Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (3): 78-92.doi: 10.11947/j.JGGS.2022.0309

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Network SpaceTime AI: Concepts, Methods and Applications


  1. SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK
  • Received:2022-05-25 Accepted:2022-07-25 Online:2022-09-20 Published:2022-11-17
  • About author:Tao CHENG(1968—),female, PhD, professor,majors in AI and big data, network complexity, urban analytics (modelling, prediction, clustering, visualisation and simulation) with applications in transport and mobility, safety and security, business intelligence, and natural hazards prevention. E-mail:
  • Supported by:
    UK Research and Innovation Council (UKRI) Funding(EP/R511683/1);UK Research and Innovation Council (UKRI) Funding(EP/J004197/1);UK Research and Innovation Council (UKRI) Funding(ES/L011840/1);UCL Dean Prize and China Scholarship Council(201603170309)


SpacetimeAI and GeoAI are currently hot topics, applying the latest algorithms in computer science, such as deep learning, to spatiotemporal data. Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing, their applications in other spatial and space-time data types are still immature. This paper sets up the proposition of using a network (&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points, polylines, and polygons. We illustrate network and graph-based SpaceTimeAI, from graph-based deep learning for prediction, to space-time clustering and optimisation. These applications demonstrate the advantages of network (graph)-based SpacetimeAI in the fields of transport&mobility, crime&policing, and public health.

Key words: spatiotemporal intelligence; network; graph; deep learning; spatiotemporal prediction; spatiotemporal clustering; spatiotemporal optimization