# Spatiotemporal Data Mining: A Survey

arXiv: https://arxiv.org/abs/2206.12753 · HTML: https://mlsanity.com/paper/2206.12753/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 79 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks](https://mlsanity.com/paper/1705.02699/) (2017)
- [Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights](https://mlsanity.com/paper/1909.09638/) (2019)
- [TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction](https://mlsanity.com/paper/1911.08684/) (2019)
- [Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach](https://mlsanity.com/paper/2201.06955/) (2022)
- [Balanced k-means for Parallel Geometric Partitioning](https://mlsanity.com/paper/1805.01208/) (2018)

## Built on by (most cited first, 3 of 3)

- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)
- [SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring](https://mlsanity.com/paper/2306.03042/) (2023)

## Abstract (arXiv)

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.