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京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第19873号 / 地環博第147号 / 新制||地環||29(附属図書館) / 32909 / 京都大学大学院地球環境学舎環境マネジメント専攻 / (主査)教授 岡﨑 健二, 准教授 小林 広英, 准教授 ｼﾞｪｰﾝ ｼﾝｶﾞｰ / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

During the last decade, the average mobile wireless data usage per person has tremendously increased. An even faster growth of the traffic demand is expected for the incoming years, due to several factors such as the increasing global population, the spread of the Internet of things (IoT), and the development of advanced technologies that require a higher amount of data. While mobile communication technologies have rapidly evolved to meet this need in the most usual situations, it is expected that the sixth generation (6G) of mobile connectivity will be the first one paying considerable attention to under-connected environments such as low-income, remote, or disaster-struck regions. Many specialized researchers and entrepreneurs are trying to design and implement alternative network architectures specifically meant for enhancing the performances of the current telecommunication (telecom) infrastructure. In particular, the use of aerial base stations (ABSs) has received considerable attention due to the main advantages of easy deployability and low-cost that are typical of unmanned aerial vehicles (UAVs), which are available in several fashions depending on the application; moreover, UAVs are also eligible to carry reflective intelligent surfaces (RISs), which represent a promising technology that allows to reflect signals towards specific directions. Another possibility that we have investigated consists in integrating the transceivers inside or atop existing rural wind turbine (WT) towers, in order to increase the coverage radius while avoiding the cost of building a separate telecom infrastructure. A powerful mathematical tool for evaluating the performance metrics of either terrestrial, aerial, or vertical heterogeneous wireless networks is stochastic geometry (SG), since it can be used to model the locations of the base stations (BSs) according to tractable spatial distributions (with either a fixed or a random cardinality) in order to imitate the typical deployments of the nodes made in realistic scenarios; in particular, in this work we focus on rural and post-disaster situations. SG makes use of point processes to model networks' topologies. The developed spatial models, in turn, allow us to analyze the quality of service (QoS) experienced by the typical user served by the proposed networks. To this extent, we creatively and efficiently studied our inhomogeneous systems by making use of what we call the indicator method, meaning that we do not subdivide the ground plane in multiple homogeneous sub-regions, but we use indicator functions to provide general expressions that are valid over the entire ground plane. To prove the effectiveness of the novel architectures, insightful comparisons with the conventional ones are presented.

Puerto Rico has been under influence and colonial rule by the United States since the Spanish-American War of 1898. This has led the island to have partial and limited control over the affairs inside it. The passing of Hurricane Maria on September 20th of 2017 exposed problems even further. Puerto Rico remains under the control of a Financial Oversight and Management Board since the passing of the PROMESA act (The Puerto Rico Oversight, Management, and Economic Stability Act) signed by President Barack Obama in 2016. This had forced Puerto Rico to make drastic cuts to its public services. One of the main services was has been its public university, The University of Puerto Rico. This study provides a critical analysis of the reality of college students staying in Puerto Rico and continuing their studies in the UPR. Ten interviews have been completed. These semi-structured qualitative interviews provided themes that can be studied to create and inspire further research and eventually influence policies that can better the quality of life of these students. The data points to mental health issues, limited opportunities in research and internships, post-hurricane experience, structural problems to the university (physical and bureaucratic), amongst others. There are also signs of resilience and community support. Analysis of the themes through the transcription and data coding have provided insight to steps that can be taken at UCF’s Puerto Rico Research Hub that can extend to Central Florida and the island itself.

This project deploys a modified Water Poverty Index (WPI) in villages reconstructed after the 2004 tsunami in southeastern India. While previous measurements of access to water have advanced understandings of waterscape complexities, this modified WPI improves past efforts and deconstructs some of the previous misunderstandings and notions regarding access to water. The traditional WPI is multidimensional and seeks to measure water access in a holistic fashion; the WPI presented here employs this approach, but is adapted to include new place-based indicators (e.g., Secondary Sources). Furthermore, unlike previous iterations of the WPI, our modified index incorporates water quality testing, three weight schemes, and operates at several scales. Ultimately, the construction and arrangement of our modified WPI enables statistical analyses, geospatial analyses, and water poverty mapping -- which are absent in most prior studies-- while still remaining easy to populate and descriptively analyze among non-academicians. Statistical tests of original household level data from a total of 24 villages in Nagapattinam District, Tamil Nadu, and Karaikal District, Puducherry, indicate significant differences between the two districts in indicator scores as well as total WPI score. Additionally, the urban and rural areas within each district were found to be significantly different in level of water poverty, and trends were similar across the three weight schemes. Multiple linear regressions show correlation of independent socioeconomic variables (i.e., Income, Education, and Assets-Networks) with the dependent indicator of Capacity, but not with the other indicators or total WPI score. Global Moran's I tests indicate positive spatial autocorrelation, demonstrating that indicator and WPI scores tend to cluster in space. Overall, the results match what was anticipated, yet serve to challenge commonly held assumptions on urban-rural hierarchies and the role of socioeconomic variables in determining water poverty. The construction, deployment, and analytical potential of this modified WPI can be used by scholars to improve existing conceptualizations and measurements of access to water, while the results can be used by local governments and nonprofits to improve resource allocation and inform spatially-targeted interventions. / Master of Science

<p dir="ltr">Increased frequency and intensity of disasters necessitate the dynamic post-disaster recovery process. Developing human mobility patterns, household return decision-making models, and agent-based simulations in disaster management has opened a new door towards more intricate and enduring recovery frameworks. Despite these opportunities, the importance of a unified framework is underestimated to identify the underlying mechanisms hindering the post-disaster recovery process. My research has been geared towards forging advancements in civil and disaster management, focusing on two main areas: (1) modeling the post-disaster recovery process and (2) identifying critical transitions within the recovery process.</p><p dir="ltr">My dissertation explores the collective and individual dynamics of post-disaster recovery across different spatial and temporal scales. I have identified the best recovery strategies for various contexts by constructing data-driven socio-physical multi-agent systems. Employing various advanced computational methodologies, including machine learning, system dynamics, causal discovery, econometrics, and network analysis, has been instrumental. I start with aggregated level analysis for post-disaster recovery. Initially, I examined the system dynamics model for the post-discovery recovery process in socio-physical systems, using normalized visit density of points of interest and power outage information. Through counterfactual analyses of budget allocation strategies, I discovered their significant impact on recovery trajectories, noting that specific budget allocations substantially enhance recovery patterns. I also revealed the urban-rural dissimilarity by the data-driven causal discovery approach. I utilized county-level normalized visit density of points of interest and nighttime light data to identify the relationship between counties. I found that urban and rural areas have similar but different recovery patterns across different types of points of interest.</p><p dir="ltr">Moving from aggregated to disaggregated level analysis on post-disaster recovery, I investigated household-level decision-making regarding disaster-induced evacuation and return behaviors. The model yielded insights into the varying influences of certain variables across urban and rural contexts. Subsequently, I developed a unified framework integrating aggregated and disaggregated level analyses through multilayer multi-agent systems to model significant shifts in the post-disaster recovery process. I evaluated various scenarios to pinpoint conditions for boosting recovery and assessing the effects of different intervention strategies on these transitions. Lastly, a comparison between mathematical models and graph convolutional networks was conducted to better understand the conditions leading to critical transitions in the recovery process. The insights and methodologies presented in this dissertation contribute to the broader understanding of the disaster recovery process in complex urban systems, advocating for a shift towards a unified framework over individual models. By harnessing big data and complex systems modeling, I can achieve a detailed quantitative analysis of the disaster recovery process, including critical transition conditions of the post-disaster recovery. This approach facilitates the evaluation of such recovery policies through inter-regional comparisons and the testing of various policy interventions in counterfactual scenarios.</p>

Professor N. Emel Ganapati, Major Professor Despite a growing body of research on gender and disaster, little is known regarding the long-term recovery priorities and participation of internally displaced women in the long-term recovery process. Focusing on this important scholarly gap in the public administration literature, the overall goal of this study is to understand the long-term recovery processes of populations displaced by the 2010 Haiti earthquake through a gendered lens. The study’s specific aims are to: (1) understand the rebuilding priorities of IDPs in Haiti through a gendered lens; (2) determine factors that enable or hinder IDP women’s participation in decision-making processes; and (3) assist policymakers, non-governmental organizations, and international aid agencies in addressing the priorities of women IDPs. The dissertation is based on a qualitative research study. Its data collection methods include semi-structured interviews (n=97), focus groups (n=63), participant observation, and a review of diverse secondary sources. Despite some similarities between the recovery priories of women and men IDPs in the short and long-term, women IDPs in Haiti had several additional priorities due to: (1) the traditional roles they play in the household; (2) their perception inside and outside the household as passive “victims” that needed help; and, (3) the location and conditions of IDP camps (e.g., increased risks of sexual assaults and violence) within which they lived. Their participation to voice their priorities were limited to participation in informal settings (e.g., camp committee meetings) in camps managed by the government or international aid agencies; and were affected by the following: (1) organizational factors (e.g., diverse range of organizations with diverse organizational cultures); (2) formal institutional factors (e.g., lack of participatory mechanisms customized for IDPs); (3) policymaker-related factors (e.g., stigma towards the IDPs); (4) IDP related factors (e.g., lack of trust); (5) place-related factors (e.g., lack of access to transportation); and (6) social capital-related factors (e.g., women’s groups). This study provides useful information to public administration scholars and policymakers who are working to support individuals living in the camps while those individuals rebuild their communities and livelihoods.

This research focuses upon cases wherein post]disaster housing assistance was affected by tenure insecurity. In the case of post]Katrina New Orleans, the Road Home, which provided monies for rebuilding, faced difficulties in allocating its aid because of heirship titles, a form of tenure insecurity to which the United States has often been misconceived as immune. In the case of post]Hurricane Mitch in Tegucigalpa, a post]disaster housing relocation program struggled to find lands in an urban land market with pervasive insecurity

For nearly 200 years, there has been a neighborhood in New Orleans, Louisiana named Milneburg, which has been constantly reimagined by its inhabitants and others. From its inception as a port of entry in 1832 until the 2011, it has been called a world-class resort, the poor-man's Riviera, a seedy red-light district, a cradle of jazz, a village, a swath of suburbia and a neighborhood. It has been destroyed eight times due to storms, fires, and civic or governmental neglect. Each time its residents have rebuilt it. In its last iteration as a post-Katrina neighborhood, the residents reestablished the Milneburg Neighborhood Association in order to define its boundaries, gain control of its redevelopment and restrict who lived there as well as what activities were permitted. This is a case study of the trajectory of Milneburg and the cultural adaptations of its residents to keep it distinct, vital and respectable.

Assessing the short-term socio-economic impacts of climate-led disasters on food trade networks requires new bottom-up models and vulnerability metrics rooted in complexity theory. Indeed, such shocks could generate cascading socio-economic losses across the networks layers where emerging agents¿ responses could trigger tipping points. We contribute to address this research gap by developing a multi-layer behavioral network methodology composed of multiple spatially-explicit layers populated by heterogeneous interacting agents. Then, by introducing a new multi-layer risk measure called vulnerability rank, or VRank, we quantify the stress in the aftermath of a shock. Our approach allows us to analyze both the supply- and the demand-side dimensions of the shock by quantifying short-term behavioral responses, the transmission channels across the layers, the conditions for reaching tipping points, and the feedback on macroeconomic indicators. By simulating a stylized two-layer supply-side production and demand-side household network model we find that, (i) socio-economic vulnerability to climate-led disasters is cyclical, (ii) the distribution of shocks depends critically on the network structure, and on the speed of supply-side and demand-side responses. Our results suggest that such a multi-layer framework could provide a comprehensive picture of how climate-led shocks cascade and how indirect losses can be measured. This is crucial to inform effective post-disaster policies aimed to build food trade network resilience to climate-led shocks, in particular in more agriculture-dependent bread-basket regions. / Series: Ecological Economic Papers