Exploring Contextual Variations in Land Use and Transport Analysis Using a Probit Model with Geographical Weights

Abstract A majority of statistical methods used in the analysis of land use and transportation systems implicitly carry the assumption that relationships are constant across locations or individuals, thus ignoring contextual variation due to geographical or socio-economic heterogeneity. In some cases, where the assumption of constant relationships is questionable, market segmentation procedures are used to study varying relationships. More recently, methodological developments, and a greater awareness of the importance of geography, have led to increasingly sophisticated ways to explore varying relationships in land use and transportation modeling. The objective of this paper is to propose a simple probit model to explore contextual variability in continuous-space. Some conceptual and technical issues are discussed, and an example is presented that reanalyzes land use change using data from California’s BART system. The results of the example suggest that considerable parametric variation exists across geographical space, and thus underlines the relevance of contextual effects.

[1]  S. Openshaw A million or so correlation coefficients : three experiments on the modifiable areal unit problem , 1979 .

[2]  Juan de Dios Ortúzar,et al.  Stated preference in the valuation of interurban road safety. , 2003, Accident; analysis and prevention.

[3]  P. Torrens,et al.  Cellular Automata and Urban Simulation: Where Do We Go from Here? , 2001 .

[4]  R. Carroll,et al.  Variance Function Estimation , 1987 .

[5]  D. McFadden MEASUREMENT OF URBAN TRAVEL DEMAND , 1974 .

[6]  Varameth Vichiensan,et al.  Discrete Choice Model with Structuralized Spatial Effects for Location Analysis , 2004 .

[7]  Darren M. Scott,et al.  Spatial statistics for urban analysis: A review of techniques with examples , 2004 .

[8]  M. Goodchild The Validity and Usefulness of Laws in Geographic Information Science and Geography , 2004 .

[9]  Kazuaki Miyamoto,et al.  A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 2. Spatial Association and Model Specification Tests , 2002 .

[10]  R. Dubin,et al.  Estimating Logit Models with Spatial Dependence , 1995 .

[11]  F. J. Martinez The Bid—Choice Land-Use Model: An Integrated Economic Framework , 1992 .

[12]  J. Landis,et al.  Rail Transit Investments, Real Estate Values, and Land Use Change: A Comparative Analysis of Five California Rail Transit Systems , 1995 .

[13]  An Application of a Switching Regimes Regression to the Study of Urban Structure , 2002 .

[14]  David A. Hensher,et al.  Handbook of Transport Modelling , 2000 .

[15]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[16]  Daniel P. McMillen,et al.  An empirical model of urban fringe land use , 1989 .

[17]  A. Case Neighborhood influence and technological change , 1992 .

[18]  Tetsuo Yai,et al.  Disaggregate behavioural models and their applications in Japan , 1989 .

[19]  D. McMillen PROBIT WITH SPATIAL AUTOCORRELATION , 1992 .

[20]  Fulong Wu,et al.  Simulating artificial cities in a GIS environment: urban growth under alternative regulation regimes , 2000, Int. J. Geogr. Inf. Sci..

[21]  M. Charlton,et al.  Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis , 1998 .

[22]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[23]  A. Páez,et al.  A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity , 2002 .

[24]  Michael G. McNally,et al.  The Four Step Model , 2007 .

[25]  Chandra R. Bhat,et al.  A MIXED SPATIALLY CORRELATED LOGIT MODEL: FORMULATION AND APPLICATION TO RESIDENTIAL CHOICE MODELING , 2004 .

[26]  Kelvyn Jones,et al.  Contextual Models of Urban House Prices: A Comparison of Fixed- and Random-Coefficient Models Developed by Expansion , 1994 .

[27]  P. Atkinson,et al.  Exploring the Relations Between Riverbank Erosion and Geomorphological Controls Using Geographically Weighted Logistic Regression , 2002 .

[28]  Mark Rounsevell,et al.  Exploring a spatio‐dynamic neighbourhood‐based model of residential behaviour in the Brussels periurban area , 2005, Int. J. Geogr. Inf. Sci..

[29]  Craig R. Rindt,et al.  The Activity-Based Approach , 2008 .

[30]  Michael Batty,et al.  Cellular Automata and Urban Form: A Primer , 1997 .

[31]  Chandra R. Bhat,et al.  The spatial analysis of activity stop generation , 2002 .

[32]  Kelvyn Jones,et al.  Specifying and estimating multilevel models for geographical research , 1991 .

[33]  Gary C. White,et al.  Statistical Applications in the Spatial Sciences. , 1981 .