# Spatial-Temporal Meta-path Guided Explainable Crime Prediction

arXiv: https://arxiv.org/abs/2205.01901 · HTML: https://mlsanity.com/paper/2205.01901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 69 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction](https://mlsanity.com/paper/1901.08518/) (2019)
- [Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction](https://mlsanity.com/paper/2006.01963/) (2020)
- [HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting](https://mlsanity.com/paper/2109.12846/) (2021)
- [Sequence-Aware Factorization Machines for Temporal Predictive Analytics](https://mlsanity.com/paper/1911.02752/) (2019)
- [Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data](https://mlsanity.com/paper/1409.2983/) (2014)
- [Mining large-scale human mobility data for long-term crime prediction](https://mlsanity.com/paper/1806.01400/) (2018)
- [Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots](https://mlsanity.com/paper/1508.02050/) (2015)
- [Crime prediction through urban metrics and statistical learning](https://mlsanity.com/paper/1712.03834/) (2017)
- [Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning](https://mlsanity.com/paper/2104.01711/) (2021)
- [MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility](https://mlsanity.com/paper/1709.10299/) (2017)
- [Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction](https://mlsanity.com/paper/2006.07516/) (2020)

## Built on by (most cited first, 2 of 2)

- [Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution](https://mlsanity.com/paper/2311.11235/) (2023)
- [Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks](https://mlsanity.com/paper/2511.02336/) (2025)

## Abstract (arXiv)

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 paper, we present a Spatial-Temporal Metapath guided Explainable Crime prediction (STMEC) framework to capture dynamic patterns of crime behaviours and explicitly characterize how the environmental and social factors mutually interact to produce the forecasts. Extensive experiments show the superiority of STMEC compared with other advanced spatiotemporal models, especially in predicting felonies (e.g., robberies and assaults with dangerous weapons).