Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (7): 1629-1639.doi: 10.11947/j.AGCS.2022.20220236

• Cartography and Geoinformation • Previous Articles     Next Articles

Network and graph-based SpaceTimeAI: conception, method and applications

CHENG Tao, ZHANG Yang, James Haworth   

  1. SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK
  • Received:2022-04-06 Revised:2022-05-23 Published:2022-08-13
  • Supported by:
    UK Research and Innovation (UKRI) (Nos. EP/R511683/1|EP/J004197/1|ES/L011840/1)|The UCL Dean Prize|China Scholarship Gouncil (No. 201603170309)

Abstract: SpaceTimeAI and GeoAI are currently a hot topic, which apply the latest algorithms in computer science, such as deep learning. Although deep learning algorithms have been successfully applied in raster data processing 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 the network (& graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the point, polyline, and polygon. We illustrate network and graph-based SpaceTimeAI, from graph-based deep learning for prediction, to clustering and optimisation. These demonstrate the advantages of the network (graph)-based SpaceTimeAI in the applications of transport & mobility, crime & policing, and public health.

Key words: SpaceTimeAI, GeoAI, network, graph, deep learning, space-time prediction

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