This page has only limited features, please log in for full access.
DAISUKE MURAKAMI received his Ph.D. degree in engineering from University of Tsukuba in 2014. From 2014 to July 2017, he was a research associate of the National Institute for Environmental Studies, Japan. After August 2017, he is an assistant professor in the Institute of Statistical Mathematics. His research interests include spatial and temporal statistics, quantitative geography, urban analysis, etc.
Spatial regression and geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a region. In contrast, relationships between response and auxiliary variables are expected to exhibit complex spatial patterns in many applications. This paper proposes a new approach for spatial regression, called spatially clustered regression, to estimate possibly clustered spatial patterns of the relationships. We combine K-means-based clustering formulation and penalty function motivated from a spatial process known as Potts model for encouraging similar clustering in neighboring locations. We provide a simple iterative algorithm to fit the proposed method, scalable for large spatial datasets. Through simulation studies, the proposed method demonstrates its superior performance to existing methods even under the true structure does not admit spatial clustering. Finally, the proposed method is applied to crime event data in Tokyo and produces interpretable results for spatial patterns. The R code is available at https://github.com/sshonosuke/SCR.
Shonosuke Sugasawa; Daisuke Murakami. Spatially clustered regression. Spatial Statistics 2021, 44, 100525 .
AMA StyleShonosuke Sugasawa, Daisuke Murakami. Spatially clustered regression. Spatial Statistics. 2021; 44 ():100525.
Chicago/Turabian StyleShonosuke Sugasawa; Daisuke Murakami. 2021. "Spatially clustered regression." Spatial Statistics 44, no. : 100525.
As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data including spatial and other effects. A specific advantage of the proposed CAMM is that it requires no explicit assumption of data distribution unlike existing AMMs. Monte Carlo experiments show the estimation accuracy and computational efficiency of CAMM for modeling non-Gaussian data including fat-tailed and/or skewed distributions. Finally, the model is applied to crime data to examine the empirical performance of the regression analysis and prediction. The result shows that CAMM provides intuitively reasonable coefficient estimates and outperforms AMM in terms of prediction accuracy. CAMM is verified to be a fast and flexible model that potentially covers a wide variety of non-Gaussian data modeling.
Daisuke Murakami; Mami Kajita; Seiji Kajita; Tomoko Matsui. Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data. Spatial Statistics 2021, 43, 100520 .
AMA StyleDaisuke Murakami, Mami Kajita, Seiji Kajita, Tomoko Matsui. Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data. Spatial Statistics. 2021; 43 ():100520.
Chicago/Turabian StyleDaisuke Murakami; Mami Kajita; Seiji Kajita; Tomoko Matsui. 2021. "Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data." Spatial Statistics 43, no. : 100520.
A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.
Daisuke Murakami; Mami Kajita; Seiji Kajita. Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis. ISPRS International Journal of Geo-Information 2020, 9, 577 .
AMA StyleDaisuke Murakami, Mami Kajita, Seiji Kajita. Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis. ISPRS International Journal of Geo-Information. 2020; 9 (10):577.
Chicago/Turabian StyleDaisuke Murakami; Mami Kajita; Seiji Kajita. 2020. "Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis." ISPRS International Journal of Geo-Information 9, no. 10: 577.
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 considered a requisite for big data analysis in machine learning, geostatistics, and related domains. Against this backdrop, this study proposes a scalable GWR (ScaGWR) for large data sets. The key improvement is the calibration of the model through a precompression of the matrices and vectors whose size depends on the sample size, prior to the leave-one-out cross-validation, which is the heaviest computational step in conventional GWR. This precompression allows us to run the proposed GWR extension so that its computation time increases linearly with the sample size. With this improvement, the ScaGWR can be calibrated with 1 million observations without parallelization. Moreover, the ScaGWR estimator can be regarded as an empirical Bayesian estimator that is more stable than the conventional GWR estimator. We compare the ScaGWR with the conventional GWR in terms of estimation accuracy and computational efficiency using a Monte Carlo simulation. Then, we apply these methods to a U.S. income analysis. The code for ScaGWR is available in the R package scgwr. The code is embedded into C++ code and implemented in another R package, GWmodel.
Daisuke Murakami; Narumasa Tsutsumida; Takahiro Yoshida; Tomoki Nakaya; Binbin Lu. Scalable GWR: A Linear-Time Algorithm for Large-Scale Geographically Weighted Regression with Polynomial Kernels. Annals of the American Association of Geographers 2020, 111, 459 -480.
AMA StyleDaisuke Murakami, Narumasa Tsutsumida, Takahiro Yoshida, Tomoki Nakaya, Binbin Lu. Scalable GWR: A Linear-Time Algorithm for Large-Scale Geographically Weighted Regression with Polynomial Kernels. Annals of the American Association of Geographers. 2020; 111 (2):459-480.
Chicago/Turabian StyleDaisuke Murakami; Narumasa Tsutsumida; Takahiro Yoshida; Tomoki Nakaya; Binbin Lu. 2020. "Scalable GWR: A Linear-Time Algorithm for Large-Scale Geographically Weighted Regression with Polynomial Kernels." Annals of the American Association of Geographers 111, no. 2: 459-480.
Real-time heatwave risk management with fine-grained spatial resolution is important for analysis of urban heat island (UHI) effects and local heatwaves. This study analyzed the spatio-temporal behavior of ground temperatures and developed methods for modeling them. The developed models consider two higher-order stochastic spatial properties (skewness and kurtosis), which are key to understanding and describing local temperature fluctuations and UHI effects. Application of the developed models to the greater Tokyo metropolitan area demonstrated the feasibility of statistically incorporating a variety of real datasets. Remotely sensed imagery and data from a variety of ground-based monitoring sites were used to build models linking urban covariates to air temperature. Air temperature models were used to capture high-resolution spatial emulator outputs for modeling ground surface temperatures. The main processes studied were the Tukey g-and-h processes for capturing spatial and temporal aspects of heat processes in urban environments. The main finding is that consideration of not only the mean temperature but also the variance, skewness, and kurtosis parameters can reveal hidden heatwave structures.
Daisuke Murakami; Gareth W. Peters; Tomoko Matsui; Yoshiki Yamagata. Spatio-Temporal Analysis of Urban Heatwaves Using Tukey g-and-h Random Field Models. IEEE Access 2020, 9, 79869 -79888.
AMA StyleDaisuke Murakami, Gareth W. Peters, Tomoko Matsui, Yoshiki Yamagata. Spatio-Temporal Analysis of Urban Heatwaves Using Tukey g-and-h Random Field Models. IEEE Access. 2020; 9 (99):79869-79888.
Chicago/Turabian StyleDaisuke Murakami; Gareth W. Peters; Tomoko Matsui; Yoshiki Yamagata. 2020. "Spatio-Temporal Analysis of Urban Heatwaves Using Tukey g-and-h Random Field Models." IEEE Access 9, no. 99: 79869-79888.
The construction of high-speed rail in China was initially a direct response to the increasing demand of up-to-date infrastructure. It is commonly understood that the construction of HSR has significant wider economic impact on local development. The benefits of HSR are represented by the accessibility to the HSR stations. Our study defines accessibility to HSR with a simple distance measure and a transportation network measure that considers travel from the center of the county through different grades of roads to the nearest HSR stations. For better understanding, we estimate both global and local (i.e., location-specific) impacts from HSR, using per capital GDP as a representation of the wider economic impact. With access to a panel dataset from 2008 to 2015 of regional socioeconomic indicators at the county-level units in China, the current study employs an eigenvector based spatial filtering (ESF) approach with and without spatially varying coefficients in an attempt to establish potential global and local relationships between HSR accessibility and county-level regional development. The analysis result suggests that it is likely that HSR accessibility might significantly contribute to regional development. A 10% decrease of the travel time to the nearest HSR station could bring about 0.44% (locally ranging from 0.28% to 3.1%) increase in local GDP per capita at the county level, ceteris paribus. The panel analysis suggests that the continued development of HSR construction in China will have long-term and sustainable support to local economic development. This is especially important to the relatively underdeveloped regions in the North and West China.
Danlin Yu; Daisuke Murakami; Yaojun Zhang; Xiwei Wu; Ding Li; Xiaoxi Wang; Guangdong Li. Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis. Transportation Research Part B: Methodological 2019, 133, 21 -37.
AMA StyleDanlin Yu, Daisuke Murakami, Yaojun Zhang, Xiwei Wu, Ding Li, Xiaoxi Wang, Guangdong Li. Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis. Transportation Research Part B: Methodological. 2019; 133 ():21-37.
Chicago/Turabian StyleDanlin Yu; Daisuke Murakami; Yaojun Zhang; Xiwei Wu; Ding Li; Xiaoxi Wang; Guangdong Li. 2019. "Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis." Transportation Research Part B: Methodological 133, no. : 21-37.
This study develops a spatial additive mixed modeling (AMM) approach estimating spatial and non-spatial effects from large samples, such as millions of observations. Although fast AMM approaches are already well established, they are restrictive in that they assume a known spatial dependence structure. To overcome this limitation, this study develops a fast AMM with the estimation of spatial structure in residuals and regression coefficients together with non-spatial effects. We rely on a Moran coefficient-based approach to estimate the spatial structure. The proposed approach pre-compresses large matrices whose size grows with respect to the sample size N before the model estimation; thus, the computational complexity for the estimation is independent of the sample size. Furthermore, the pre-compression is done through a block-wise procedure that makes the memory consumption independent of N. Eventually, the spatial AMM is memory free and fast even for millions of observations. The developed approach is compared to alternatives through Monte Carlo simulation experiments. The result confirms the estimation accuracy of the spatially varying coefficients and group coefficients, and computational efficiency of the developed approach. Finally, we apply our approach to an income analysis using United States (US) data in 2015.
Daisuke Murakami; Daniel A. Griffith. A memory-free spatial additive mixed modeling for big spatial data. Japanese Journal of Statistics and Data Science 2019, 3, 215 -241.
AMA StyleDaisuke Murakami, Daniel A. Griffith. A memory-free spatial additive mixed modeling for big spatial data. Japanese Journal of Statistics and Data Science. 2019; 3 (1):215-241.
Chicago/Turabian StyleDaisuke Murakami; Daniel A. Griffith. 2019. "A memory-free spatial additive mixed modeling for big spatial data." Japanese Journal of Statistics and Data Science 3, no. 1: 215-241.
Clarifying characteristics of hazards and risks of climate change at 2ºC and 1.5ºC global warming is important for understanding the implications of the Paris Agreement. We perform and analyse large ensembles of 2ºC and 1.5ºC warming simulations. In the 2ºC runs, we find substantial increases in extreme hot days, heavy rainfalls, high streamflow and labor capacity reduction related to heat stress. For example, about half of the world's population is projected to experience a present day 1-in-10 year hot day event every other year at 2ºC warming. The regions with large increases of these four hazard indicators coincide with countries characterized by small CO2 emissions, low-income and high vulnerability. Limiting global warming to 1.5ºC, compared to 2ºC, is projected to lower increases in the four hazard indicators especially in those regions.
Hideo Shiogama; Tomoko Hasegawa; Shinichiro Fujimori; Daisuke Murakami; Kiyoshi Takahashi; Katsumasa Tanaka; Seita Emori; Izumi Kubota; Manabu Abe; Yukiko Imada; Masahiro Watanabe; Daniel Mitchell; Nathalie Schaller; Jana Sillmann; Erich M Fischer; John F Scinocca; Ingo Bethke; Ludwig Lierhammer; Jun'ya Takakura; Tim Trautmann; Petra Doell; Sebastian Ostberg; Hannes Müller Schmied; Fahad Saeed; Carl-Friedrich Schleussner. Limiting global warming to 1.5 °C will lower increases in inequalities of four hazard indicators of climate change. Environmental Research Letters 2019, 14, 124022 .
AMA StyleHideo Shiogama, Tomoko Hasegawa, Shinichiro Fujimori, Daisuke Murakami, Kiyoshi Takahashi, Katsumasa Tanaka, Seita Emori, Izumi Kubota, Manabu Abe, Yukiko Imada, Masahiro Watanabe, Daniel Mitchell, Nathalie Schaller, Jana Sillmann, Erich M Fischer, John F Scinocca, Ingo Bethke, Ludwig Lierhammer, Jun'ya Takakura, Tim Trautmann, Petra Doell, Sebastian Ostberg, Hannes Müller Schmied, Fahad Saeed, Carl-Friedrich Schleussner. Limiting global warming to 1.5 °C will lower increases in inequalities of four hazard indicators of climate change. Environmental Research Letters. 2019; 14 (12):124022.
Chicago/Turabian StyleHideo Shiogama; Tomoko Hasegawa; Shinichiro Fujimori; Daisuke Murakami; Kiyoshi Takahashi; Katsumasa Tanaka; Seita Emori; Izumi Kubota; Manabu Abe; Yukiko Imada; Masahiro Watanabe; Daniel Mitchell; Nathalie Schaller; Jana Sillmann; Erich M Fischer; John F Scinocca; Ingo Bethke; Ludwig Lierhammer; Jun'ya Takakura; Tim Trautmann; Petra Doell; Sebastian Ostberg; Hannes Müller Schmied; Fahad Saeed; Carl-Friedrich Schleussner. 2019. "Limiting global warming to 1.5 °C will lower increases in inequalities of four hazard indicators of climate change." Environmental Research Letters 14, no. 12: 124022.
This study downscales the population and gross domestic product (GDP) scenarios given under Shared Socioeconomic Pathways (SSPs) into 0.5-degree grids. Our downscale approach has the following features. (i) It explicitly considers spatial and socioeconomic interactions among cities, (ii) it utilizes auxiliary variables, including road network and land cover, (iii) it endogenously estimates the influence from each factor by a model ensemble approach, and (iv) it allows us to control urban shrinkage/dispersion depending on SSPs. It is confirmed that our downscaling results are consistent with scenario assumptions (e.g., concentration in SSP1 and dispersion in SSP3). Besides, while existing grid-level scenarios tend to have overly-smoothed population distributions in nonurban areas, ours does not suffer from the problem, and captures the difference in urban and nonurban areas in a more reasonable manner. Our gridded dataset, including population counts and gross productivities by 0.5 degree grids by 10 years, are available from http://www.cger.nies.go.jp/gcp/population-and-gdp.html.
Daisuke Murakami; Yoshiki Yamagata. Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling. Sustainability 2019, 11, 2106 .
AMA StyleDaisuke Murakami, Yoshiki Yamagata. Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling. Sustainability. 2019; 11 (7):2106.
Chicago/Turabian StyleDaisuke Murakami; Yoshiki Yamagata. 2019. "Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling." Sustainability 11, no. 7: 2106.
While spatially varying coefficient (SVC) modeling is popular in applied science, its computational burden is substantial. This is especially true if a multiscale property of SVC is considered. Given this background, this study develops a Moran’s eigenvector-based spatially varying coefficients (M-SVC) modeling approach that estimates multiscale SVCs computationally efficiently. This estimation is accelerated through a (i) rank reduction, (ii) pre-compression, and (iii) sequential likelihood maximization. Steps (i) and (ii) eliminate the sample size N from the likelihood function; after these steps, the likelihood maximization cost is independent of N. Step (iii) further accelerates the likelihood maximization so that multiscale SVCs can be estimated even if the number of SVCs, K, is large. The M-SVC approach is compared with geographically weighted regression (GWR) through Monte Carlo simulation experiments. These simulation results show that our approach is far faster than GWR when N is large, despite numerically estimating 2K parameters while GWR numerically estimates only 1 parameter. Then, the proposed approach is applied to a land price analysis as an illustration. The developed SVC estimation approach is implemented in the R package “spmoran.”
Daisuke Murakami; Daniel A. Griffith. Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions. Spatial Statistics 2019, 30, 39 -64.
AMA StyleDaisuke Murakami, Daniel A. Griffith. Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions. Spatial Statistics. 2019; 30 ():39-64.
Chicago/Turabian StyleDaisuke Murakami; Daniel A. Griffith. 2019. "Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions." Spatial Statistics 30, no. : 39-64.
This study develops an approach for optimizing the size/scale of microgrids used in electricity sharing around each residence by considering the uncertainty between the electricity supply from photovoltaics and electricity demand. Uncertainties are quantified using simulations that consider actual daily variations in supply and demand. The developed approach is applied to microgrid optimization in Sumida-ku, Tokyo, Japan, and results suggests that large-scale microgrids are required in central districts but small microgrids are sufficient in old residential areas. Results also show the statistically significant effect of optimizing microgrids to enhance energy self-sufficiency.
Daisuke Murakami; Yoshiki Yamagata; Takahiro Yoshida; Tomoko Matsui. Optimization of local microgrid model for energy sharing considering daily variations in supply and demand. Energy Procedia 2019, 158, 4109 -4114.
AMA StyleDaisuke Murakami, Yoshiki Yamagata, Takahiro Yoshida, Tomoko Matsui. Optimization of local microgrid model for energy sharing considering daily variations in supply and demand. Energy Procedia. 2019; 158 ():4109-4114.
Chicago/Turabian StyleDaisuke Murakami; Yoshiki Yamagata; Takahiro Yoshida; Tomoko Matsui. 2019. "Optimization of local microgrid model for energy sharing considering daily variations in supply and demand." Energy Procedia 158, no. : 4109-4114.
The objective of this study is to map direct and indirect seasonal urban carbon emissions using spatial micro Big Data, regarding building and transportation energy-use activities in Sumida, Tokyo. Building emissions were estimated by considering the number of stories, composition of use (e.g., residence and retail), and other factors associated with individual buildings. Transportation emissions were estimated through dynamic transportation behaviour modelling, which was obtained using person-trip surveys. Spatial seasonal emissions were evaluated and visualized using three-dimensional (3D) mapping. The results suggest the usefulness of spatial micro Big Data for seasonal urban carbon emission mapping; a process which combines both the building and transportation sectors for the first time with 3D mapping, to detect emission hot spots and to support community-level carbon management in the future.
Yoshiki Yamagata; Takahiro Yoshida; Daisuke Murakami; Tomoko Matsui; Yuki Akiyama. Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data. Sustainability 2018, 10, 4472 .
AMA StyleYoshiki Yamagata, Takahiro Yoshida, Daisuke Murakami, Tomoko Matsui, Yuki Akiyama. Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data. Sustainability. 2018; 10 (12):4472.
Chicago/Turabian StyleYoshiki Yamagata; Takahiro Yoshida; Daisuke Murakami; Tomoko Matsui; Yuki Akiyama. 2018. "Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data." Sustainability 10, no. 12: 4472.