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Research Areas

Project Name

Spatio-Temporal Analytics


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As the world becomes instrumented and interconnected, spatio-temporal data are more ubiquitous and richer than ever before. Moving object (e.g., taxi, bird) trajectories recorded by GPS devices, social event (e.g., microblogs, crime) with location tag and time stamps, and environment monitoring (e.g., remote sensing images for weather forecasting) are typical spatio-temporal data that we meet every day. These emerging spatio-temporal data also bring new challenges and opportunities to data analytics research and business intelligence solution: Can we discover more predictive patterns from combining space and time dimensions, and better solve real-world business problems?

The first law of geography tells us that “everything is related to everything else but nearby things are more related than distant things”. Such a characteristic is also known as the spatial autocorrelation. Therefore, the widely used i.i.d. assumption in data mining is too strong when analyzing spatial data. New methods and modeling techniques are needed to tackle with the spatial heterogeneity and the spatial relationships (such as topological relationships, directional relationships, etc.), which are unique to spatial data. Spatio-temporal data are further temporally dynamic, which requires explicit or implicit modeling the spatio-temporal autocorrelation and constraints to achieve good prediction performance.

In real world, we also face great challenges from massive data volume, data uncertainty, complex relationship, and system dynamics. Thus spatio-temporal analytics research will focus on the following topics:

  • Effective storage and indexing of massive spatio-temporal data
  • Pre-processing and denoising technology to address spatio-temporal data uncertainty
  • Robust spatio-temporal pattern mining and prediction algorithms
  • Spatio-temporal data visualization to make data more consumable

    • Some of the components have been developed and contributed to IBM predictive analytics software such as SPSS Modeler, and industrial solutions such as Crime Information Warehouse (CIW) and asset failure pattern analysis.

    Typical business scenarios of spatio-temporal analytics include:

  • Traffic congestion prediction: Extraction of traffic congestion recursive patterns and the congestion spread patterns from historical traffic data. Prediction of congestion occurrence and its impacts at the right place and the right time.
  • Crime pattern analysis and prediction: Discovering spatio-temporal trends of crime hotspot movements from historical crime records. Crime occurrences prediction via spatio-temporal modeling crime likelihood, considering spatio-temporal contextual information such as weather, census data and point-of-interests (POI).
  • Epidemic spread characterization and alerting: Surveillance of epidemic outbreak based on reported disease data. Characterization of disease spread pattern.