Daisuke Murakami
Biographic Data
| ID | 1340273 |
|---|---|
| NAME | Daisuke Murakami |
| GIVEN NAMES | Daisuke |
| FAMILY NAME | Murakami |
| SIGNATURE | MURAKAMI D |
| AFFILIATIONS | The Institute of Statistical Mathematics |
| ORCID | 0000-0002-9544-9542 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 18 |
| WORKS AS AUTHOR | 15 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Coarse‐to‐Fine Spatial Modeling
This study proposes coarse‐to‐fine spatial modeling (CFSM) as a scalable and machine learning‐compatible alternative to conventional spatial process models. Unlike conventional covariance‐based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger‐scale patterns that are easier to learn are modeled first, followed by smaller‐scale patterns, with training terminated o…
Fast Spatio‐Temporally Varying Coefficient Modeling With Reluctant Interaction Selection
Spatially and temporally varying coefficient (STVC) models are attracting attention as a flexible tool to explore the spatio‐temporal patterns in regression coefficients. However, these models often struggle with balancing the computational efficiency, flexibility, and interpretability of the coefficients. This study develops a fast and flexible STVC model to address this challenge. To enhance flexibility and interpretability, we assume multiple …
Considerations for Creating a User-Friendly Telemedicine Service in Japan
Telemedicine aims to slow the spread of infection, enhance the quality and cost of healthcare, and increase patient engagement and satisfaction. Recently, the Japanese government has promoted the use of telemedicine. Despite the efforts of the government, the service is not in widespread use in Japan, even in the era of the COVID-19 pandemic. The present study aims to assess the awareness of telemedicine among Japanese older adults and learn abou…
Encapsulating Spatially Varying Relationships with a Generalized Additive Model
This paper describes the use of Generalized Additive Models (GAMs) to create regression models whose coefficient estimates vary with geographic location—spatially varying coefficient (SVC) models. The approach uses Gaussian Process (GP) splines (smooths) for each predictor variable, which are parameterised with observation location in order to generate SVC estimates. These describe the spatially varying relationships between predictor and respons…
Sub‐Model Aggregation for Scalable Eigenvector Spatial Filtering
This study proposes a method for aggregating/synthesizing global and local sub‐models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub‐model, while the generalized product‐of‐experts method was used to aggregate these sub‐models. The major advantages of the proposed method are as follows: (i) it is highly scalable …
A Route Map for Successful Applications of Geographically Weighted Regression
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, se…
Balancing Spatial and Non‐Spatial Variation in Varying Coefficient Modeling
This study discusses the importance of balancing spatial and non‐spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non‐spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non‐spatial variation is needed not only to improve model accuracy but also to reduce spuriou…
Tokyo's Covid-19
This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID…
Scalable GWR
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 t…
Scalable Model Selection for Spatial Additive Mixed Modeling
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 typ…
Limiting global warming to 1.5 °C will lower increases in inequalities of four hazard indicators of climate change
Clarifying characteristics of hazards and risks of climate change at 2 °C and 1.5 °C global warming is important for understanding the implications of the Paris Agreement. We perform and analyze large ensembles of 2 °C and 1.5 °C warming simulations. In the 2 °C 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 popul…
The Importance of Scale in Spatially Varying Coefficient Modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the “spatial scale” of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of …
Estimating water–food–ecosystem trade-offs for the global negative emission scenario (IPCC-RCP2.6)
Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (Isimip2b)
In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe th…
Japanese Imaginings of Tibet
This article attempts to demonstrate and analyse Japanese images and fantasies that have been projected onto Tibet both in Japan's colonial and contemporary eras. The author focuses particularly on the latter period, investigating literature and social vocabularies demonstrated by important Japanese monks, scholars and travellers who disseminated conflicting and distinctive images of Tibet. In so doing, he argues that Japanese imaginings of Tibet…
Scalable GWR
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 t…
The Importance of Scale in Spatially Varying Coefficient Modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the “spatial scale” of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of …
Estimating water–food–ecosystem trade-offs for the global negative emission scenario (IPCC-RCP2.6)
Japanese Imaginings of Tibet
This article attempts to demonstrate and analyse Japanese images and fantasies that have been projected onto Tibet both in Japan's colonial and contemporary eras. The author focuses particularly on the latter period, investigating literature and social vocabularies demonstrated by important Japanese monks, scholars and travellers who disseminated conflicting and distinctive images of Tibet. In so doing, he argues that Japanese imaginings of Tibet…
Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (Isimip2b)
In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe th…
Estimating water–food–ecosystem trade-offs for the global negative emission scenario (IPCC-RCP2.6)
Limiting global warming to 1.5 °C will lower increases in inequalities of four hazard indicators of climate change
Clarifying characteristics of hazards and risks of climate change at 2 °C and 1.5 °C global warming is important for understanding the implications of the Paris Agreement. We perform and analyze large ensembles of 2 °C and 1.5 °C warming simulations. In the 2 °C 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 popul…
The Importance of Scale in Spatially Varying Coefficient Modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the “spatial scale” of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of …
Scalable Model Selection for Spatial Additive Mixed Modeling
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 typ…
Scalable GWR
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 t…
A Route Map for Successful Applications of Geographically Weighted Regression
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, se…
Balancing Spatial and Non‐Spatial Variation in Varying Coefficient Modeling
This study discusses the importance of balancing spatial and non‐spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non‐spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non‐spatial variation is needed not only to improve model accuracy but also to reduce spuriou…
Tokyo's Covid-19
This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID…
Encapsulating Spatially Varying Relationships with a Generalized Additive Model
This paper describes the use of Generalized Additive Models (GAMs) to create regression models whose coefficient estimates vary with geographic location—spatially varying coefficient (SVC) models. The approach uses Gaussian Process (GP) splines (smooths) for each predictor variable, which are parameterised with observation location in order to generate SVC estimates. These describe the spatially varying relationships between predictor and respons…
Sub‐Model Aggregation for Scalable Eigenvector Spatial Filtering
This study proposes a method for aggregating/synthesizing global and local sub‐models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub‐model, while the generalized product‐of‐experts method was used to aggregate these sub‐models. The major advantages of the proposed method are as follows: (i) it is highly scalable …
Fast Spatio‐Temporally Varying Coefficient Modeling With Reluctant Interaction Selection
Spatially and temporally varying coefficient (STVC) models are attracting attention as a flexible tool to explore the spatio‐temporal patterns in regression coefficients. However, these models often struggle with balancing the computational efficiency, flexibility, and interpretability of the coefficients. This study develops a fast and flexible STVC model to address this challenge. To enhance flexibility and interpretability, we assume multiple …
Considerations for Creating a User-Friendly Telemedicine Service in Japan
Telemedicine aims to slow the spread of infection, enhance the quality and cost of healthcare, and increase patient engagement and satisfaction. Recently, the Japanese government has promoted the use of telemedicine. Despite the efforts of the government, the service is not in widespread use in Japan, even in the era of the COVID-19 pandemic. The present study aims to assess the awareness of telemedicine among Japanese older adults and learn abou…
Coarse‐to‐Fine Spatial Modeling
This study proposes coarse‐to‐fine spatial modeling (CFSM) as a scalable and machine learning‐compatible alternative to conventional spatial process models. Unlike conventional covariance‐based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger‐scale patterns that are easier to learn are modeled first, followed by smaller‐scale patterns, with training terminated o…
Spatial and Panel Data Analysis (9 works) · Statistics (9 works) · Computer Science (8 works) · Mathematics (8 works) · Geography (5 works) · Land Use and Ecosystem Services (5 works) · Economic and Environmental Valuation (4 works) · Environmental Science (4 works) · Monte Carlo method (4 works) · Regression analysis (4 works)