Cheng, T;
Zhang, Y;
Haworth, J;
(2022)
Network SpaceTime Al: Concepts, Methods and Applications.
Journal of Geodesy and Geoinformation Science
, 5
(3)
pp. 78-92.
10.11947/j.JGGS.2022.0309.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Network SpaceTime Al: Concepts, Methods and Applications |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.11947/j.JGGS.2022.0309 |
| Publisher version: | https://doi.org/10.11947/j.JGGS.2022.0309 |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | spatiotemporal intelligence; network; graph; deep learning; spatiotemporal prediction; spatiotemporal clustering; spatiotemporal optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10199582 |
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