Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data

Sheng Hua Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan, China;b School of Geography and Information Engineering, China University of Geosciences, Wuhan, China;c Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, China;d GeoSpatialx Lab, Geography Department, National University of Singapore, Singapore, SingaporeView further author information

Song Gaoe GeoDs Lab, Department of Geography, University of Wisconsin‐Madison, Madison, WI, USACorrespondence[email protected]
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Wei Luod GeoSpatialx Lab, Geography Department, National University of Singapore, Singapore, SingaporeView further author information

Liang Wuf School of Computer Science, China University of Geosciences, Wuhan, China;g National Engineering Research Center of Geographic Information System, Wuhan, ChinaCorrespondence[email protected]
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Tianqi Lib School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaView further author information

Yongyang Xuc Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, China;f School of Computer Science, China University of Geosciences, Wuhan, China;g National Engineering Research Center of Geographic Information System, Wuhan, ChinaView further author information

Ziwei Zhangb School of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaView further author information

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Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data

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Revealing intra-urban hierarchical spatial structure through representation learning by combining road network abstraction model and taxi trajectory data

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