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- Publisher Website: 10.1073/pnas.1815928115
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- PMID: 30455293
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Article: Tracking job and housing dynamics with smartcard data
Title | Tracking job and housing dynamics with smartcard data |
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Authors | |
Keywords | commuting pattern job dynamics housing dynamics mobility group smartcard data |
Issue Date | 2018 |
Publisher | National Academy of Sciences. The Journal's web site is located at http://www.pnas.org |
Citation | Proceedings of the National Academy of Sciences, 2018, v. 115 n. 50, p. 12710-12715 How to Cite? |
Abstract | Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces. Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences. Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns. Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle- to high-income groups. Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership. Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home. Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants. |
Persistent Identifier | http://hdl.handle.net/10722/278828 |
ISSN | 2023 Impact Factor: 9.4 2023 SCImago Journal Rankings: 3.737 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, J | - |
dc.contributor.author | Levinson, D | - |
dc.contributor.author | Wang, J | - |
dc.contributor.author | Zhou, J | - |
dc.contributor.author | Wang, Z-J | - |
dc.date.accessioned | 2019-10-21T02:14:46Z | - |
dc.date.available | 2019-10-21T02:14:46Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of the National Academy of Sciences, 2018, v. 115 n. 50, p. 12710-12715 | - |
dc.identifier.issn | 0027-8424 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278828 | - |
dc.description.abstract | Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces. Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences. Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns. Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle- to high-income groups. Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership. Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home. Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants. | - |
dc.language | eng | - |
dc.publisher | National Academy of Sciences. The Journal's web site is located at http://www.pnas.org | - |
dc.relation.ispartof | Proceedings of the National Academy of Sciences | - |
dc.rights | Proceedings of the National Academy of Sciences. Copyright © National Academy of Sciences. | - |
dc.subject | commuting pattern | - |
dc.subject | job dynamics | - |
dc.subject | housing dynamics | - |
dc.subject | mobility group | - |
dc.subject | smartcard data | - |
dc.title | Tracking job and housing dynamics with smartcard data | - |
dc.type | Article | - |
dc.identifier.email | Zhou, J: zhoujp@hku.hk | - |
dc.identifier.authority | Zhou, J=rp02236 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1073/pnas.1815928115 | - |
dc.identifier.pmid | 30455293 | - |
dc.identifier.pmcid | PMC6294921 | - |
dc.identifier.scopus | eid_2-s2.0-85058326128 | - |
dc.identifier.hkuros | 307752 | - |
dc.identifier.volume | 115 | - |
dc.identifier.issue | 50 | - |
dc.identifier.spage | 12710 | - |
dc.identifier.epage | 12715 | - |
dc.identifier.isi | WOS:000452866000065 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0027-8424 | - |