| 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 | 589 | 2017 |
| Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling D Murakami, Y Yamagata Sustainability 11 (7), 2106, 2019 | 202 | 2019 |
| Random effects specifications in eigenvector spatial filtering: a simulation study D Murakami, DA Griffith Journal of Geographical Systems 17, 311-331, 2015 | 101 | 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 | 95 | 2017 |
| 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 | 92 | 2023 |
| Eigenvector spatial filtering for large data sets: fixed and random effects approaches D Murakami, DA Griffith Geographical analysis, 2018 | 74 | 2018 |
| 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 | 71 | 2019 |
| 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 | 61 | 2016 |
| 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 | 54 | 2018 |
| Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions D Murakami, DA Griffith Spatial Statistics, 2019 | 52 | 2019 |
| 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 | 51 | 2021 |
| 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 | 47 | 2018 |
| Gridded GDP projections compatible with the five SSPs (shared socioeconomic pathways) D Murakami, T Yoshida, Y Yamagata Frontiers in Built Environment 7, 760306, 2021 | 42 | 2021 |
| 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 | 42 | 2020 |
| 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 | 41 | 2015 |
| 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 | 37 | 2016 |
| Land price maps of Tokyo metropolitan area M Tsutsumi, A Shimada, D Murakami Procedia-Social and Behavioral Sciences 21, 193-202, 2011 | 30 | 2011 |
| Energy demand estimation using quasi-real-time people activity data T Yoshida, Y Yamagata, D Murakami Energy Procedia 158, 4172-4177, 2019 | 27 | 2019 |
| 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 | 25 | 2020 |
| 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 |
