also provide the source code of our algorithm for reproducibility.

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Keywords
Crime forecasting, Probabilistic graph models, Deep learning
Citation
Published Version (Please cite this version)