115 citations · 174 across the 7 of their papers we have counts for
7 papers
A linearization for stable and fast geographically weighted Poisson regression
Daisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida +3
Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found fo…
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…
Investigating Spatial Error Structures in Continuous Raster Data
Narumasa Tsutsumida, Pedro Rodríguez-Veiga, Paul Harris +2
The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks…
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…