Spatial modelling of disaster resilience using infrastructure components of baseline resilience indicators for communities (BRIC) in special region of Yogyakarta

2017 ◽  
Author(s):  
Febriana Kuscahyadi ◽  
Irwan Meilano ◽  
Akhmad Riqqi
Keyword(s):  
Spatial Modelling ◽  
Disaster Resilience ◽  
Resilience Indicators ◽  
Special Region

scholarly journals Twitter Use in Hurricane Isaac and Its Implications for Disaster Resilience

2021 ◽  
Vol 10 (3) ◽  
pp. 116
Author(s):  
Kejin Wang ◽  
Nina S. N. Lam ◽  
Lei Zou ◽  
Volodymyr Mihunov
Keyword(s):  
Census Data ◽  
Situational Awareness ◽  
Research Question ◽  
Community Resilience ◽  
Disaster Resilience ◽  
Positive Correlations ◽  
Social Environmental ◽  
Use Of Social Media ◽  
Resilience Indicators ◽  
Three Phases

Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized to monitor human response during different phases of disasters to better understand resilience. An important research question is: Does Twitter use correlate with disaster resilience? Specifically, will communities with more disaster-related Twitter uses be more resilient to disasters, presumably because they have better situational awareness? The underlying issue is that if there are social and geographical disparities in Twitter use, how will such disparities affect communities’ resilience to disasters? This study examines the relationship between Twitter use and community resilience during Hurricane Isaac, which hit Louisiana and Mississippi in August 2012. First, we applied the resilience inference measurement (RIM) model to calculate the resilience indices of 146 affected counties. Second, we analyzed Twitter use and their sentiment patterns through the three phases of Hurricane Isaac—preparedness, response, and recovery. Third, we correlated Twitter use density and sentiment scores with the resilience scores and major social–environmental variables to test whether significant geographical and social disparities in Twitter use existed through the three phases of disaster management. Significant positive correlations were found between Twitter use density and resilience indicators, confirming that communities with higher resilience capacity, which are characterized by better social–environmental conditions, tend to have higher Twitter use. These results imply that Twitter use during disasters could be improved to increase the resilience of affected communities. On the other hand, no significant correlations were found between sentiment scores and resilience indicators, suggesting that further research on sentiment analysis may be needed.

scholarly journals Assessment of Urban Resiliency Concerning Disaster Risk: A Review on Multi- Dimensional Approaches

2022 ◽  
Vol 12 (3) ◽  
pp. 111-125
Author(s):  
Tusar Kanti Roy ◽  
Sharmin Siddika ◽  
Mizbah Ahmed Sresto
Keyword(s):  
Assessment Tool ◽  
Disaster Resilience ◽  
Urban Resilience ◽  
Analytic Hierarchy ◽  
Qualitative And Quantitative ◽  
Resilience Index ◽  
Definition Of ◽  
Resilience Indicators ◽  
New Research ◽  
Hierarchy Process

There have been a number of new research published with different methodologies and frameworks in recent years, aimed at improving city resilience to a variety of man-made and natural calamities. As climate change progresses, resilience will become a more important topic in scientific and policy circles that influence future urban development. This review article first provides the definition of resilience. Then it represents some of the adopted methodologies in an extensive way. Approaches including Baseline Resilience Indicators for Communities (BRIC), Climate Disaster Resilience Index (CDRI), Disaster resilience index based on Analytic Hierarchy Process (AHP), Composite indicator based approach, Hyogo Framework and so on. This section discusses about urban resiliency assessments to mitigate vulnerability, offer a set of principles and indicators for creating an urban resilience assessment tool. Findings of this study not only address a variety of qualitative and quantitative aspects of urban resilience but also describes about different indicators such as environmental resources, socio-economic and built environment, infrastructure, governance and institutional indicators. Journal of Engineering Science 12(3), 2021, 111-125

scholarly journals Towards Developing a Model for the Evaluation of Hospital Disaster Resilience: A Systematic Review

2019 ◽  
Author(s):  
Saeed Fallah Aliabadi ◽  
Abbas Ostadtaghizadeh ◽  
Ali Ardalan ◽  
Farin Fatemi ◽  
Bijan Khazaei ◽  
...  
Keyword(s):  
Systematic Review ◽  
Key Words ◽  
Full Text ◽  
System Performance ◽  
Vital Role ◽  
Structural Systems ◽  
Disaster Resilience ◽  
Inclusion Criteria ◽  
Essential Services ◽  
Resilience Indicators

Abstract Background: Hospitals play a vital role in disaster stricken regions. The resilient hospitals will be able to provide essential services to affected people and it can mitigate the risk of injuries during and after disasters. The aim of this paper was to obtain the indicators required for the evaluation of hospital resilience. Methods: This systematic review was conducted in 2018. Through this systematic review, the international electronic databases were investigated for the researches published in English. The exclusion and inclusion criteria were determined to extract the hospital resilience indicators. These indicators were used in order to develop a model to keep the system performance at an acceptable level during disasters. Results: Out of 1794 researches that were published until September 2018, 89 articles and guidelines with full text were surveyed. Thirty-two articles and guidelines were then selected and analyzed to collect the indicators related to hospital disaster resilience (HDR). The domains and the indicators were extracted from these selected researches. We suggested a model including three domains and twenty seven subdomains. The three domains were constructive, infrastructural and administrative resilience. The relevant indicators were designed for each subdomain to measure the HDR. Conclusion ; Since diverse indicators affect hospital resilience, therefore, other studies should be conducted to propose some models or tools to quantify the hospital resilience in different countries and scopes with an all hazard approach. Key words : Disaster; Hospital; Resilience; Structural and non-structural systems; Indicators;

scholarly journals Towards Developing a Model for the Evaluation of Hospital Disaster Resilience: A Systematic Review

2020 ◽  
Author(s):  
Saeed Fallah Aliabadi ◽  
Abbas Ostadtaghizadeh ◽  
Ali Ardalan ◽  
Farin Fatemi ◽  
Bijan Khazaei ◽  
...  
Keyword(s):  
Systematic Review ◽  
Full Text ◽  
System Performance ◽  
Vital Role ◽  
Acceptable Level ◽  
Disaster Resilience ◽  
Inclusion Criteria ◽  
Essential Services ◽  
Resilience Indicators ◽  
Research Studies

Abstract Background: Hospitals play a vital role in disaster stricken regions. The resilient hospitals will be able to provide essential services to affected people and it can mitigate the risk of injuries during and after disasters. This study aimed to obtain the indicators required for the evaluation of hospital resilience. Methods: This systematic review was conducted in 2018. Through this systematic review, international electronic databases were investigated for the research studies published in English. The exclusion and inclusion criteria were determined to extract the hospital resilience indicators. These indicators will be used in order to develop a model to keep the system performance at an acceptable level during disasters. Results: Out of 1794 research studies published until September 2018, 89 articles and guidelines with full text were surveyed. Thirty-two articles and guidelines were then selected and analyzed to collect the indicators related to hospital disaster resilience (HDR). The domains and the indicators were extracted from these selected research studies. The authors collected and categorized them into three domains and twenty seven subdomains. The three domains included constructive, infrastructural, and administrative resilience. The relevant indicators were designed for each subdomain to assess HDR. Conclusion : Since diverse indicators affect hospital resilience, other studies should be conducted to propose some models or tools to quantify the hospital resilience in different countries and scopes with an all hazards approach.

Establishing Disaster Resilience Indicators for Tan-sui River Basin in Taiwan

2012 ◽  
Vol 115 (1) ◽  
pp. 387-418 ◽  
Author(s):  
Shin-Liang Chan ◽  
Wann-Ming Wey ◽  
Pin-Huai Chang
Keyword(s):  
River Basin ◽  
Disaster Resilience ◽  
Resilience Indicators

scholarly journals Implementing Disaster Policy: Exploring Scale and Measurement Schemes for Disaster Resilience

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Susan L. Cutter ◽  
Sahar Derakhshan
Keyword(s):  
National Level ◽  
Community Resilience ◽  
Disaster Resilience ◽  
Geographic Scale ◽  
Policy Makers ◽  
Disaster Policy ◽  
Data Resolution ◽  
Measurement Tools ◽  
Spatial Differences ◽  
Resilience Indicators

Abstract Resilience measurement continues to be a meeting ground between policy makers and academics. However, there are inherent limitations in measuring disaster resilience. For example, resilience indicators produced by FEMA and one produced by an independent academic group (BRIC) measure community resilience by defining and quantifying community resilience at a national level, but they each have a different conceptual model of the resilience concept. The FEMA approach focuses on measuring resilience capacity based on preparedness capabilities embodied in the National Preparedness Goals at state and county scales. BRIC examines community (spatially defined as county) components (or capitals) that influence resilience and provides a baseline of pre-existing resilience in places to enable periodic updates to measure resilience improvements. Using these two approaches as examples, this paper examines the differences and similarities in these two approaches in terms of the conceptual framing, data resolution, and representation and the resultant statistical and spatial differences in outcomes. Users of resilience measurement tools need to be keenly aware of the conceptual framing, input data, and geographic scale of any schema before implementation as these parameters can and do make a difference in the outcome even when they claim to be measuring the same concept.

scholarly journals Towards Developing a Model for the Evaluation of Hospital Disaster Resilience: A Systematic Review

2019 ◽  
Author(s):  
Saeed Fallah Aliabadi ◽  
Abbas Ostadtaghizadeh ◽  
Ali Ardalan ◽  
Farin Fatemi ◽  
Bijan Khazaei ◽  
...  
Keyword(s):  
Systematic Review ◽  
Full Text ◽  
System Performance ◽  
Vital Role ◽  
Structural Systems ◽  
Disaster Resilience ◽  
Inclusion Criteria ◽  
Essential Services ◽  
Resilience Indicators ◽  
Research Studies

Abstract Background: Hospitals play a vital role in disaster stricken regions. The resilient hospitals will be able to provide essential services to affected people and it can mitigate the risk of injuries during and after disasters. This study aimed to obtain the indicators required for the evaluation of hospital resilience. Methods: This systematic review was conducted in 2018. Through this systematic review, international electronic databases were investigated for the research studies published in English. The exclusion and inclusion criteria were determined to extract the hospital resilience indicators. These indicators will be used in order to develop a model to keep the system performance at an acceptable level during disasters. Results: Out of 1794 research studies published until September 2018, 89 articles and guidelines with full text were surveyed. Thirty-two articles and guidelines were then selected and analyzed to collect the indicators related to hospital disaster resilience (HDR). The domains and the indicators were extracted from these selected research studies. The authors collected and categorized them into three domains and twenty seven subdomains. The three domains included constructive, infrastructural, and administrative resilience. The relevant indicators were designed for each subdomain to assess HDR. Conclusion : Since diverse indicators affect hospital resilience, other studies should be conducted to propose some models or tools to quantify the hospital resilience in different countries and scopes with an all hazards approach. Key words : Disaster; Hospital; Resilience; Structural and Non-structural Systems; Indicators

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