36 citations · 41 across the 5 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…
Big Issues for Big Data: challenges for critical spatial data analytics
Chris Brunsdon, Alexis Comber
In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenge…
Opening practice: supporting Reproducibility and Critical spatial data science
Chris Brunsdon, Alexis Comber
This paper reflects on a number of trends towards a more open and reproducible approach to geographic and spatial data science over recent years. In particular it considers trends…
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…
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…