18 citations · 32 across the 7 of their papers we have counts for
7 papers
Scalable coarse-to-fine spatial downscaling
Daisuke Murakami, Yongwan Chun, Takahiro Yoshida +1
This study proposes coarse-to-fine downscaling (CF-DS), a scalable spatial downscaling method extending coarse-to-fine spatial modeling. Unlike conventional spatial-statistical dow…
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…
Spatial prediction of apartment rent using regression-based and machine learning-based approaches with a large dataset
Takahiro Yoshida, Hajime Seya
Employing a large dataset (at most, the order of n = 10^6), this study attempts enhance the literature on the comparison between regression and machine learning (ML)-based rent pri…
gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets
Joseph Emile Honour Percival, Narumasa Tsutsumida, Daisuke Murakami +2
Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local rel…
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…
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…