A new approach for identifying urban employment centers using mobile phone data: a case study of Shanghai

Abstract

Identifying urban employment centers (UECs) is crucial for understanding urban spatial structures. Conventional identification methods use census-type aggregated data. The increasing pervasiveness of mobile phone data provides new possibilities to analyze UECs at finer spatial resolution but requires novel identification methods. This study proposes a new approach, the Locally Decaying Model (LDM), to fit the employment density locally and identify UECs based on statistically significant local peaks. We compared the proposed LDM with conventional methods by Monte-Carlo simulation and real data experiments on employment distributions generated from Shanghai mobile phone data. The Monte-Carlo simulation showed that the LDM performed significantly better. The Shanghai case study demonstrated greater stability of the LDM in identifying UEC numbers and locations on aggregated data than the conventional methods. Furthermore, UECs extracted by LDM from the employment density raster were more consistent with the existing local plan. This research contributed a new subcenter identification method that can be applied to recently available urban big data and a comprehensive analytical framework for evaluating such methods.

Keywords:

• Urban employment center
• subcenter identification method
• locally decaying model
• mobile phone data

Author contributions

Longxu Yan: Conceptualization, methodology, formal analysis, visualization, and writing – original draft; Yishu Wang: Conceptualization, methodology, investigation, validation, and writing – original draft; De Wang: Resources, writing – review & editing, supervision, and funding acquisition; Shangwu Zhang: Conceptualization and writing – review & editing; Yang Xiao: Writing – reviewing and editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available with the identifier(s) at the public link (https://doi.org/10.6084/m9.figshare.12027183).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant [52208074], the National Key Research and Development Plan [2022YFC3800801], the Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100] and the Fundamental Research Funds for the Central Universities.

Notes on contributors

Longxu Yan

Longxu Yan is an assistant professor in the College of Architecture and Urban Planning (CAUP) at Tongji University, China. His research focus on implementing innovative data-driven approaches in urban studies to provide deep insights into urban spatial structure, city performance, and smart planning.

Yishu Wang

Yishu Wang is a PhD candidate in department of economics, The Chinese University of Hong Kong, China. He is interested in econometric theory and application, time series and machine learning methods.

De Wang

De Wang is a professor in the CAUP at Tongji University, China. His research interests focus on behavior and policy, urban mobility, and big data analysis.

Shangwu Zhang

Shangwu Zhang is a professor in the CAUP at Tongji University, China. His research interests mainly focus on urban and regional development, spatial planning, and rural planning.

Yang Xiao

Yang Xiao is an Associate Professor in the College of Architecture and Urban Planning at Tongji University, China. He received his PhD degree in Urban Planning from Cardiff University, UK. His interests mainly focus on the big data mining and interaction of built environment and human behavior.