A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Understanding Social Disaster Resilience Using GeographicallyWeighted Regression: A Case Study of Seoul Metropolitan Area
학위논문 (석사)-- 서울대학교 대학원 : 건설환경공학부, 2017. 2. 지석호. ; Quantifying urban resilience under disaster events have been interested scholars through considerable period of time. Although many researches have tried to assess various dimensions of community resilience, it is still challenging to select significant attributes that can be used for the assessment model, especially for social resilience. This study proposes a practical assessment model of social resilience through the following steps: (1) examining appropriate variables that are considered to be related to disaster damage, (2) analyzing the impact of spatial heterogeneity of the social attributes by using Geographically Weighted Regression (GWR) method. A Geographic Information System (GIS) software, was used for the visualization of the assessment result. Through an experimental case study on Seoul Metropolitan Area, the author proposes meaningful variables and distinguishes the relationship between disaster damage and social resilience. 5 variables including population density, vulnerable age, disability, administrative service, and multi-cultural population are determined as related proxy variables to social resilience. Significant coefficients representing the influence of each variable to social resilience is derived in positive and negative values. The study looks forward to support the decision-making process of disaster management and to improve current disaster mitigation project planning. ; Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Problem Statement 6 1.3 Research Objectives 8 1.4 Research Scope 9 1.5 Research Process 10 Chapter 2. Literature Review 11 2.1 Disaster Resilience and Social Vulnerability 11 2.1.1 Disaster Resilience 11 2.1.2 Social Vulnerability and Resilience 13 2.2 Disaster Mitigation Project Planning 16 2.3 Social Resilience Assessments 18 2.4 Geographically Weighted Regression (GWR) 26 2.5 Visualization/Geographic Information System (GIS) 29 Chapter 3. Assessment Model Development 31 3.1 Variable Selection 32 3.2 Data Collection 36 3.2.1 Case Study Area: Seoul 36 3.2.2 Data Source 38 3.2.3 Data preprocessing 42 3.3 Geographically Weighted Regression (GWR) Analysis 45 Chapter 4. Assessment Model Evaluation 54 4.1 Verification 54 4.1.1 Quantitative Verification 54 4.1.2 Qualitative Verification 55 4.2 Validation 56 Chapter 5. Conclusion 58 5.1 Summary 58 5.2 Contributions and Future Study 59 Bibliography 62 Abstract (Korean) 70 ; Master
Understanding Social Disaster Resilience Using GeographicallyWeighted Regression: A Case Study of Seoul Metropolitan Area
학위논문 (석사)-- 서울대학교 대학원 : 건설환경공학부, 2017. 2. 지석호. ; Quantifying urban resilience under disaster events have been interested scholars through considerable period of time. Although many researches have tried to assess various dimensions of community resilience, it is still challenging to select significant attributes that can be used for the assessment model, especially for social resilience. This study proposes a practical assessment model of social resilience through the following steps: (1) examining appropriate variables that are considered to be related to disaster damage, (2) analyzing the impact of spatial heterogeneity of the social attributes by using Geographically Weighted Regression (GWR) method. A Geographic Information System (GIS) software, was used for the visualization of the assessment result. Through an experimental case study on Seoul Metropolitan Area, the author proposes meaningful variables and distinguishes the relationship between disaster damage and social resilience. 5 variables including population density, vulnerable age, disability, administrative service, and multi-cultural population are determined as related proxy variables to social resilience. Significant coefficients representing the influence of each variable to social resilience is derived in positive and negative values. The study looks forward to support the decision-making process of disaster management and to improve current disaster mitigation project planning. ; Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Problem Statement 6 1.3 Research Objectives 8 1.4 Research Scope 9 1.5 Research Process 10 Chapter 2. Literature Review 11 2.1 Disaster Resilience and Social Vulnerability 11 2.1.1 Disaster Resilience 11 2.1.2 Social Vulnerability and Resilience 13 2.2 Disaster Mitigation Project Planning 16 2.3 Social Resilience Assessments 18 2.4 Geographically Weighted Regression (GWR) 26 2.5 Visualization/Geographic Information System (GIS) 29 Chapter 3. Assessment Model Development 31 3.1 Variable Selection 32 3.2 Data Collection 36 3.2.1 Case Study Area: Seoul 36 3.2.2 Data Source 38 3.2.3 Data preprocessing 42 3.3 Geographically Weighted Regression (GWR) Analysis 45 Chapter 4. Assessment Model Evaluation 54 4.1 Verification 54 4.1.1 Quantitative Verification 54 4.1.2 Qualitative Verification 55 4.2 Validation 56 Chapter 5. Conclusion 58 5.1 Summary 58 5.2 Contributions and Future Study 59 Bibliography 62 Abstract (Korean) 70 ; Master
Understanding Social Disaster Resilience Using GeographicallyWeighted Regression: A Case Study of Seoul Metropolitan Area
천휘경 (author) / 지석호 / Hwikyung Chun / 공과대학 건설환경공학부
2017-01-01
Theses
Electronic Resource
English
DDC:
710
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