115 citations · 165 across the 6 of their papers we have counts for
6 papers
gwverse: a template for a new generic Geographically Weighted Rpackage
Alexis Comber, Chris Brunsdon, Martin Callaghan +3
GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding proc…
The GWR route map: a guide to the informed application of Geographically Weighted Regression
Alexis Comber, Chris Brunsdon, Martin Charlton +8
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships…
Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels
Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida +2
Although a number of studies have developed fast geographically weighted regression (GWR) algorithms for large samples, none of them has achieved linear-time estimation, which is c…
The importance of scale in spatially varying coefficient modeling
Daisuke Murakami, Binbin Lu, Paul Harris +4
While spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study…
The GWmodel R package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models
Binbin Lu, Paul Harris, Martin Charlton +1
In this study, we present a collection of local models, termed geographically weighted (GW) models, that can be found within the GWmodel R package. A GW model suits situations when…
GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models
Isabella Gollini, Binbin Lu, Martin Charlton +2
Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel, we…