First, recent trends in the availability of analytical tools and data allow for the realistic representation of the complex objective environment for the analysis of human spatial behavior (Kwan 1997). If detailed attributes of land parcels and the transportation systems can be represented through incorporating the relevant information into a comprehensive geographic database, the analyst may go beyond the simplified and geometric operationalization of geographic constructs as often done in traditional spatial analysis. For example, instead of using the straight-line distance between two locations, the actual travel distance over the transportation network can be used (as in Talen 1997 and Kwan 1998). Further, given the more realistic geographic environment represented in the GIS, it is possible for the analyst to perform “non-isotropic” spatial analysis, which does not depend on any assumed spatial distribution of opportunities in the urban environment (Tobler 1993).
Second, with appropriate data collection effort and using the spatial data handling capabilities of GIS, elements of individual cognitive map which bear upon spatial behavior may be incorporated into analytical models (Golledge et al 1994; Kwan and Hong 1998). By taking into account factors which affect human spatial behavior (e.g. cognitive and space-time constraints) through establishing more realistic representations of the subjective environment, spatial analysis in a GIS environment can be based upon the more relevant “effective” environment of individuals. This will extend the theoretical foundation of spatial analysis to include the behavioral dimensions into the analytical framework (Fotheringham 1993).
Third, using geo-referenced individual-level data in a GIS, spatial analysis will no longer be affected by any prior zonal or areal partition of the study area (as in the case where socio-demographic data are aggregated based on a zonal schema) (Kwan 1998). This implies a shift from traditional methods to new techniques for specific problems. For example, point-pattern techniques (such as cross K-function) may be more appropriate than conventional zone-based methods for measuring individual accessibility to urban opportunities when individual-level data are used. This, in other words, allows for the use and development of “frame independent” spatial analytical methods (Tobler 1989), which may help ameliorate the modifiable areal unit problem.
Changes in the above three areas will allow for the application of new methods to specific problem areas pertaining to human spatial behavior. Further, by placing the individual into the focus of spatial analysis, and with considerations of both the objective and subjective environment, such person-based and frame independent framework will enable the examination of fine-scaled, inter-personal differences based on gender, race, or other socially significant categories. This, perhaps, could be the beginning point for a mode of spatial analysis which is more congenial to poststructuralist and feminist conception of space and the individual. Obviously, much research is needed to examine this possibility,
Golledge, R.G., M.-P. Kwan and T. Garling (1994) “Computational-process modeling of household travel decisions using a geographical information system.” Papers in Regional Science, 73(2):99-117.
Kwan, M.-P. (1997) “GISICAS: An activity-based travel decision support system using a GIS-interfaced computational-process model” In Activity-Based Approaches To Travel Analysis, 263-282, edited by D.F. Ettema and H.J.P. Timmermans. Pergamon: New York.
Kwan, M.-P. (1998) “Space-time and integral measures of individual accessibility:
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30(3): 191- 216.
Kwan, M.-P. and X.-D. Hong (1998) “Network-based constraints-oriented
choice set formation using GIS” Geographical Systems, in press.
Talen, E. (1997) “The social equity of urban service distribution: An exploration of park access in Pueblo, Colorado, and Macon, Georgia” Urban Geography 18:521-541.
Tobler, W.R. (1989) “Frame independent spatial analysis” In The Accuracy of Spatial Databases, 115-122, edited by M. Goodchild and S. Gopal. Taylor and Francis: London.
Tobler, W.R. (1993) Three Presentations on Geographical Analysis and Modeling. NCGIS Technical Report 93-1.
1998 “Information Representation For Driver Decision Support Systems.” In Theoretical Foundations of Travel Choice Modelling, edited by Tommy Garling, Thomas Laitila and Kerstin Westin. Elsevier Science: New York. (M.-P. Kwan, R. G. Golledge and J. Speigle).
1997 “GISICAS: An Activity-Based Travel Decision Support System Using a GIS-Interfaced Computational-Process Model.” In Activity-Based Approaches To Travel Analysis, 263-282, edited by Dick F. Ettema and Harry J.P. Timmermans. Pergamon: New York.
1997 “Computational Process Modelling of Disaggregate Travel Behaviour.” In Recent Developments in Spatial Analysis: Spatial Statistics, Behavioural Modelling and Neurocomputing”, 171-185, edited by Manfred M. Fischer and Arthur Getis. Springer-Verlag: Berlin. (M.-P. Kwan and R. G. Golledge).
1996 Research Planning Grant for Women Scientists and Engineers, the National Science Foundation for “A Study of Gender/Ethnic Differences in Activity-Travel Patterns Using Geographical Information Systems.” 4/96-6/98.
1995 Research Grant from the Urban Affairs Committee, the Ohio State University for “Women’s Access to Urban Opportunities in Columbus, Ohio.” 7/95-12/96.