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Daisuke Murakami

Researcher at National Institute for Environmental Studies

Publications -  98
Citations -  1706

Daisuke Murakami is an academic researcher from National Institute for Environmental Studies. The author has contributed to research in topics: Spatial analysis & Spatial dependence. The author has an hindex of 17, co-authored 97 publications receiving 1244 citations. Previous affiliations of Daisuke Murakami include University of Tsukuba.

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Assessing the impacts of 1.5 °C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

Katja Frieler, +60 more
TL;DR: In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Concerning on Climate Change (UNFCCC) invited the Inter- governmental Panel on Climate change (IPCC).

Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

Katja Frieler, +60 more
TL;DR: In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Concerning on Climate Change (UNFCCC) invited the Inter- governmental Panel on Climate change (IPCC) as mentioned in this paper.
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Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling

TL;DR: In this article, the authors downscales the population and gross domestic product (GDP) scenarios given under Shared Socioeconomic Pathways (SSPs) into 05-degree grids.
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Random effects specifications in eigenvector spatial filtering: a simulation study

TL;DR: The main findings of this simulation are that in many cases, parameter estimates of the extended RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE- ESF inMany cases.
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A Moran coefficient-based mixed effects approach to investigate spatially varying relationships

TL;DR: In this article, a spatially varying coefficient model was developed by extending the random effects eigenvector spatial filtering model, which is defined by a linear combination of the eigenvectors describing the Moran coefficient, and each of its coefficients can have a different degree of spatial smoothness.