36 citations · 67 across the 7 of their papers we have counts for
5 papers · 1 filter
Coarse-to-fine spatial modeling: A scalable, machine-learning-compatible spatial model
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional cov…
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…
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…