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

doi:10.11947/j.AGCS.2022.20220236

Abstract

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

CLC Number:

P208

CHENG Tao, ZHANG Yang, James Haworth. Network and graph-based SpaceTimeAI: conception, method and applications[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1629-1639.

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