Non-linear effects of built environment and socio-demographics on activity space

Zhengyu Duan, Haoran Zhao and Zhenming Li

Journal of Transport Geography, 2023, vol. 111, issue C

Abstract: The activity space of residents is an important mediator for the exploration of the relationship between residents' activities and the city. In this study, one-month mobile phone data are used to study the activity space of residents from three aspects, namely the radius of the activity space, the frequency of activity, and the diversity of activities. Combined with census data and point-of-interest data, a random forest model is established, and partial dependency plots are used to explore the non-linear effects of the built environment and socio-demographic attributes on the activity space. After analyzing mobile phone data from the central area of Shanghai, China, over one month, it is found that the facility density, accessibility, location, housing conditions, and marital status are the most important factors affecting the activity space of residents. Different from previous studies, it is found that the facility density and accessibility have a diminishing marginal effect on the activity space, and both the built environment and socio-demographic attributes have threshold effects on the activity space. The location relative to the Huangpu River is also found to influence the frequency of activity. Moreover, there are obvious differences between the activity spaces of home-owning residents and tenants. Among tenants, a too-low or too-high monthly rent can lead to a small radius of activity space and a low diversity of activities. Married residents often have a large radius of the activity space, a high frequency of activity, and a high diversity of activities. Some factors are also found to affect the influences of other factors on the activity space, and can even change the direction of correlation. The findings of this study can provide references for urban and transportation planning.

Keywords: Residents' activity space; Mobile phone data; Built environment; Random forest; Partial dependency plot (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:111:y:2023:i:c:s0966692323001436

DOI: 10.1016/j.jtrangeo.2023.103671

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