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Article

Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering

Department of Geo-Informatics, Central South University, Changsha 410006, China
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Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(5), 274; https://doi.org/10.3390/ijgi10050274
Received: 10 March 2021 / Revised: 15 April 2021 / Accepted: 21 April 2021 / Published: 23 April 2021
In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is typically biased and results in difficulty obtaining a comprehensive view of urban land use. It remains challenging to fuse high-dimensional and noisy multi-source urban mobility data to infer urban land use. This study aimed to infer urban land use from multi-source urban mobility data using latent multi-view subspace clustering. The variation in the number of origin/destination points over time was initially used to characterize land use types. Then, a latent multi-view representation was applied to construct the common underlying structure shared by multi-source urban mobility data and effectively deal with noise. Finally, based on the latent multi-view representation, the subspace clustering method was used to infer the land use types. Experiments on taxi trajectory data and bus smart card data in Beijing reveal that, compared with the method using a single type of urban mobility dataset and the weighted fusion method, the approach presented in this study obtains the highest detection rate of land use. The urban land use inferred in this study provides calibration and reference for urban planning. View Full-Text
Keywords: urban land use; urban mobility data; multi-source; latent representation; subspace clustering urban land use; urban mobility data; multi-source; latent representation; subspace clustering
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MDPI and ACS Style

Liu, Q.; Huan, W.; Deng, M.; Zheng, X.; Yuan, H. Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering. ISPRS Int. J. Geo-Inf. 2021, 10, 274. https://doi.org/10.3390/ijgi10050274

AMA Style

Liu Q, Huan W, Deng M, Zheng X, Yuan H. Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering. ISPRS International Journal of Geo-Information. 2021; 10(5):274. https://doi.org/10.3390/ijgi10050274

Chicago/Turabian Style

Liu, Qiliang, Weihua Huan, Min Deng, Xiaolin Zheng, and Haotao Yuan. 2021. "Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering" ISPRS International Journal of Geo-Information 10, no. 5: 274. https://doi.org/10.3390/ijgi10050274

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