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A Methodology for the Disaggregate, Multidimensional Measurement of Residential Neighbourhood Type

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  • Bagley, Michael N.
  • Mokhtarian, Patricia L.
  • Kitamura, Ryuichi

Abstract

Binary designation of a residential neighbourhood as either traditional or suburban is a distortion of reality, since a location may have some characteristics of both types and since residents in different parts of the neighbonrhood may perceive its character differently. This paper presents and applies a methodology for assessing neighbourhood type that results in a measure that is continuous rather than binary, disaggregate rather than aggregate, and potentially multidimensional. Specifically, 18 variables identified by the literature as distinguishing traditional and suburban locations are measured for 852 residents of 5 San Francisco area neighbourhoods. These data are factor-analysed to develop scales on which each individual has a person-specific score. Although we expected a single ’tradifionainess’ dimension to result, instead we found two factors: traditional and suburban. Study neighbourhoods could and did score highly on both dimensions, and considerable individual variation within neighbourhood was observed. By more accurate|y capturing the complexity in classifying a neighbourhood and the heterogeneity of individual perception within a neighbourhood, use of this methodology to measure neighbourhood type is expected to improve models involving residential location as an endogenous or exogenous variable.

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  • Bagley, Michael N. & Mokhtarian, Patricia L. & Kitamura, Ryuichi, 2002. "A Methodology for the Disaggregate, Multidimensional Measurement of Residential Neighbourhood Type," University of California Transportation Center, Working Papers qt4g44z01p, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt4g44z01p
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    Cited by:

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    2. Ettema, Dick, 2010. "The impact of telecommuting on residential relocation and residential preferences: A latent class modelling approach," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(1), pages 7-24.
    3. Patterson, Zachary & Saddier, Simon & Rezaei, Ali & Manaugh, Kevin, 2014. "Use of the Urban Core Index to analyze residential mobility: the case of seniors in Canadian metropolitan regions," Journal of Transport Geography, Elsevier, vol. 41(C), pages 116-125.
    4. Circella, Giovanni & Alemi, Farzad & Tiedeman, Kate & Berliner, Rosaria M & Lee, Yongsung & Fulton, Lew & Mokhtarian, Patricia L & Handy , Susan, 2017. "What Affects Millennials’ Mobility? PART II: The Impact of Residential Location, Individual Preferences and Lifestyles on Young Adults’ Travel Behavior in California," Institute of Transportation Studies, Working Paper Series qt5kc117kj, Institute of Transportation Studies, UC Davis.
    5. Schwanen, Tim & Mokhtarian, Patricia L., 2005. "What Affects Commute Mode Choice: Neighborhood Physical Structure or Preferences Toward Neighborhoods?," University of California Transportation Center, Working Papers qt4nq9r1c9, University of California Transportation Center.
    6. Schwanen, Tim & Mokhtarian, Patricia L., 1998. "Does Dissonance Between Desired and Current Residential Neighbourhood Type Affect Individual Travel Behaviour? An Empirical Assessment From the San Francisco Bay Area," University of California Transportation Center, Working Papers qt26k8w6xf, University of California Transportation Center.
    7. FAbio DUARTE & Rafael BARCZAK & Yumi YAMAWAKI, 2016. "Urban Transportation And Major Sporting Events?What Is Left After The Games: An Analysis Of Sydney And Cape Town," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 11(1), pages 41-58, February.
    8. Christopher Harding & Zachary Patterson & Luis F Miranda-Moreno & Seyed Amir Zahabi, 2014. "A Spatial and Temporal Comparative Analysis of the Effects of Land-Use Clusters on Activity Spaces in Three Quebec Cities," Environment and Planning B, , vol. 41(6), pages 1044-1062, December.
    9. Popovich, Natalie & Spurlock, C. Anna & Needell, Zachary & Jin, Ling & Wenzel, Tom & Sheppard, Colin & Asudegi, Mona, 2021. "A methodology to develop a geospatial transportation typology," Journal of Transport Geography, Elsevier, vol. 93(C).
    10. Tim Schwanen & Patricia L Mokhtarian, 2004. "The Extent and Determinants of Dissonance between Actual and Preferred Residential Neighborhood Type," Environment and Planning B, , vol. 31(5), pages 759-784, October.
    11. Jago Dodson, 2014. "Suburbia under an Energy Transition: A Socio-technical Perspective," Urban Studies, Urban Studies Journal Limited, vol. 51(7), pages 1487-1505, May.
    12. Jia Guo & Tao Feng & Harry J. P. Timmermans, 2020. "Modeling co-dependent choice of workplace, residence and commuting mode using an error component mixed logit model," Transportation, Springer, vol. 47(2), pages 911-933, April.
    13. Kees Maat & Paul de Vries, 2006. "The Influence of the Residential Environment on Green-Space Travel: Testing the Compensation Hypothesis," Environment and Planning A, , vol. 38(11), pages 2111-2127, November.
    14. Aiga Stokenberga, 2019. "How family networks drive residential location choices: Evidence from a stated preference field experiment in Bogotá, Colombia," Urban Studies, Urban Studies Journal Limited, vol. 56(2), pages 368-384, February.

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