Perspectives on Explanatory Spatial Modeling

Music City Center 

Providing spatial explanations of observable phenomena – in a formal causal sense or an informal exploratory sense – is a fundamental objective in geography, the science of place and space. Geographers ask questions about how humans use space and interact with the environment, explore physical and social mechanisms behind differences in places, and seek to understand the Earth through its human and natural complexities. Many such spatially-oriented questions cannot be directly addressed using traditional spatial regression models, which are designed primarily for purposes of spatial prediction and smoothing as opposed to spatial explanation. As an alternative to spatial regression modeling, a data analytic technique known as geographically weighted regression (GWR) was introduced in the geography literature nearly 30 years ago. Despite not receiving much attention in the statistics literature and having some well-studied limitations, GWR remains a popular data analytic tool in the fields of geography and spatial epidemiology. In this presentation, I will review some of David Wheeler's important early contributions to GWR and its model-based counterpart, the spatially-varying coefficient model. I will also make connections between the motivation for and criticisms of GWR and themes in the emerging body of statistical research on spatial causal inference.

spatial statistics

causal inference