Papers - Yoshiki YAMAGATA

Systematizing and upscaling urban climate change mitigation

Creutzig F, Bai X, Khosla R, Viguie V, Yamagata Y

Environmental Research Letters (Environmental Research Letters)  15 ( 10 )  2020.10

Joint Work,  ISSN  17489318

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The question of what cities can contribute to mitigation and adapting to climate change is gaining traction among researchers and policy makers alike. However, while the field is rich with case studies, methods that provide rich data across municipalities and potentially at global scale remain underdeveloped, and comparative insights remain scarce. Here we summarize contributions to the focus issue on 'Systematizing and Upscaling Urban Climate Solutions', also drawing from presentations given at an accompanying conference in 2018. We highlight four core areas for systematizing and upscaling urban climate mitigation solutions. First, with more and better (big) data and associated machine learning methods, there is increasing potential to compare types of cities and leverage collective understanding. Second, while urban climate assessments have mostly emphasized urban planning, demand-side action as related to both behavioral change and modified social practices relevant to urban space deserve more academic attention and integration across a diverse set of social sciences. Third, climate mitigation would be intangible as a single objective at the urban scale, and measures and solutions that coordinate mitigation coherently with adaptation and broader sustainable development goals require explicit conceptualization and systematization. Forth, all insights should come together to develop governance frameworks that translate scientific exercises into concrete, realistic and organized action plans on the ground, for all cities.

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Rethinking sustainable bioenergy development in Japan: decentralised system supported by local forestry biomass

Goh C.S, Aikawa T, Ahl A, Ito K, Kayo C, Kikuchi Y, Takahashi Y, Furubayashi T, Nakata T, Kanematsu Y, Saito O, Yamagata Y

Sustainability Science (Sustainability Science)  15 ( 5 ) 1461 - 1471 2020.09

Research paper (scientific journal), Joint Work,  ISSN  18624065

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Bioenergy has been promoted in Japan with ambitious targets. However, the incentive schemes excluded renewable heat and overlooked synergies with local forest management, leading to the development of large-scale biomass plants that heavily rely on overseas biomass supplies. This case report discussed an alternative scenario of decentralised bioenergy systems supported with local biomass through five important questions. The currently available knowledge indicates that such a scenario is feasible with integrative forest management that considers both ecosystem services and multiple uses of wood. In addition to various environmental benefits, replacing imported fossil fuels with local biomass can also enhance energy security. Realising this scenario requires careful consideration of local context, empowerment of local governments and encouragement of both public and private initiatives.

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Urban land cover mapping under the Local Climate Zone scheme using Sentinel-2 and PALSAR-2 data

La Y., Bagan H., Yamagata Y.

Urban Climate (Urban Climate)  33 2020.09

ISSN  22120955

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The spatial and spectral heterogeneity of urban areas makes land cover classification a challenging process. In this study, we highlight the potential of combined multi-spectral Sentinel-2 and fully polarimetric PALSAR-2 data for land cover classification in dense urban areas, based on the Local Climate Zone (LCZ) scheme. We classified differently combined spectral and back-scattering characteristics using the subspace method in comparison with the Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) methods. Results show that, (i) the overall accuracy (OA) was 65.9% for the Sentinel-2 data, (ii) higher OA (71.9%) was achieved by adding four intensity images of PALSAR-2 to Sentinel-2, (iii) the inclusion of decomposed components increased OA to 72.8%, and (iv) the highest OA (73.3%) was achieved using all features. These results suggest that the inclusion of different backscattering characteristics disproportionately improved classification accuracy from using multi-spectral data alone. The results of comparison between different methods show that the subspace method performed better than SVM and MLC, particularly when high-dimensional data were used. The subspace method classified particularly well for some specific LCZ classes which are easily mixed between each other. It provides a promising option for LCZ mapping.

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On the Potential of 'PV + EV' for Deep Decarbonization of Kyoto’s Power Systems: Techno-Economic-Social Considerations

Kobashi, Takuro, Takahiro Yoshida, Yoshiki Yamagata, Katsuhiko Naito, Stefan Pfenninger, Kelvin Say, Yasuhiro Takeda, Amanda Ahl, Masaru Yarime, Keishiro Hara

Applied Energy Forthcoming 2020.06

Research paper (scientific journal), Joint Work, Accepted

Techno-economic assessment of photovoltaics plus electric vehicles towards household-sector decarbonization in Kyoto and Shenzhen by the year 2030

Kobashi T., Say K., Wang J., Yarime M., Wang D., Yoshida T., Yamagata Y.

Journal of Cleaner Production (Journal of Cleaner Production)  253 2020.04

ISSN  09596526

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Finding economical and sustainable pathways for the deployment of renewables is critical for the success of decarbonizing energy systems. Because of the variable nature of renewable energy, however, the integration of renewables becomes increasingly costly and difficult as the renewable penetration becomes higher. The recent rise of electric vehicles (EVs) provides us with an opportunity to increase self-consumption of solar photovoltaic (PV) at households with substantially lower additional costs. In this paper, an environmental techno-economic assessment of residential PV systems is conducted with battery or EVs allowing charging and discharging (Vehicle to Home: V2H) in Kyoto, Japan, and Shenzhen, China, incorporating the cost projections of these technologies towards 2030. It is found that “PV + EV” (V2H) becomes highly cost-effective towards 2030 in Kyoto in comparison with “PV only”, “PV + battery”, and “EV charge”. In the region of lower electricity costs such as Shenzhen, switching to EVs from gasoline vehicles is the most cost-effective option during 2020s. Finally, it is found that “PV + EV” has the highest CO emission reduction potential across all the technology combinations considered, owing to EV charging of carbon-free electricity from PV. To facilitate a rapid household decarbonization, policy makers should reinforce policies to enhance the penetration of combined technologies of “PV + EV” (V2H) towards 2030. 2

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Energy sharing boundaries integrating buildings and vehicles tangled in spatial and temporal changes

Chang S, Yoshida T, Binder R.B, Yamagata Y, Castro-Lacouture D

Construction Research Congress 2020: Infrastructure Systems and Sustainability - Selected Papers from the Construction Research Congress 2020 (Construction Research Congress 2020: Infrastructure Systems and Sustainability - Selected Papers from the Construction Research Congress 2020)     434 - 443 2020

Research paper (international conference proceedings), Joint Work,  ISSN  9780784482858

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Technical advancements such as building-integrated renewable systems and autonomous vehicles (AVs) require existing electricity grids to be transformed into adaptable microgrids considering future energy supply and demand. Interactions between energy demanders and suppliers should be predicted to quickly respond and adapt to future demands or uncertain events. However, communities consisting of buildings, transports, and people are complex and their interactions are difficult to be recognized. This research investigates the interactions among elements in communities featuring AVs and solar photovoltaics (PV)-integrated buildings. A framework for establishing smart energy networks that can be adjustable spatially and temporally is developed by communicating buildings and vehicles. The methodology is tested in buildings and road networks in Kyojima 1, North Sumida, Tokyo, Japan. Existing energy demands and transportation systems are reviewed to establish future scenarios for applying AVs and solar PV buildings. The scenarios are analyzed using community clustering and Voronoi diagram algorithms, in which sustainable community boundaries are examined using prediction data from parametric energy modeling and spatial network analysis. The framework can support decision-making for future community boundaries, thereby distributing energy securely and efficiently. This research will also contribute to preparing communities to share energy information and to achieve resilient and sustainable infrastructure systems.

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Spatial modeling and design of smart communities

Takahiro Yoshida, Yoshiki Yamagata, Soowon Chang, Vincent de Gooyert, Hajime Seya, Daisuke Murakami, Peraphan Jittrapirom, Gerasimos Voulgaris

Urban Systems Design (Elsevier)     199 - 255 2020

Joint Work, Accepted

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Modeling and design of smart buildings

Soowon Chang, Perry P.J. Yang, Yoshiki Yamagata, Michael B. Tobey

Urban Systems Design (Elsevier)     59 - 86 2020

Joint Work, Accepted

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Spatial scenario creation based on downscale methods

Murakami D., Yamagata Y.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     259 - 270 2019.11

ISSN  9780128131275

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This chapter introduces a spatial econometric model application to a downscaling of global socioeconometric scenarios. Section 10.1 provides a brief review of relevant literature. Section 10.2 describes our downscale approach and Section 10.3 employs it to estimate populations and GDPs by 0.5-degree grids. Finally, Section 10.4 concludes our discussion.

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Geostatistics and Gaussian process models

Murakami D., Yamagata Y., Hirano T.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     57 - 112 2019.11

ISSN  9780128131275

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In this chapter, Section 4.1 briefly introduces the history of geostatistics. Section 4.2 explains stationary spatial processes and the basic geostatistical model to describe the process; Section 4.3 explains how to estimate the model parameters. After introducing Kriging, which is the spatial interpolation approach, in Section 4.4, Section 4.5 introduces its extensions including linear and non-linear kriging approaches. Then, Sections 4.6, 4.7, and 4.8, respectively, explain semiparametric, Bayesian, and spatiotemporal extensions of the geostatistical approach. Finally, Section 4.9 explains geostatistical approaches for large samples.

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Evaluating walkability using mobile GPS data

Yamagata Y., Murakami D., Yoshida T.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     239 - 257 2019.11

ISSN  9780128131275

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This chapter illustrates the results of applications of geographically weighted regression modeling (Chapter 6) for assessing goodness of walking environment, that is, walkability. Section 9.1 briefly introduces some representative measures of walkability. Section 9.2 explains about using data and walkability measures. The settings for geographically weighted regression modeling are also explained here. Sections 9.3 and 9.4 show the estimation results. The former focuses on the impact of pedestrian network structure on the number of pedestrians, whereas the latter focuses on heatwave risk for pedestrians at an intra-urban scale. Finally, Section 9.5 concludes this chapter with a discussion.

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Quasi real-time energy use estimation using Google's Popular Times data

Yoshida T., Yamagata Y.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     271 - 280 2019.11

ISSN  9780128131275

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This chapter illustrates a geostatistic model (Chapter 4) application to estimate quasi real-time energy consumption in each building using Google's Popular Times data. After the introduction in Section 11.1 where our research motivations are explained, Section 11.2 describes the empirical methods, and we adopt compositional kriging approach for this analysis. Section 11.3 shows the estimation results of estimating building-level energy consumption. Effective visualization is attempted with the results. Section 11.4 concludes this chapter with a discussion.

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Spatial econometric models

Seya H., Yoshida T., Yamagata Y.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     113 - 158 2019.11

ISSN  9780128131275

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Section 5.1 of this chapter provides a brief review of spatial econometrics. Section 5.2 describes various types of spatial econometric models. Impact measures are also explained here. Section 5.3 explains representative parameter estimation methods for spatial econometric models. Sections 5.4 and 5.5 introduce likelihood-based tests for spatial autocorrelation and spatial heterogeneity, respectively. Section 5.6 explains the remaining important models that were not explained in Section 5.2. Finally, Section 5.7 explores the methods that seem useful when we apply spatial econometric models to a large dataset.

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Spatial heat-wave assessments using geotagged Twitter data

Murakami D., Yamagata Y.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     227 - 238 2019.11

ISSN  9780128131275

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This chapter introduces a twitter data application to a heatwave analysis. Section 8.1 introduces our discussion emphasizing on statistical modeling combining different types of spatial data. Section 8.2 introduces the twitter data and their processing. Section 8.3 explains our analysis method, and Section 8.4 applies this method to a heatwave analysis. Section 8.5 concludes our discussion.

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Mathematical preparation

Seya H., Yamagata Y.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     9 - 31 2019.11

ISSN  9780128131275

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Section 2.1 of this chapter defines the typical notations. Section 2.2 explains the classical linear regression model with typical assumptions and discusses what may happen when these assumptions are violated. Estimation techniques such as ordinary least squares, two-stage least squares, and generalized method of moments are briefly explained. Sections 2.3 and 2.4 introduce two advanced regression models, the generalized linear model and the additive model. Section 2.5 explains the basics of Bayesian statistics and the Markov chain Monte Carlo in terms of a regression model.

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Models in quantitative geography

Murakami D., Yamagata Y.

Spatial Analysis Using Big Data: Methods and Urban Applications (Spatial Analysis Using Big Data: Methods and Urban Applications)     159 - 178 2019.11

ISSN  9780128131275

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This chapter introduces approaches in quantitative geography. After the introduction in Section 6.1, Sections 6.2 and 6.3 explain the geographically weighted regression and the spatial filtering approaches, respectively. Section 6.4 describes extensions of these approaches for large spatial dataset.

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Typologies of Rapid Urbanization in Developing Asian Countries: A Study of Shanghai's Rapid Urbanization and Subsequent Strategies

Boynton Tobey M, Chang S, Brent Binder R, Yamagata Y

IOP Conference Series: Earth and Environmental Science (IOP Conference Series: Earth and Environmental Science)  294 ( 1 )  2019.08

Joint Work,  ISSN  17551307

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Over the last several decades developing countries, especially in Asia, have undergone large scale rapid urbanization, impacting their developmental patterns. Compounded with increased economic prosperity and social mobility attracting individuals and companies, to urban centres, has led to severe administrative challenges that can negatively affect the citizens, nation, and environment. In efforts to mitigate those adverse side effects cities have instituted, or citizens naturally adopted, a variety of strategies to address those issues. These strategies result in the typologies of rapid urbanization that are most prevalent in developing Asian cities. By focusing on Asian and Chinese cities, using shanghai as a test case, this research proposes several major typological categories associated with rapidly urbanizing areas. Shanghai's rapid urbanization has been shaped by seven main typologies of urban growth and expansion, since the implementation of China's economic growth policies and its resurgence as an influential global city. Spatial typological patterns of urban growth in Shanghai are analyzed to explain how the economic prosperity and social mobility have been managed. Typological adaptations learned from the historical urban expansion provide the subsequent strategies for future sustainable development in a rapidly urbanizing area.

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Assessing nighttime lights for mapping the urban areas of 50 cities across the globe

Bagan H., Borjigin H., Yamagata Y.

Environment and Planning B: Urban Analytics and City Science (Environment and Planning B: Urban Analytics and City Science)  46 ( 6 ) 1097 - 1114 2019.07

ISSN  23998083

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Nighttime data from the Defense Meteorological Satellite Program Operational Linescan System have been widely used to map urban/built-up areas (hereafter referred to as “built-up area”), but to date there has not been a geographically comprehensive evaluation of the effectiveness of using nighttime lights data to map urban areas. We created accurate, convenient, and scalable grid cells based on Defense Meteorological Satellite Program/Operational Linescan System nighttime light pixels. We then calculated the density of Landsat-derived built-up areas within each grid cell. We explored the relationship between Defense Meteorological Satellite Program/Operational Linescan System nighttime lights data and the density of built-up areas to assess the utility of nighttime lights for mapping urban areas in 50 cities across the globe. We found that the brightness of nighttime lights was only in moderate agreement with the density of built-up areas; moreover, correlations between nighttime lights and Landsat-derived built-up areas were weak. Even in relatively sparsely populated urban regions (where the density of the built-up area is less than 20%), the highest correlation coefficient (R ) was only 0.4. Furthermore, nighttime lights showed lighted areas that extended beyond the area of large cities, and nighttime lights reduced the area of small cities. The results suggest that it is difficult to use the regression model to calibrate the Defense Meteorological Satellite Program/Operational Linescan System nighttime lights to fit urban built up areas. 2

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Verification on Evacuation of Flood Disaster by Using GPS: Case Study in Mabi, Japan 2018

Yoshida T., Hiroi K., Yamagata Y., Murakami D.

International Geoscience and Remote Sensing Symposium (IGARSS) (International Geoscience and Remote Sensing Symposium (IGARSS))     5633 - 5635 2019.07

ISSN  9781538691540

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This study attempts to simulate people flow before and after the flooding in the West Japan heavy rain on July 2018, using a mobile GPS (global positioning system) data. We focus on Mabi-Cho, Kurashiki-City, which is one of the most damaged area on the flood disaster. A Monte Carlo simulation approach is introduced to generate micro-scale people's evacuation flows from spatially sparse GPS information. The result shows that GPS data effectively simulate people flow after flooding even if the simulation approach is very simple, and how people flow changed before and after the disaster.

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Spatiotemporal Heatwave Risk Evaluation: Considering Hazard, Exposure, and Vulnerability

Yamagata Y., Murakami D., Yoshida T.

International Geoscience and Remote Sensing Symposium (IGARSS) (International Geoscience and Remote Sensing Symposium (IGARSS))     5524 - 5527 2019.07

ISSN  9781538691540

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Urban heatwave is increasingly severe as the global warming advances. Even worse, especially in Japan, an increasing proportion of residences are vulnerable against heats under the aging society. Today, heatwave monitoring is an emergent task toward climate adaptive urban development. Our final goal is developing a smart navigation system to monitor real-time and micro-scale heatwave risk in Tokyo. To achieve it, we performed the following observation experiments: collection people location data with age information and people sentiment data by twitter, ground surface temperature monitoring by airborne and from the Tokyo Sky Tree. A method to combine these multi-scale information is developed to estimate micro-scale spatiotemporal behavior on heatwave risks. Based on the result, it is discussed how we can achieve the real-time and micro-scale heatwave monitoring, and make Tokyo more heat risk mitigative.

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