# Urban Anomaly Analytics: Description, Detection, and Prediction

arXiv: https://arxiv.org/abs/2004.12094 · HTML: https://mlsanity.com/paper/2004.12094/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System](https://mlsanity.com/paper/2105.14476/) (2021)
- [Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network](https://mlsanity.com/paper/2411.08755/) (2024)
- [Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features](https://mlsanity.com/paper/2509.06167/) (2025)
- [Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports](https://mlsanity.com/paper/2603.11546/) (2026)

## Abstract (arXiv)

Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.