# Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction

arXiv: https://arxiv.org/abs/2304.01569 · HTML: https://mlsanity.com/paper/2304.01569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning](https://mlsanity.com/paper/2201.02435/) (2022)
- [Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data](https://mlsanity.com/paper/1409.2983/) (2014)
- [Modelling Regional Crime Risk using Directed Graph of Check-ins](https://mlsanity.com/paper/1908.02570/) (2019)
- [Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction](https://mlsanity.com/paper/2110.01794/) (2021)

## Built on by (most cited first, 1 of 1)

- [Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity](https://mlsanity.com/paper/2407.19668/) (2024)

## Abstract (arXiv)

Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance. Existing methods typically use deep learning to capture the intra-dependencies in spatial and temporal dimensions. However, numerous key challenges remain unsolved, for instance, sparse zero-inflated data due to urban anomalies occurring with low frequency (which can lead to poor performance on real-world datasets), and both intra- and inter-dependencies of abnormal patterns across spatial, temporal, and semantic dimensions. Moreover, a unified approach to predict multiple kinds of anomaly is left to explore. In this paper, we propose STS to jointly capture the intra- and inter-dependencies between the patterns and the influential factors in three dimensions. Further, we use a multi-task prediction module with a customized loss function to solve the zero-inflated issue. To verify the effectiveness of the model, we apply it to two urban anomaly prediction tasks, crime prediction and traffic accident risk prediction, respectively. Experiments on two application scenarios with four real-world datasets demonstrate the superiority of STS, which outperforms state-of-the-art methods in the mean absolute error and the root mean square error by 37.88% and 18.10% on zero-inflated datasets, and, 60.32% and 37.28% on non-zero datasets, respectively.