Journal of Transport and Land Use

Exploring spatial association between residential and commercial urban spaces: A machine learning approach using taxi trajectory data

Lei Zhou

Nanjing University of Posts and Telecommunications

Weiye Xiao

Nanjing Institute of Geography and Limnology

Chen Wang

Nanjing University of Posts and Telecommunications

Haoran Wang

Nanjing Normal University

DOI: https://doi.org/10.5198/jtlu.2024.1800

Keywords: Commercial and residential spaces; Spatial association; Social network analysis; Community detection; Taxi trajectory data

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Abstract

Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people's commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research.

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Author Biographies

Lei Zhou, Nanjing University of Posts and Telecommunications

School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

Weiye Xiao, Nanjing Institute of Geography and Limnology

Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 214000, China

Chen Wang, Nanjing University of Posts and Telecommunications

School of Internet of Things

Formerly: School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications

Haoran Wang, Nanjing Normal University

School of Geographic Science, Nanjing Normal University, Nanjing, 210023, China

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