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Space-Time Diaries and Travel Characteristics for Different Levels of Respondent Aggregation

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  • D G Janelle
  • M F Goodchild
  • B Klinkenberg

    (Department of Geography, University of British Columbia, 1984 West Mall, Vancouver, V6T 1W5 Canada)

Abstract

Significant progress has been made in the analysis of space—time diary data. Drawing on the flexibility that such data provide, in this study the authors group respondents at five different levels of aggregation, and compare them according to their mean and standard deviation values for selected measures of travel behaviour. The measures, derived from the time—geography model, relate to the range and speed of daily movement and to the duration of activities. Wide variation in values were observed among subpopulations and role groups at each level of aggregation and, in general, these increased for higher levels of disaggregation. Graphic plots of the mean and standard deviation values permit evaluations of the effects of aggregation and provide a basis for identification of relationships between respondents' sociodemographic characteristics and their travel behaviour.

Suggested Citation

  • D G Janelle & M F Goodchild & B Klinkenberg, 1988. "Space-Time Diaries and Travel Characteristics for Different Levels of Respondent Aggregation," Environment and Planning A, , vol. 20(7), pages 891-906, July.
    • Handle: RePEc:sae:envira:v:20:y:1988:i:7:p:891-906
    DOI: 10.1068/a200891
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    References listed on IDEAS

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    1. Eric I. Pas, 1983. "A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior," Transportation Science, INFORMS, vol. 17(4), pages 405-429, November.
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    Cited by:

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    2. Shoval, Noam & Auslander, Gail & Cohen-Shalom, Kineret & Isaacson, Michal & Landau, Ruth & Heinik, Jeremia, 2010. "What can we learn about the mobility of the elderly in the GPS era?," Journal of Transport Geography, Elsevier, vol. 18(5), pages 603-612.
    3. Michal Isaacson & Noam Shoval & Hans-Werner Wahl & Frank Oswald & Gail Auslander, 2016. "Compliance and data quality in GPS-based studies," Transportation, Springer, vol. 43(1), pages 25-36, January.
    4. Versichele, Mathias & de Groote, Liesbeth & Claeys Bouuaert, Manuel & Neutens, Tijs & Moerman, Ingrid & Van de Weghe, Nico, 2014. "Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium," Tourism Management, Elsevier, vol. 44(C), pages 67-81.
    5. Andrew Harvey, 1990. "Time use studies for leisure analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 23(4), pages 309-336, December.

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