Wei Kang

TL;DR: In this article, a multiscale geographically weighted regression (MGWR) is proposed, which allows different processes to operate at different spatial scales by deriving an optimal bandwidth vector in which each element indicates the spatial scale at which a particular process takes place.

TL;DR: In this paper, the authors introduce mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity, and provide novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements in estimation routines.

TL;DR: In this paper, the authors propose a generalized additive model (GMM) for MGWR and derive standard errors for the local parameters in MGWR, which can be used to compare the overall fit of an MGWR model and for each of the covariates within the model.

TL;DR: Concerns are discussed with the PSDM GWR framework in terms of model interpretability, complexity, and computational efficiency, including how to more holistically assess model variations.

TL;DR: PySAL as discussed by the authors is a library for geocomputation and spatial data science written in Python, which has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work.