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What construct one's familiar area?: A quantitative and longitudinal study
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.ORCID iD: 0000-0001-5961-5008
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics.
2019 (English)In: Environment and Planning B: Urban Analytics and City Science, ISSN 2399-8083, Vol. 46, no 2, p. 322-340Article in journal (Refereed) Published
Abstract [en]

There is a lack of understanding of how certain characteristics of the urban environment influence an individual's spatial cognition and familiarity with surrounding areas, and, subsequently, their travel behaviours and how these change over time. This paper aims to address this research gap in exploring the dynamics of individuals' spatial cognitions by observing the changes of respondents' familiar areas over time, and investigating the possible determinants that constitute respondents' familiar areas. Panel data, containing two-week travel diaries and maps of familiar areas, were collected in four different waves over a seven-month period for 55 individuals in Stockholm, Sweden. The reported familiar areas for each individual were digitised into quantifiable variable form and further analysed by applying dynamic binary probit and linear regression models. The results show that, while familiar area is largely influenced by one's previous knowledge of the area, it is also continuously corrected by events in between. Different land use characteristics have different impacts on different social groups' travel patterns, thus contributing to the variability in the size of one's familiar areas.

Place, publisher, year, edition, pages
Sage Publications, 2019. Vol. 46, no 2, p. 322-340
Keywords [en]
Mental map, familiar area, multiday analysis, dynamic binary probit model, Stockholm
National Category
Human Geography
Identifiers
URN: urn:nbn:se:kth:diva-245942DOI: 10.1177/2399808317714798ISI: 000458830400008Scopus ID: 2-s2.0-85041494946OAI: oai:DiVA.org:kth-245942DiVA, id: diva2:1296217
Funder
Integrated Transport Research Lab (ITRL)
Note

QC 20190314

Available from: 2019-03-14 Created: 2019-03-14 Last updated: 2019-06-10Bibliographically approved

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Zhang, WenTermida, Nursitihazlin Ahmad

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CiteExportLink to record
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  • apa
  • harvard1
  • ieee
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Output format
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