An empirical study on the intra-urban goods movement patterns using logistics big data |
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Authors: | Pengxiang Zhao Wenzhong Shi Tao Jia Wengen Li Min Chen |
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Institution: | 1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland;3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, PR China;4. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;5. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, PR China;6. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, PR China https://orcid.org/0000-0001-8922-8789 |
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Abstract: | ABSTRACTMovement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning. |
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Keywords: | Goods movement patterns logistics big data spatial analysis network analysis spatial interaction |
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