Daisuke Murakami
Daisuke Murakami
Verified email at ism.ac.jp
Cited by
Cited by
Assessing the impacts of 1.5 C global warming–simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)
K Frieler, S Lange, F Piontek, CPO Reyer, J Schewe, L Warszawski, ...
Geoscientific Model Development, 2017
Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling
D Murakami, Y Yamagata
Sustainability 11 (7), 2106, 2019
Random effects specifications in eigenvector spatial filtering: a simulation study
D Murakami, DA Griffith
Journal of Geographical Systems 17 (4), 311-331, 2015
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships
D Murakami, T Yoshida, H Seya, DA Griffith, Y Yamagata
Spatial Statistics 19, 68-89, 2017
Value of urban views in a bay city: Hedonic analysis with the spatial multilevel additive regression (SMAR) model
Y Yamagata, D Murakami, T Yoshida, H Seya, S Kuroda
Landscape and Urban Planning 151, 89-102, 2016
The importance of scale in spatially varying coefficient modeling
D Murakami, B Lu, P Harris, C Brunsdon, M Charlton, T Nakaya, ...
Annals of the American Association of Geographers, 2019
Eigenvector spatial filtering for large data sets: fixed and random effects approaches
D Murakami, DA Griffith
Geographical analysis, 2018
Participatory sensing data tweets for micro-urban real-time resiliency monitoring and risk management
D Murakami, GW Peters, Y Yamagata, T Matsui
IEEE Access 4, 347-372, 2016
Land price maps of Tokyo metropolitan area
M Tsutsumi, A Shimada, D Murakami
Procedia-Social and Behavioral Sciences 21, 193-202, 2011
Application of LASSO to the eigenvector selection problem in eigenvector‐based spatial filtering
H Seya, D Murakami, M Tsutsumi, Y Yamagata
Geographical Analysis 47 (3), 284-299, 2015
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
D Murakami, DA Griffith
Spatial Statistics, 2019
A comparison of grid-level residential electricity demand scenarios in Japan for 2050
Y Yamagata, D Murakami, H Seya
Applied Energy 158, 255-262, 2015
Spatially filtered unconditional quantile regression: Application to a hedonic analysis
D Murakami, H Seya
Environmetrics, 2019
Area-to-point parameter estimation with geographically weighted regression
D Murakami, M Tsutsumi
Journal of Geographical Systems 17 (3), 207-225, 2015
A new areal interpolation method based on spatial statistics
D Murakami, M Tsutsumi
Procedia-Social and Behavioral Sciences 21, 230-239, 2011
spmoran (ver. 0.2.0): An R package for Moran eigenvector-based scalable spatial additive mixed modeling
D Murakami
ArXiv:1703.04467, 2020
Estimating water–food–ecosystem trade-offs for the global negative emission scenario (IPCC-RCP2. 6)
Y Yamagata, N Hanasaki, A Ito, T Kinoshita, D Murakami, Q Zhou
Sustainability Science 13 (2), 301-313, 2018
Energy demand estimation using quasi-real-time people activity data
T Yoshida, Y Yamagata, D Murakami
Energy Procedia 158, 4172-4177, 2019
Electricity self-sufficient community clustering for energy resilience
Y Yamagata, D Murakami, K Minami, N Arizumi, S Kuroda, T Tanjo, ...
Energies 9 (7), 543, 2016
A first step towards resilient graph partitioning for electrical grids
N Arizumi, K Minami, T Tanjo, H Maruyama, D Murakami, Y Yamagata
7th International Conference on Information and Automation for …, 2014
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