“Seeing Is Believing”: Exploring Opportunities for the Visualization of Activity–Travel and Land Use Processes in Space–Time

  • Ron N. BuliungEmail author
  • Catherine Morency
Part of the Advances in Spatial Science book series (ADVSPATIAL)


The study of the relationship between activity–travel behaviour and the development of city-regions is a matter of great concern among researchers and urban planners. Much of the current debate focuses on understanding and influencing the relationship between transportation and land use systems, with a view to achieving economic, sustainability, and quality of life policy objectives. The essence of the transport-land use link is that the development of “new” or the presence of “old” transport infrastructure (e.g., road, rail, etc.) increases the relative accessibility and hence attractiveness of place, giving rise to several possible outcomes including: the enhancement of economic growth and spatial interaction. The economic benefits that materialize in this context, however, have been the subject of debate (Black 2001). Accessibility effects have also become prominent in policy-based discourse and research focused on the efficacy of urban design as a mechanism for reducing transports’ negative externalities. Researchers have set out to test the conventional wisdom that placing and mixing the “things” people want to or have to do, close to where people “want” to or “have to” live or work, will facilitate reductions in automobile use, energy consumption, and environmental emissions (e.g., Buliung and Kanaroglou 2006b; Cervero and Kockelman 1997; Crane 2000). The results appear to be somewhat inconsistent, with context specific evidence suggesting that the relationship between transport and land use tends to vary from person to person, and place to place.


Transportation Network Travel Behaviour Central Business District Travel Demand Power Centre 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank the anonymous reviewers for their contributions to this manuscript. The first author wishes to thank Dr. Tony Hernandez at the Centre for the Study of Commercial Activity, Ryerson University for providing access to the retail opportunities data. The second author extends her gratitude to the transport authorities by whom the large-scale surveys, mainly Household Origin-Destination surveys, are conducted. Those surveys authorize the continuation of a travel behaviour observational and analytical culture in the GMA: STM, RTL, STL, AMT and MTQ. Both authors acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC).


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of GeographyUniversity of Toronto MississaugaMississaugaCanada

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