Advertisement

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

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

Abstract

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.

Keywords

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.

Notes

Acknowledgements

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).

References

  1. Al-Kodmany K (1999) Using visualization techniques for enhancing public participation in planning and design: process, implementation, and evaluation. Landsc Urban Plann 45:37–45CrossRefGoogle Scholar
  2. Al-Kodmany K (2002) Visualization tools and methods in community planning: from freehand sketches to virtual reality. J Plann Lit 17:189–211CrossRefGoogle Scholar
  3. Anas A, Arnott R, Small KA (1998) Urban spatial structure. J Econ Lit 36:1426–1464Google Scholar
  4. Anselin L (1995) Local indicators of spatial association – LISA. Geogr Anal 27:93–115CrossRefGoogle Scholar
  5. Anselin L (2000) Computing environments for spatial data analysis. J Geogr Syst 2:201–220CrossRefGoogle Scholar
  6. Anselin L, Syabri I, Kho Y (2006) GeoDa: an introduction to spatial data analysis. Geogr Anal 38:5–22CrossRefGoogle Scholar
  7. Bachi R (1963) Standard distance measures and related methods for spatial analysis. Pap Reg Sci Assoc 10:83–132CrossRefGoogle Scholar
  8. Baddley A, Turner R (2005) Spatstat: an R package for analyzing spatial point patterns. J Stat Software 12:1–42Google Scholar
  9. Badoe DA, Miller EJ (2000) Transportation-land use interaction: empirical findings in North America, and their implications for modeling. Transport Res D 5:235–263CrossRefGoogle Scholar
  10. Bailey TC, Gatrell AC (1995) Interactive spatial data analysis. Addison-Wesley-Longman, CambridgeGoogle Scholar
  11. Bivand RS (2006) Implementing spatial data analysis software tools in R. Geogr Anal 38:23–40CrossRefGoogle Scholar
  12. Bivand RS, Neteler M (2000) Open source geocomputation: using the R data analysis language integrated with GRASS GIS and PostgreSQL data base systems. Presented at GeoComputation 2000, University of Greenwhich, KentGoogle Scholar
  13. Black WR (2001) An unpopular essay on transportation. J Transport Geogr 9:1–11CrossRefGoogle Scholar
  14. Bonnafous A, Tabourin E (1998) Modélisation de l’évolution des densités urbaines, in Données Urbaines n ∘ 2, ed. Anthropos, mai 98: 273–285Google Scholar
  15. Borgeat L, Godin G, Massicotte P, Poirier G, Blais F, Beraldin J-A (2007) Visualizing and analysing the Mona Lisa. Real-time interaction with complex models, IEEE Computer Society, Nov./Dec. 2007Google Scholar
  16. Buliung RN (2001) Spatiotemporal patterns of employment and non-work activities in Portland, Oregon. In: Proceedings of the 2001 ESRI International User Conference, San Diego, CaliforniaGoogle Scholar
  17. Buliung RN (2007) Broadband technology and metropolitan sustainability: an interpretive review. Report submitted to The Ministry of Government Services, Province of Ontario, Broadband Research Initiative. Available at: http://kmdi.utoronto.ca/broadband/publications/default.html, 29 Sept. 2009
  18. Buliung RN, Hernandez T (2007) The growth and change of retail opportunity in the Greater Toronto Area. Paper presented at the 54th Annual Meeting of the North American Regional Science Association International, Savannah, GA, November 2007Google Scholar
  19. Buliung RN, Kanaroglou PS (2006a) A GIS toolkit for exploring geographies of household activity/travel behavior. J Transport Geogr 14:35–51CrossRefGoogle Scholar
  20. Buliung RN, Kanaroglou PS (2006b) Urban form and household activity-travel behaviour. Growth Change 37:174–201CrossRefGoogle Scholar
  21. Buliung RN, Remmel TK (2008) Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package. J Geogr Syst 10:191–216CrossRefGoogle Scholar
  22. Buliung RN, Roorda MJ, Remmel T (2008) Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS). Transportation 35:697–722CrossRefGoogle Scholar
  23. Center for Spatially Integrated Social Sciences (CSISS) (2008) CSISS classics. Available at: http://www.csiss.org/classics/. Accessed Mar 2008
  24. Cervero R, Kockelman KM (1997) Travel demand and the 3ds: density, diversity, and design. Transport Res D 2:199–219CrossRefGoogle Scholar
  25. Chapleau R, Morency C (2005) Dynamic spatial analysis of urban travel survey data using GIS. Twenty-Fifth Annual ESRI International User Conference, San Diego, CaliforniaGoogle Scholar
  26. Civic Transportation Committee [map] (1915) Scale not given. “Diagram Showing Homeward Passenger Movement During The Evening Rush Period Mid-week Conditions 4–30 To 7–30 P.M.” University of Toronto Data, Map & Geographic Information Systems Centre. http://prod.library.utoronto.ca:8090/maplib/digital/rushhour.jpg. 24 Sept. 2008
  27. Crane R (2000) The influence of urban form on travel: an interpretive review. J Plann Lit 15:3–23CrossRefGoogle Scholar
  28. Dodge M, Kitchin, R (2001) Mapping cyberspace. Routledge, New YorkGoogle Scholar
  29. Downs, A. (2004) Still stuck in traffic. Brookings Institute, WashingtonGoogle Scholar
  30. Ebdon D (1988) Statistics in Geography, 2nd edn. Blackwell, OxfordGoogle Scholar
  31. Gahegan M (2000) The case for inductive and visual techniques in the analysis of spatial data. J Geogr Syst 2:77–83CrossRefGoogle Scholar
  32. Goodchild, MF, Janelle, DG (1984) The city around the clock: space-time patterns of urban ecological structure. Environ Plann A 16:807–820CrossRefGoogle Scholar
  33. Haining R, Wise S (1997) Exploratory spatial data analysis, NCGIA Core Curriculum in GIScience. Available at: http://www.ncgia.ucsb.edu/giscc/units/u128/u128.html, Mar. 2008
  34. Haining R, Wise S, Ma J (1998) Exploratory spatial data analysis in a geographical information system environment. The Statistician 47:457–469Google Scholar
  35. Haining R, Wise S, Ma J (2000) Designing and implementing software for spatial statistical analysis in a GIS environment. J Geogr Syst 2:257–286CrossRefGoogle Scholar
  36. Haining R, Wise S, Signoretta P (2000) Providing scientific visualization for spatial data analysis: criteria and an assessment of SAGE. J Geogr Syst 2:121–140CrossRefGoogle Scholar
  37. Health Canada (2002) Canada’s aging population. (Cat. H39–608/2002E). Minister of Public Works and Government Services, Ottawa, CanadaGoogle Scholar
  38. Hearnshaw HM, Unwin D (1994) Visualization in geographical information systems. JohnWiley, ChichesterGoogle Scholar
  39. Heisz A., LaRochelle-Côté S (2005) Work and commuting in census metropolitan areas, 1996–2001. (Catalogue No. 89–613-MIE). Ottawa, Statistics CanadaGoogle Scholar
  40. Janelle DG, Goodchild M (1983) Diurnal patterns of social group distributions in a Canadian city. Econ Geogr 59:403–425CrossRefGoogle Scholar
  41. Jones K, Doucet M (2000) Big-box retailing and the urban retail structure: the case of the Toronto area. J Retailing Consum Serv 7:233–247CrossRefGoogle Scholar
  42. Jones, P.M. (1979) New approaches to understanding travel behavior: the human activity approach. In: Hensher DA, Stopher PR (eds) Behavioural travel modelling, Redwood Burn Ltd, London, pp 55–80Google Scholar
  43. Kwan, MP (2000) Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transport Res C 8:185–203CrossRefGoogle Scholar
  44. Levine N (2006) Crime mapping and the Crimestat program. Geogr Anal 38:41–56CrossRefGoogle Scholar
  45. Lewis JL, Sheppard SRJ (2006) Culture and communication: can landscape visualization improve forest management consultation with indigenous communities? Landsc Urban Plann 77:291–313CrossRefGoogle Scholar
  46. MacEachren AM (1995) How maps work: representation, visualization, and design. The Guilford Press, New York.Google Scholar
  47. MacEachren AM, Kraak M-J (2001) Research challenges in geovisualization. Cartography and Geographic Information Science 28:3–12CrossRefGoogle Scholar
  48. Maoh H, Kanaroglou PS (2007) Geographic clustering of firms and urban form: a multivariate analysis. J Geogr Syst 9:29–52CrossRefGoogle Scholar
  49. Morency C (2004) Contributions à la modélisation totalement désagrégée des interactions entre mobilité urbaine et dynamiques spatiales, Thèse de doctorat, École Polytechnique de MontréalGoogle Scholar
  50. Morency C, Chapleau R (2008) Age and its relation with home location, household structure and travel behaviors: 15 years of observation. Paper presented at the 86th Annual Meeting of the Transportation Research Board, Washington, DCGoogle Scholar
  51. Morency Catherine, Trépanier Martin, Characterizing parking spaces using travel survey data, CIRRELT, CIRRELT-2008-15, 2008Google Scholar
  52. Morency C, Saubion B, Trépanier M (2006) Evaluating the use of parking spaces in strategic urban areas using travel survey data. Paper presented at the 2006 North American Meetings of the Regional Science Association International 53rd Annual Conference, TorontoGoogle Scholar
  53. Openshaw S, Taylor PJ (1979) A million or so correlation coefficients: three experiments on the modifiable areal unit problem. In: Wrigley N (ed) Statistical applications in the spatial sciences. Pion, London, pp 127–144Google Scholar
  54. Peguy P-Y (2002) Analyse économique des configurations urbaines et de leur étalement, Thèse pour le Doctorat en Sciences Économiques, mention Économie des Transports, Université Lumière Lyon 2, Faculté de Sciences Économiques et de GestionGoogle Scholar
  55. Rey SJ, Janikas MV (2006) STARS: space-time analysis of regional systems. Geogr Anal 38:67–86CrossRefGoogle Scholar
  56. Rowlingson BS, Diggle PJ (1993) Splancs: spatial point pattern analysis code in s-plus. Comput Geosci 19:627–655CrossRefGoogle Scholar
  57. Scheou, B (1998) L’estimation de la population totale à un niveau communal: utilisation du modèle de René Bussière, Document de travail no 98/01, http://web.mrash.fr/let/francais/indexpub.htm, Dec. 2002
  58. Shaw S-L, Wang D (2000) Handling disaggregate spatiotemporal travel data in GIS. GeoInformatica 4:161–117CrossRefGoogle Scholar
  59. Shaw S-L, Bombom LS, Wu H (2008) A space-time GIS approach to exploring large individual-based spatiotemporal datasets. Transactions in GIS 12:425–441CrossRefGoogle Scholar
  60. Shearmur R, Coffey WJ (2002) A tale of four cities: intrametropolitan employment distribution in Toronto, Montreal, Vancouver, and Ottawa–Hull, 1981–1996. Environ Plann A 34:575–598CrossRefGoogle Scholar
  61. Takatsuka M, Gahegan M (2002) GeoVISTA studio: a codeless visual programming environment for geoscientific data analysis and visualization. Comput Geosci 28:1131–1144CrossRefGoogle Scholar
  62. Time (2007) One day in America. Available at: http://www.time.com/time/2007/america_numbers/commuting.html, Mar. 2008
  63. Tress B, Tress G (2003) Scenario visualisation for participatory landscape planning – a study from Denmark. Landsc Urban Plann 64:161–178CrossRefGoogle Scholar
  64. Tufte ER (2001) The visual display of quantitative information, 2nd edn. Graphics Press, CheshireGoogle Scholar
  65. Tukey J (1977) Exploratory data analysis, Addison-Wesley, CambridgeGoogle Scholar
  66. US Census Bureau (2007) Mean centre of the population of the United States: 1790 to 2000. Available at: http://www.census/gov/geo/www/cenpop/cntpop2k.html. Accessed Dec 2007
  67. Willson RW, Shoup DC (1990) Parking subsidies and travel choices: assessing the evidence. Transportation 17:141–157CrossRefGoogle Scholar
  68. Wise SM, Haining RP, Signoretta P (1999) Scientific visualization and the exploratory analysis of area-based data. Environ Plann A 31:1825–1838CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of GeographyUniversity of Toronto MississaugaMississaugaCanada

Personalised recommendations