Identification of Urban Jobs–Housing Sites Based on Online Car-Hailing Data

Shuoben Bi (), Luye Wang, Shaoli Liu, Lili Zhang and Cong Yuan
Additional contact information
Shuoben Bi: School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Luye Wang: School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Shaoli Liu: School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Lili Zhang: School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Cong Yuan: School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

Sustainability, 2023, vol. 15, issue 2, 1-20

Abstract: With the development of cities, the organization of jobs–housing space is becoming more complex, and the rapid, effective identification of both residences and workplaces is crucial to sustainable urban development. The long time series of online car-hailing data conveys a large amount of activity trajectory information about urban populations, which can represent the social functions of urban areas, including workplaces and residences. This paper constructs a jobs–housing site identification model based on human activity characteristics. This model uses a time series dataset of online car hailing that characterizes the changes in regional passenger flow and implements the similarity measure and semi-supervised learning of time series to determine the classification of urban areas. Then, the jobs–housing factor method is introduced to extract the jobs–housing characteristics of different regions, which achieves the jobs–housing site identification. Finally, the empirical analysis of Chengdu city shows that the proposed model method can effectively mine the distribution of urban jobs–housing sites. The identification results are consistent with the actual situation, and the combination of the time series similarity and the jobs–housing feature variable improves the identification effect, providing a new way of thinking about urban jobs–housing space research.

Keywords: urban jobs–housing sites; online car-hailing data; time series mining; similarity measurement; jobs–housing factor method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/2/1712/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/2/1712/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:2:p:1712-:d:1037616

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().