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Daisuke Murakami
Daisuke Murakami
在 ism.ac.jp 的电子邮件经过验证
标题
引用次数
引用次数
年份
Assessing the impacts of 1.5  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 10 (12), 4321-4345, 2017
5892017
Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling
D Murakami, Y Yamagata
Sustainability 11 (7), 2106, 2019
2022019
Random effects specifications in eigenvector spatial filtering: a simulation study
D Murakami, DA Griffith
Journal of Geographical Systems 17, 311-331, 2015
1012015
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
952017
A route map for successful applications of geographically weighted regression
A Comber, C Brunsdon, M Charlton, G Dong, R Harris, B Lu, Y Lü, ...
Geographical Analysis 55 (1), 155-178, 2023
922023
Eigenvector spatial filtering for large data sets: fixed and random effects approaches
D Murakami, DA Griffith
Geographical analysis, 2018
742018
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
712019
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
612016
Mapping building carbon emissions within local climate zones in Shanghai
Y Wu, A Sharifi, P Yang, H Borjigin, D Murakami, Y Yamagata
Energy Procedia 152, 815-822, 2018
542018
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
D Murakami, DA Griffith
Spatial Statistics, 2019
522019
Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels
D Murakami, N Tsutsumida, T Yoshida, T Nakaya, B Lu
Annals of the American Association of Geographers, 2021
512021
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, 301-313, 2018
472018
Gridded GDP projections compatible with the five SSPs (shared socioeconomic pathways)
D Murakami, T Yoshida, Y Yamagata
Frontiers in Built Environment 7, 760306, 2021
422021
Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis
D Yu, D Murakami, Y Zhang, X Wu, D Li, X Wang, G Li
Transportation Research Part B: Methodological 133, 21-37, 2020
422020
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
412015
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
372016
Land price maps of Tokyo metropolitan area
M Tsutsumi, A Shimada, D Murakami
Procedia-Social and Behavioral Sciences 21, 193-202, 2011
302011
Energy demand estimation using quasi-real-time people activity data
T Yoshida, Y Yamagata, D Murakami
Energy Procedia 158, 4172-4177, 2019
272019
Spatial modeling and design of smart communities
T Yoshida, Y Yamagata, S Chang, V de Gooyert, H Seya, D Murakami, ...
Urban Systems Design, 199-255, 2020
252020
spmoran (ver. 0.2.0): An R package for Moran eigenvector-based scalable spatial additive mixed modeling
D Murakami
ArXiv:1703.04467, 2020
23*2020
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