# Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks

arXiv: https://arxiv.org/abs/2408.04193 · HTML: https://mlsanity.com/paper/2408.04193/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction](https://mlsanity.com/paper/2204.08587/) (2022)
- [Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning](https://mlsanity.com/paper/2201.02435/) (2022)
- [Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks](https://mlsanity.com/paper/2208.05908/) (2022)
- [MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer](https://mlsanity.com/paper/2309.09067/) (2023)
- [HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting](https://mlsanity.com/paper/2109.12846/) (2021)
- [ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting](https://mlsanity.com/paper/2308.07496/) (2023)
- [Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction](https://mlsanity.com/paper/2306.09882/) (2023)
- [ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks](https://mlsanity.com/paper/2305.06480/) (2023)
- [Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction](https://mlsanity.com/paper/2309.05072/) (2023)
- [Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots](https://mlsanity.com/paper/1508.02050/) (2015)
- [Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction](https://mlsanity.com/paper/2305.05722/) (2023)
- [Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities](https://mlsanity.com/paper/2311.02485/) (2023)

## Built on by (most cited first, 5 of 5)

- [Domain Adaptation Framework for Turning Movement Count Estimation with Limited Data](https://mlsanity.com/paper/2503.20113/) (2025)
- [Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)](https://mlsanity.com/paper/2409.16594/) (2024)
- [Network-Level Vehicle Delay Estimation at Heterogeneous Signalized Intersections](https://mlsanity.com/paper/2510.01292/) (2025)
- [Harnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localities](https://mlsanity.com/paper/2512.11178/) (2025)
- [Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports](https://mlsanity.com/paper/2603.11546/) (2026)

## Abstract (arXiv)

Crime forecasting is a critical component of urban analysis and essential for stabilizing society today. Unlike other time series forecasting problems, crime incidents are sparse, particularly in small regions and within specific time periods. Traditional spatial-temporal deep learning models often struggle with this sparsity, as they typically cannot effectively handle the non-Gaussian nature of crime data, which is characterized by numerous zeros and over-dispersed patterns. To address these challenges, we introduce a novel approach termed Spatial Temporal Multivariate Zero-Inflated Negative Binomial Graph Neural Networks (STMGNN-ZINB). This framework leverages diffusion and convolution networks to analyze spatial, temporal, and multivariate correlations, enabling the parameterization of probabilistic distributions of crime incidents. By incorporating a Zero-Inflated Negative Binomial model, STMGNN-ZINB effectively manages the sparse nature of crime data, enhancing prediction accuracy and the precision of confidence intervals. Our evaluation on real-world datasets confirms that STMGNN-ZINB outperforms existing models, providing a more reliable tool for predicting and understanding crime dynamics.