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From Data Gaps to Risk Maps: Evaluating Risk to Essential Services Access in Data-Scarce Communities
From Data Gaps to Risk Maps: Evaluating Risk to Essential Services Access in Data-Scarce Communities
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105213
- ISBN
- 9798291564875
- DDC
- 910
- 서명/저자
- From Data Gaps to Risk Maps: Evaluating Risk to Essential Services Access in Data-Scarce Communities
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 209 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: A.
- 주기사항
- Advisor: Guikema, Seth David.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약Communities depend on essential services, such as water, electricity, communication, health care, food, and education, to sustain daily life and for their development. However, these services are increasingly vulnerable to disruption from natural hazards. Disruptions in these services can lead to significant cascading failures, disproportionately affecting vulnerable populations and exacerbating existing inequities. This dissertation addresses the need for fine-scale, data-driven methods to assess disaster risk and its impact on essential service access in a way that is scalable and practical for data-scarce environments. This work presents four key contributions through the integration of operations research, risk analysis, and spatial modeling. First, it offers a set of criteria to evaluate and refine the conceptual foundation of existing frameworks developed to identify critical services and infrastructure, emphasizing the importance of local-level context. Second, it introduces a novel method for generating synthetic water distribution systems using publicly available data, enabling hydraulic analysis without proprietary utility datasets. Third, it evaluates projected power outage risks under a 3°C global warming scenario using seven global climate models, identifying regions with strong model agreement on increased outage risk. Finally, I present the AccES framework, developed to quantify building-level loss of access to essential services following disasters. The framework incorporates interdependencies among essential services and leverages open-source data to assess spatial inequities in post-disaster accessibility within the study area. This work aims to support more informed and inclusive decision-making in resilience planning, particularly in underserved or data-constrained communities. The methodologies developed in this dissertation align with international resilience goals and contribute to operationalizing people-centered disaster risk reduction strategies at the local level.
- 일반주제명
- Geography
- 일반주제명
- Environmental science
- 일반주제명
- Public administration
- 키워드
- Data gaps
- 키워드
- Risk maps
- 키워드
- Data scarcity
- 기타저자
- University of Michigan Industrial & Operations Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-02A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798291564875
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■035 ▼a(MiAaPQ)umichrackham006535
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a910
■1001 ▼aPagan Cajigas, Zaira P.
■24510▼aFrom Data Gaps to Risk Maps: Evaluating Risk to Essential Services Access in Data-Scarce Communities
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a209 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: A.
■500 ▼aAdvisor: Guikema, Seth David.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aCommunities depend on essential services, such as water, electricity, communication, health care, food, and education, to sustain daily life and for their development. However, these services are increasingly vulnerable to disruption from natural hazards. Disruptions in these services can lead to significant cascading failures, disproportionately affecting vulnerable populations and exacerbating existing inequities. This dissertation addresses the need for fine-scale, data-driven methods to assess disaster risk and its impact on essential service access in a way that is scalable and practical for data-scarce environments. This work presents four key contributions through the integration of operations research, risk analysis, and spatial modeling. First, it offers a set of criteria to evaluate and refine the conceptual foundation of existing frameworks developed to identify critical services and infrastructure, emphasizing the importance of local-level context. Second, it introduces a novel method for generating synthetic water distribution systems using publicly available data, enabling hydraulic analysis without proprietary utility datasets. Third, it evaluates projected power outage risks under a 3°C global warming scenario using seven global climate models, identifying regions with strong model agreement on increased outage risk. Finally, I present the AccES framework, developed to quantify building-level loss of access to essential services following disasters. The framework incorporates interdependencies among essential services and leverages open-source data to assess spatial inequities in post-disaster accessibility within the study area. This work aims to support more informed and inclusive decision-making in resilience planning, particularly in underserved or data-constrained communities. The methodologies developed in this dissertation align with international resilience goals and contribute to operationalizing people-centered disaster risk reduction strategies at the local level.
■590 ▼aSchool code: 0127.
■650 4▼aGeography
■650 4▼aArea planning & development
■650 4▼aEnvironmental science
■650 4▼aPublic administration
■653 ▼aData gaps
■653 ▼aRisk maps
■653 ▼aData scarcity
■653 ▼aEssential services access
■690 ▼a0796
■690 ▼a0366
■690 ▼a0768
■690 ▼a0617
■690 ▼a0341
■71020▼aUniversity of Michigan▼bIndustrial & Operations Engineering.
■7730 ▼tDissertations Abstracts International▼g87-02A.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359784▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
