# Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data

arXiv: https://arxiv.org/abs/1902.06792 · HTML: https://mlsanity.com/paper/1902.06792/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 15 of 15)

- [Fredformer: Frequency Debiased Transformer for Time Series Forecasting](https://mlsanity.com/paper/2406.09009/) (2024)
- [Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction](https://mlsanity.com/paper/2207.06959/) (2022)
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- [CrashFormer: A Multimodal Architecture to Predict the Risk of Crash](https://mlsanity.com/paper/2402.05151/) (2024)
- [Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data](https://mlsanity.com/paper/2209.06813/) (2022)
- [FredNormer: Frequency Domain Normalization for Non-stationary Time Series Forecasting](https://mlsanity.com/paper/2410.01860/) (2024)
- [Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting](https://mlsanity.com/paper/2505.14555/) (2025)
- [TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series](https://mlsanity.com/paper/2602.17122/) (2026)
- [Net-Ev2: A Generative Simulator for Network Event Evolution](https://mlsanity.com/paper/2606.12494/) (2026)
- [COSTI: a New Classifier for Sequences of Temporal Intervals](https://mlsanity.com/paper/2204.13467/) (2022)
- [Interpretable Traffic Event Analysis with Bayesian Networks](https://mlsanity.com/paper/2310.06713/) (2023)
- [Accident Impact Prediction based on a deep convolutional and recurrent neural network model](https://mlsanity.com/paper/2411.07537/) (2024)
- [A Selective Temporal Hamming distance to find patterns in state transition event timeseries, at scale](https://mlsanity.com/paper/2512.01440/) (2025)
- [Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data](https://mlsanity.com/paper/2607.05995/) (2026)

## Abstract (arXiv)

Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose frameworks) is not semantically representative of geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation patterns and the other to explore influential patterns. Propagation patterns reveal common cascading forms of geospatial entities in a region. Influential patterns demonstrate the impact of temporally long-term geospatial entities on their neighborhood. We apply this framework on a large dataset of traffic and weather data at countrywide scale, collected for the contiguous United States over two years. Our important findings include the identification of 90 common propagation patterns of traffic and weather entities (e.g., rain --> accident --> congestion), which results in identification of four categories of states within the US; and interesting influential patterns with respect to the "location", "duration", and "type" of long-term entities (e.g., a major construction --> more traffic incidents). These patterns and the categorization of the states provide useful insights on the driving habits and infrastructure characteristics of different regions in the US, and could be of significant value for applications such as urban planning and personalized insurance.