Incorporating spatial interaction patterns in classifying and understanding urban land use |
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| Authors: | Xi Liu Chaogui Kang Li Gong |
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| Institution: | 1. Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, China;2. Beijing Key Lab of Spatial Information Integration and Its Applications, Peking University, Beijing, China;3. Department of Geography, The Pennsylvania State University, University Park, PA, USA;4. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China |
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| Abstract: | Land use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai. |
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| Keywords: | Urban land use spatial interaction classification social sensing |
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