Characterizing the urban spatial structure using taxi trip big data and implications for urban planning

Abstract

Urban spatial structure is an important feature for assessing the effects of urban planning. Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments. Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures. However, these methods cannot efficiently reflect the influence of human activities. With the wide application of big data, analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning. In this study, we constructed a human-activity space network using the taxi trip big data. Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning. This method was applied to a case study based on one-month taxi trip data of Dongguan City. Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020, which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan. We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure. The analysis demonstrated that the taxi trip data are important big data on social spatial perception, and taxi data should be used for evaluating spatial structures in future urban planning.

Keywords

urban structure / taxi GPS data / complex networks / community management

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Acknowledgments

We sincerely thank all the anonymous reviewers for their constructive comments and suggestions. This research was supported by the National Natural Science Foundation of China (Grant Nos. 42001326 and 41871318), the Fundamental Research Funds for the Central Universities (Grant No. 19lgpy53), and the China National Postdoctoral Program for Innovative Talents (Grant No. BX20180389).

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