Anticipation of Land use Change through use of Geographically Weighted Regression Models for Discrete Response

Abstract

Geographically weighted regression (GWR) enjoys wide application in regional science, thanks to its relatively straightforward formulation and explicit treatment of spatial effects. The application of GWR to discrete-response data sets and land use change at the level of urban parcels has remained a novelty, however. This paper describes work that combined logit specifications with GWR techniques to anticipate five categories of land use change in Austin, Texas, and controlled 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 suggested spatial variations in—and significant influence of—these covariates, especially roadway vicinity and regional access. A 1% increase in the distance of an undeveloped parcel to the nearest freeway, for example, was estimated, on average, to increase the probability of residential development by 1.2%, while the same increase in distance to a major arterial was estimated to increase the probability by 1.8%. Conversely, proximity to roads (through reductions in such distances) was estimated to boost the likelihood of nonresidential development (e.g., 9.0% in the case of commercial development in response to a 1% decrease in distance to such arterials). The logsum accessibility index was estimated to exert an average positive influence on commercial, office, and industrial development tendencies, while it dampened land use transitions from an undeveloped state to residential uses. Comparisons of results with a spatial autoregressive binary probit (with the use of all developed land use categories as a single response) and GWR binary probit also provided some insights. The latter seemed to surpass the former in its account for spatial effects, as reflected by a lower Akaike information criterion value.

Commercial property values in a border metropolitan economy

“…The GWR improvement value model yields 14,349 regression points with invertible matrices, equivalent to only 13.6% from the sample for commercial properties (Table 3). Wang et al (2012) report a similar outcome where less than 10.0% of the sample yields invertible Hessians. The mean local GWR coefficients for DistInterstate and POE_DrivingTime have signs that are consistent with the OLS parameters, but with greater magnitudes.…”

“…A Koenker-Breusch-Pagan (BP) test is used to examine whether problems with nonstationarity or heteroscedasticity are present (Koenker 1981). To counter local multicollinearity issues associated with insufficient variation of observations neighboring the epicenter u i ; v i ð Þ, adaptive kernels are determined by setting the bandwidth to 1000 neighbors as Wang et al (2012). When the variance inflation factor (VIF) is larger than 7.5 for any variable, local multicollinearity is problematic and that variables is excluded from the GWR specification.…”

“…GWR has proven useful in allowing for such spatial effects (Bitter et al 2007;Wang et al 2012;Páez et al 2007;Yu et al 2007;Farber and Yeates 2006;Kestens et al 2006). One of the critiques of GWR is that multivariate parameter estimates might be intrinsically correlated, making the interpretation of map patterns for individual coefficients difficult.…”

The impact of weight matrices on parameter estimation and inference: A case study of binary response using land-use data

“…Due to the discrete nature of the responses being analyzed, the three models described and applied here rely on Bayesian MCMC estimation techniques. The SAR binary Probit (SARP) specification follows Chapter 10 of LeSage and Pace's (2009) (Wang et al 2011). LeSage and Pace (2004) report MESS estimation to run approximately six times faster than conventional SAR models in the MCMC paradigm for a continuous response.…”

The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data

“…In addition to these local factors, this study also controls for an "Accessibility Index" (AI) as an indicator of the parcel's regional access to jobs, with distances being the shortest-path network distances. More details on variables can be found in Wang et al (2010).…”