https://doi.org/10.1140/epjds/s13688-024-00487-w
Research
Downscaling spatial interaction with socioeconomic attributes
1
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China
2
Ordos Research Institute of Energy, Peking University, Inner Mongolia, China
Received:
28
November
2023
Accepted:
24
June
2024
Published online:
5
July
2024
A variety of complex socioeconomic phenomena, for example, migration, commuting, and trade can be abstracted by spatial interaction networks, where nodes represent geographic locations and weighted edges convey the interaction and its strength. However, obtaining fine-grained spatial interaction data is very challenging in practice due to limitations in collection methods and costs, so spatial interaction data such as transportation data and trade data are often only available at a coarse scale. Here, we propose a gravity downscaling (GD) method based on readily accessible socioeconomic data and the gravity law to infer fine-grained interactions from coarse-grained data. GD assumes that interactions of different spatial scales are governed by the similar gravity law and thus can transfer the parameters estimated from coarse-grained regions to fine-grained regions. Results show that GD has an average improvement of 24.6% in Mean Absolute Percentage Error over alternative downscaling methods (i.e., the areal-weighted method and machine learning models) across datasets with different spatial scales and in various regions. Using simple assumptions, GD enables accurate downscaling of spatial interactions, making it applicable to a wide range of fields, including human mobility, transportation, and trade.
Key words: Spatial interaction / Gravity model / Downscaling / Transferability
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-024-00487-w.
© The Author(s) 2024
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