Learning spatial interaction representation with heterogeneous graph convolutional networks for urban land-use inference-Reference-Cited by-同舟云学术

Learning spatial interaction representation with heterogeneous graph convolutional networks for urban land-use inference

Published:2024-07-22 Issue: Volume: Page:1-37
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Gong Zhaoya¹²,Wang Chenglong¹²,Chen Yuting¹²,Liu Bin¹²,Zhao Pengjun¹²,Zhou Zhengzi¹²

Affiliation:

1. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China

2. Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China, Peking University Shenzhen Graduate School, Shenzhen, China

Funder

Shenzhen Science and Technology Program

Guangdong Basic and Applied Basic Research Foundation

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2379473

Reference51 articles.

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3. The size, scale, and shape of cities;Batty M.;Science (New York, N.Y.),2008

4. Comprehensive survey on distance/similarity measures between probability density functions;Cha S.H.;City,2007

5. Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities;Chen B.;Big Earth Data,2021

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