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Learning place representations from spatial interactions

Wang, Xuechen; Chen, Huanfa; Liu, Yu; (2024) Learning place representations from spatial interactions. International Journal of Geographical Information Science 10.1080/13658816.2024.2332908. (In press).

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Abstract

The development of geospatial artificial intelligence (GeoAI) systems depends on the ability to learn effective representations of places. To learn accurate place representations from spatial interactions, it is important to extract features that capture both the spatial and non-spatial driving factors. However, existing methods lack a robust interpretation and the explanatory power of the learned representations on spatial factors remains unexplored. Here, we propose an approach to learning place representations from spatial interactions. Our method is inspired by flow allocation, which is the main focus of single-constrained gravity models. We first validate the method on synthetic flows with known driving factors and then apply it to multi-scale real-world flows. Results show that the learned representations can effectively capture features that explain place characteristics, along with the impact of spatial impedance. Our study not only contributes an efficient method to learn place representations from spatial interactions but also offers insights into pre-training procedures in GeoAI.

Type: Article
Title: Learning place representations from spatial interactions
DOI: 10.1080/13658816.2024.2332908
Publisher version: http://dx.doi.org/10.1080/13658816.2024.2332908
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Spatial interaction; place representation learning; GeoAI; flow allocation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10191904
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