Anticipating Land Use Change Using Geographically Weighted Regression Models for Discrete Response

Geographically weighted regression (GWR) enjoys wide application in regional science, thanks to its relatively straightforward formulation and explicit treatment of spatial effects. However, its application to discrete-response data sets and land use change at the level of urban parcels has remained a novelty. This work combines logit specifications with GWR techniques to anticipate five categories of land use change in Austin, Texas while controlling for parcel geometry, slope, regional accessibility, local population density, and distances to Austin’s downtown and various roadway types. Results of this multinomial logit GWR model suggest spatial variations in – and significant influence – of these covariates, especially roadway vicinity and regional access. For example, a one-percent increase in the distance on an undeveloped parcel’s distance to its nearest freeway is estimated, on average, to increase the probability of residential development by 1.2%, while the same increase in distance to a major arterial is estimated to increase the probability by 1.8%. Conversely, proximity of roads (via reductions in such distances) is estimated to boost the likelihood of non-residential development (9.0% in the case of commercial development, for simply a 1% decrease in distance to such arterials. The logsum accessibility index is estimated to exert an average positive influence on commercial, office and industrial development tendencies, while dampening land use transitions from an undeveloped state to residential uses. Comparisons of results with a spatial autoregressive binary probit (using all developed land use categories as a single response) and GWR binary probit also provide some insights, with the latter seeming to surpass the former in accounting for spatial effects, as reflected by a lower AIC value. Yiyi Wang Graduate Student Researcher Department of Civil, Architectural & Environmental Engineering The University of Texas at Austin 512-466-8693 [email protected]