Geographically Explicit Network Analysis of Urban Living and Working Interaction Pattern in Shenzhen City, South China

Zhilu Yuan,Shengjun Tang,Haojia Lin,Renzhong Guo

doi:10.3389/fphy.2021.795641

Abstract

Human daily mobility plays an important role in urban research. Commuting of urban residents is an important part of urban daily mobility, especially in working days. However, the characteristic of the mobility network formed by the commuting of urban residents and its impact on the internal structure of the city are still an important work that needs to be explored further. Aiming to study the living–working interaction pattern of meta-populations over urban divisions within cities, a fine-grained dataset of living–working tracking of Shenzhen is curated and used to construct an urban living–working mobility network, and the living–working interaction pattern is analyzed through the community structures of the network. The results show that human daily mobility plays an important role in understanding the formation of urban structure, the administrative divisions of the city affect human daily mobility, and human daily mobility reacts on the formation of urban structure.

Highlights

Large-scale demographic census enables measurements of human living–working traces, which have become popular and served as essential reasons of motivation for human mobility [1]
We found that there is an interaction between the human daily mobility and the formation of urban structure, the administrative divisions of the city affect human daily mobility, and human daily mobility reacts on the urban structure
Human daily mobility plays an important role in understanding the formation of urban structure

Summary

Introduction

Large-scale demographic census enables measurements of human living–working traces, which have become popular and served as essential reasons of motivation for human mobility [1]. In Shenzhen, on average, people travel by subway for distance longer than 27 km while by bus for 9 km [10]. This is, arguably, due to the lack of fine-grained public datasets that could describe the living–working dynamics within cities. There are some open-access datasets covering small geographical locations considering the time ordering of location tracking, such as networks of mobile-phone users within a city [11] and between cities [12], which can infer people’s living and working locations potentially. Fine-grained living–working datasets covering large geographical regions within a city with large populations are still missing from the open-access datasets

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Results

Conclusion