Spatial-Temporal Meta-path Guided Explainable Crime Prediction
Yuting Sun, Tong Chen, Hongzhi Yin

TL;DR
This paper introduces STMEC, a novel explainable model that captures explicit spatial-temporal patterns and social-environmental interactions to improve crime prediction accuracy, especially for felonies.
Contribution
The paper proposes a Spatial-Temporal Metapath guided framework that explicitly models environmental and social factors influencing crime, enhancing interpretability and prediction performance.
Findings
STMEC outperforms existing models in crime prediction accuracy.
The framework effectively captures explicit social and environmental interactions.
Superior performance in predicting felonies like robberies and assaults.
Abstract
Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to prevent crimes. With the increasing availability of both fine-grained urban and public service data, there is a recent surge in fusing such cross-domain information to facilitate crime prediction. By capturing the information about social structure, environment, and crime trends, existing machine learning predictive models have explored the dynamic crime patterns from different views. However, these approaches mostly convert such multi-source knowledge into implicit and latent representations (e.g., learned embeddings of districts), making it still a challenge to investigate the impacts of explicit factors for the occurrences of crimes behind the scenes. In this…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Crime Patterns and Interventions · Data Visualization and Analytics
Methodstravel james
