Anticipation of land use change through use of geographically weighted regression models for discrete response

摘要: Geographically weighted regression (GWR) enjoys wide application in regional science, thanks to its relatively straightforward formulation and explicit treatment of spatial effects. The GWR discrete-response data sets land use change at the level urban parcels has remained a novelty, however. This paper describes work that combined logit specifications with techniques anticipate five categories Austin, Texas, controlled for parcel geometry, slope, accessibility, local population density, distances Austin's downtown various roadway types. Results this multinomial model suggested variations in—and significant influence of—these covariates, especially vicinity access. A 1% increase distance an undeveloped nearest freeway, example, was estimated, on average, probability residential development by 1.2%, while same ma...