Commuting Pattern Recognition of Industrial Parks Using Mobile Phone Signaling Data: A Case Study of Nanjing, China

As a novel industrial space to cope with global competition, industrial parks have gradually become important growth poles to promote regional development and provide a large number of employment opportunities. This study utilizes mobile phone signaling data to identify the commuting origins and destinations (OD) of different industrial parks in Nanjing while comparing the distribution of the working population, residential population, and commuting patterns across varying types and levels of industrial parks. The level of coordination of the employment–residential system in each park is quantified by calculating the resident commuting index (HSCi), employee commuting index (WSCi), and their coupling coordination degree. Additionally, geographic detectors are employed to identify the influencing factors and interaction effects that impact the employment–residential balance in industrial parks. Results show that industrial parks located in the central urban area attract more residential and working populations. The commuting volume of national and municipal as well as high-tech industrial parks is higher than other types of industrial parks. Most industrial parks experience more inward than outward commuting, and there is an uneven distribution of commuting flows, resulting in a network-like pattern of “central dense, peripheral sparse”. Various industrial parks exhibit a highly coupled job–housing system, and those with high HSCi tend to have high WSCi as well. The coupling coordination of industrial parks ranged from 0.16 to 0.93, with 13 being primary coordination or above and 3 being disordered. Industrial parks are classified into three types: employment-oriented, residential-oriented, and employment–residential balanced, with the residential-oriented type being predominant. The density of public transportation stops, park area, and land use mix are the primary factors affecting the employment–residential balance. Industrial parks with larger scale, better land allocation, and higher service facility levels are more likely to achieve coordination in the employment–residential system. Our work utilizes mobile signaling data to characterize the commuting patterns of industrial parks, providing insights for industrial park planning and promoting the integration of industry and city.

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Publication Details

Journal
Land
Published
2024-10-03
DOI
https://doi.org/10.3390/land13101605
Citations
2
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
1.37
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article

Commuting Pattern Recognition of Industrial Parks Using Mobile Phone Signaling Data: A Case Study of Nanjing, China

Yingyu Wang, Xingping Wang, Yang Zhang, Gai Zhang et al.
2 citations
Land
Human Mobility and Location-Based Analysis
1.37
article

Commuting Pattern Recognition of Industrial Parks Using Mobile Phone Signaling Data: A Case Study of Nanjing, China

Yingyu Wang, Xingping Wang, Yang Zhang, Gai Zhang, Zhan Gao, Xinguo Yuan, Juan Li
article en
2 citations

Abstract

As a novel industrial space to cope with global competition, industrial parks have gradually become important growth poles to promote regional development and provide a large number of employment opportunities. This study utilizes mobile phone signaling data to identify the commuting origins and destinations (OD) of different industrial parks in Nanjing while comparing the distribution of the working population, residential population, and commuting patterns across varying types and levels of industrial parks. The level of coordination of the employment–residential system in each park is quantified by calculating the resident commuting index (HSCi), employee commuting index (WSCi), and their coupling coordination degree. Additionally, geographic detectors are employed to identify the influencing factors and interaction effects that impact the employment–residential balance in industrial parks. Results show that industrial parks located in the central urban area attract more residential and working populations. The commuting volume of national and municipal as well as high-tech industrial parks is higher than other types of industrial parks. Most industrial parks experience more inward than outward commuting, and there is an uneven distribution of commuting flows, resulting in a network-like pattern of “central dense, peripheral sparse”. Various industrial parks exhibit a highly coupled job–housing system, and those with high HSCi tend to have high WSCi as well. The coupling coordination of industrial parks ranged from 0.16 to 0.93, with 13 being primary coordination or above and 3 being disordered. Industrial parks are classified into three types: employment-oriented, residential-oriented, and employment–residential balanced, with the residential-oriented type being predominant. The density of public transportation stops, park area, and land use mix are the primary factors affecting the employment–residential balance. Industrial parks with larger scale, better land allocation, and higher service facility levels are more likely to achieve coordination in the employment–residential system. Our work utilizes mobile signaling data to characterize the commuting patterns of industrial parks, providing insights for industrial park planning and promoting the integration of industry and city.

LandVol. 13(10)
Chengdu University of Technology (CN), China Design Group (China) (CN), Southeast University (CN)
Openalex Percentile: Top 16%
Human Mobility and Location-Based Analysis
1.37
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