Authors: Mehak Sachdeva*, Arizona State University, Stewart Fotheringham, SPARC, Arizona State University, Ziqi Li, Department of Geography and GIS, UIUC, Hanchen Yu, SPARC, Arizona State University
Topics: Spatial Analysis & Modeling, Quantitative Methods
Keywords: Generalized Additive Models, Multiscale Geographically Weighted Regression, non-linear regression, spatial regression, spatially varying processes
Session Type: Virtual Paper
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 18
Presentation File: No File Uploaded
This research concerns the study of spatially varying parameter estimates commonly obtained in the calibration of local statistical models. The interpretation of the variation observed in such spatially varying parameters is typically explained in terms of spatially varying processes. The GAM framework, however, provides an alternative interpretation of the varying nature of local estimates in terms of nonlinearity. Consequently, there is a problem in determining the provenance of the variation observed in local estimates from local modeling. This paper highlights this issue and provides a simple diagnostic test which should be applied to all studies of local parameter estimates before ascribing such variation to process spatial nonstationarity. The test is demonstrated first through a simulated dataset which provides control over the induced spatial nonstationarity and modeled nonlinearity and is then applied to two real world datasets – one in the real estate research and the other on voting behavior.