# Spatio-Temporal Data Mining: A Survey of Problems and Methods

arXiv: https://arxiv.org/abs/1711.04710 · HTML: https://mlsanity.com/paper/1711.04710/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 29 of 29)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://mlsanity.com/paper/2309.13363/) (2023)
- [Localised Adaptive Spatial-Temporal Graph Neural Network](https://mlsanity.com/paper/2306.06930/) (2023)
- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)
- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation](https://mlsanity.com/paper/2403.11960/) (2024)
- [A Survey on Spatio-temporal Data Analytics Systems](https://mlsanity.com/paper/2103.09883/) (2021)
- [Review of Clustering Methods for Functional Data](https://mlsanity.com/paper/2210.00847/) (2022)
- [Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework](https://mlsanity.com/paper/2202.10339/) (2022)
- [Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter](https://mlsanity.com/paper/2311.04190/) (2023)
- [Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey](https://mlsanity.com/paper/2103.12019/) (2021)
- [Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction](https://mlsanity.com/paper/2409.15764/) (2024)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression](https://mlsanity.com/paper/2603.10410/) (2026)
- [Mining Topological Dependencies of Recurrent Congestion in Road Networks](https://mlsanity.com/paper/2107.09554/) (2021)
- [Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns](https://mlsanity.com/paper/2112.01863/) (2021)
- [Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective](https://mlsanity.com/paper/2210.01344/) (2022)
- [Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection](https://mlsanity.com/paper/2211.10052/) (2022)
- [A Multi-Modal Machine Learning Approach to Detect Extreme Rainfall Events in Sicily](https://mlsanity.com/paper/2212.08102/) (2022)
- [A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies](https://mlsanity.com/paper/2304.09182/) (2023)
- [Clustering of Time Series Data with Prior Geographical Information](https://mlsanity.com/paper/2107.01310/) (2021)
- [Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility](https://mlsanity.com/paper/2211.14885/) (2022)
- [LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data](https://mlsanity.com/paper/2412.15365/) (2024)
- [Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales](https://mlsanity.com/paper/2510.09500/) (2025)
- [Context-Enriched Natural Language Descriptions of Vessel Trajectories](https://mlsanity.com/paper/2603.12287/) (2026)
- [General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting](https://mlsanity.com/paper/2608.17440/) (2026)

## Abstract (arXiv)

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data mining community. In this article we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data mining problems in each of these categories.