63 citations · 178 across the 24 of their papers we have counts for
14 papers · 1 filter
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…
Analysis of COVID-19 evolution based on testing closeness of sequential data
Tomoko Matsui, Nourddine Azzaoui, Daisuke Murakami
A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied…
Adaptively Robust Geographically Weighted Regression
Shonosuke Sugasawa, Daisuke Murakami
We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function…
Improved log-Gaussian approximation for over-dispersed Poisson regression: application to spatial analysis of COVID-19
Daisuke Murakami, Tomoko Matsui
In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiabili…
Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data
Daisuke Murakami, Mami Kajita, Seiji Kajita +1
As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast an…
Spatially Clustered Regression
Shonosuke Sugasawa, Daisuke Murakami
Spatial regression or geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a r…
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…
A memory-free spatial additive mixed modeling for big spatial data
Daisuke Murakami, Daniel A. Griffith
This study develops a spatial additive mixed modeling (AMM) approach estimating spatial and non-spatial effects from large samples, such as millions of observations. Although fast…
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…
Low rank spatial econometric models
Daisuke Murakami, Hajime Seya, Daniel A. Griffith
This article presents a re-structuring of spatial econometric models in a linear mixed model framework. To that end, it proposes low rank spatial econometric models that are robust…
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
Daisuke Murakami, Daniel A. Griffith
While spatially varying coefficient (SVC) modeling is popular in applied science, its computational burden is substantial. This is especially true if a multiscale property of SVC i…
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…
Eigenvector spatial filtering for large data sets: fixed and random effects approaches
Daisuke Murakami, Daniel A. Griffith
Eigenvector spatial filtering (ESF) is a spatial modeling approach, which has been applied in urban and regional studies, ecological studies, and so on. However, it is computationa…
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships
Daisuke Murakami, Takahiro Yoshida, Hajime Seya +2
This study develops a spatially varying coefficient model by extending the random effects eigenvector spatial filtering model. The developed model has the following properties: its…