# Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

arXiv: https://arxiv.org/abs/2103.17128 · HTML: https://mlsanity.com/paper/2103.17128/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 359 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 30 of 59)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [YouTube-VOS: Sequence-to-Sequence Video Object Segmentation](https://mlsanity.com/paper/1809.00461/) (2018)
- [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series](https://mlsanity.com/paper/1905.12374/) (2019)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Semantic Amodal Segmentation](https://mlsanity.com/paper/1509.01329/) (2015)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)
- [Session-based Social Recommendation via Dynamic Graph Attention Networks](https://mlsanity.com/paper/1902.09362/) (2019)
- [SeGAN: Segmenting and Generating the Invisible](https://mlsanity.com/paper/1703.10239/) (2017)
- [Fast and Accurate Online Video Object Segmentation via Tracking Parts](https://mlsanity.com/paper/1806.02323/) (2018)
- [Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation](https://mlsanity.com/paper/1803.04242/) (2018)
- [Learning Motion Patterns in Videos](https://mlsanity.com/paper/1612.07217/) (2016)
- [CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF](https://mlsanity.com/paper/1803.09453/) (2018)
- [Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems](https://mlsanity.com/paper/1903.10433/) (2019)
- [Generative Adversarial Networks for Spatio-temporal Data: A Survey](https://mlsanity.com/paper/2008.08903/) (2020)
- [Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks](https://mlsanity.com/paper/1701.02543/) (2017)
- [Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters](https://mlsanity.com/paper/1811.10863/) (2018)
- [SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification](https://mlsanity.com/paper/2006.15353/) (2020)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps](https://mlsanity.com/paper/1802.07957/) (2018)
- [Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning](https://mlsanity.com/paper/1903.02678/) (2019)
- [Spatiotemporal CNN for Video Object Segmentation](https://mlsanity.com/paper/1904.02363/) (2019)

## Built on by (most cited first, 13 of 13)

- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)
- [A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention](https://mlsanity.com/paper/2504.15223/) (2025)
- [Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities](https://mlsanity.com/paper/2307.10803/) (2023)
- [MARL: Multimodal Attentional Representation Learning for Disease Prediction](https://mlsanity.com/paper/2105.00310/) (2021)
- [Geometry-aware Active Learning of Spatiotemporal Dynamic Systems](https://mlsanity.com/paper/2504.19012/) (2025)
- [Functional Complexity-adaptive Temporal Tensor Decomposition](https://mlsanity.com/paper/2502.06164/) (2025)
- [Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention](https://mlsanity.com/paper/2407.04440/) (2024)
- [PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning](https://mlsanity.com/paper/2412.03715/) (2024)
- [TrajLearn: Trajectory Prediction Learning using Deep Generative Models](https://mlsanity.com/paper/2501.00184/) (2024)
- [SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring](https://mlsanity.com/paper/2306.03042/) (2023)
- [Deep Heuristic Learning for Real-Time Urban Pathfinding](https://mlsanity.com/paper/2411.05044/) (2024)
- [A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting](https://mlsanity.com/paper/2503.19423/) (2025)
- [Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data](https://mlsanity.com/paper/2607.05995/) (2026)

## Abstract (arXiv)

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.