# A Survey on Spatio-temporal Data Analytics Systems

arXiv: https://arxiv.org/abs/2103.09883 · HTML: https://mlsanity.com/paper/2103.09883/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 284 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [The Case for Learned Spatial Indexes](https://mlsanity.com/paper/2008.10349/) (2020)
- [AsterixDB: A Scalable, Open Source BDMS](https://mlsanity.com/paper/1407.0454/) (2014)
- [An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R](https://mlsanity.com/paper/1412.0436/) (2014)
- [Distributed Publish/Subscribe Query Processing on the Spatio-Textual Data Stream](https://mlsanity.com/paper/1612.02564/) (2016)
- [LocationSpark: In-memory Distributed Spatial Query Processing and Optimization](https://mlsanity.com/paper/1907.03736/) (2019)
- [R*-Grove: Balanced Spatial Partitioning for Large-scale Datasets](https://mlsanity.com/paper/2007.11651/) (2020)

## Built on by (most cited first, 9 of 9)

- [SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery](https://mlsanity.com/paper/2311.17179/) (2023)
- [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025)
- [A Survey of Generative Techniques for Spatial-Temporal Data Mining](https://mlsanity.com/paper/2405.09592/) (2024)
- [Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUs](https://mlsanity.com/paper/2503.11901/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models](https://mlsanity.com/paper/2601.12083/) (2026)
- [Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints](https://mlsanity.com/paper/2306.12857/) (2023)
- [CubeGraph: Efficient Retrieval-Augmented Generation for Spatial and Temporal Data](https://mlsanity.com/paper/2604.06616/) (2026)
- [MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories](https://mlsanity.com/paper/2606.17978/) (2026)

## Abstract (arXiv)

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatio-temporal data processing infrastructures, and (3) programming languages and software tools for processing spatio-temporal data. Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.