Urban Resources Selection and Allocation for Emergency Shelters: In a Multi-Hazard Environment

Chen, Wei; Zhai, Guofang; Ren, Chongqiang; Shi, Yijun; Zhang, Jianxin

doi:10.3390/ijerph15061261

Cited by 31 publications

(27 citation statements)

References 45 publications

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“…SCIP is a noncommercial solver for mixed integer programming and mixed integer nonlinear programming and it is also a framework for constraint integer programming and branchcut-and-price (SCIP 2018). Branch-cut-and-price method is a combination of branch-and-price and branch-and-cut [39] research, potential shelter locations in a pilot area in China were analyzed and 41% of potential shelter locations were determined to be school playgrounds. For model A (earthquake scenario 1) and C (earthquake scenario 2), risk factor of each SA was calculated, depending on the neighborhood it is located at, using the risk factor of neighborhoods in Table 2.…”

Section: Application In Turkeymentioning

confidence: 99%

A Multi-Objective Stochastic Model for an Earthquake Relief Network

Yenice

Samanlıoğlu

2020

Journal of Advanced Transportation

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Earthquake relief network involves storage and distribution of relief aid to people in need. In this paper, a new stochastic multi-objective mixed integer mathematical model is developed and implemented in Kadikoy municipality of Istanbul, Turkey in order to configure part of the earthquake relief network. The aim of the model is to help decision makers decide on the locations of storage areas for shelters pre-earthquake and distribution of shelters from these areas to temporary shelter areas post-earthquake while minimizing earthquake scenario-specific total expected distribution distance, total expected earthquake damage risk factor of storage areas and expected total penalty cost related to unsatisfied demand at temporary shelter areas, simultaneously. In the model, storage area capacity and coverage distance restrictions are taken into consideration. The data related to potential storage areas and shelter locations were obtained from Kadikoy municipality of Istanbul and Istanbul Metropolitan Municipality (IMM). The earthquake damage risk factors were determined based on possible earthquake scenarios given in Japan International Cooperation Agency’s (JICA) report. Four event scenarios with two different earthquake scenario likelihoods were considered and sample efficient solutions from the Pareto frontier were obtained implementing the normalized (scaled) weighted sum method.

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Section: Application In Turkeymentioning

confidence: 99%

A Multi-Objective Stochastic Model for an Earthquake Relief Network

Yenice

Samanlıoğlu

2020

Journal of Advanced Transportation

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“…For example, with valid disaster insurance, residents can be reimbursed by insurance companies after the disaster to rebuild their economic resilience (Kaushalya, Karunasena, and Amarathunga, ). Another example is the provision of emergency shelters, which can ensure people's lives and improve disaster resilience during an evacuation (Chen et al., ). (3)Natural capital in the community mainly reflects the behavior of a community's energy and resource requests and the waste output in the operation and maintenance phases (Ma and Wang, ). Regarding disasters, however, natural capital emphasizes the ability to use resources and energy, rely on or modify the natural environment to reduce and mitigate the effects of a disaster, and quickly restore the original functions of communities after disasters (Freitag et al., ).…”

Section: Community Disaster‐adapting Capitalsmentioning

confidence: 99%

Measuring the Disaster Resilience of an Urban Community Using ANP‐FCE Method from the Perspective of Capitals

Cui¹,

Li²

2019

Social Science Quarterly

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Objective. Facing increasingly frequent disasters, the resilience concept can make up the limitations of traditional community disaster management. This article evaluated the disaster resilience in an urban community from the perspective of capital, and provides measurements for stakeholders to enhance the community resilience. Methods. On the basis of selected capital indicators using systematic literature review, the urban community resilience framework is established based on analytic network process and fuzzy comprehensive evaluation. Finally, the results are analyzed using the importance-performance analysis. Results. First, 12 indicators within three dimensions of community capitals are identified. Then, the weights of social, economic, and natural capital are calculated as 33, 41, and 26 percent, respectively. Afterward, taking a waterlogging disaster as an example, an aged community acquired a 46.57 score of resilience. The importance-performance quadrant shows the priority of factors to be improved. Conclusion. Relationship, norms, demographic characteristics, voluntary activity, physical facilities, communication system, general property, dedicated assets, resources, energy, ecological environment, and artificial environment are considered as key capitals of community resilience. In addition, a general framework to calculate and evaluate the resilience in a community is established that provides a benchmark for rating the resilience of urban communities.Various natural hazards-such as earthquake, storm, flood, and waterlogging-are becoming increasingly normalized. These uncertain disturbances described as the "worst in a hundred-year period" or "ever recorded," as well as "the most serious in history" are often reported in the news and have catastrophic consequences for urban communities, including human casualties, property damage, functional failures, and social order imbalance. For example, two destructive hurricanes, Harvey and Irma, made landfall in the southern United States in 2017, resulting floods, power failure, and severe casualties. In 2012, China's capital, Beijing, suffered an extremely heavy rainstorm. Seventy-nine people died, more than 10,000 community houses collapsed, and the economic losses reached 11.64 billion yuan. In 2011, a 9.0 magnitude earthquake and a tsunami in Tohoku, Japan, killed more than 18,500 people and displaced nearly half a million.Governments and organizations worldwide have issued a series of policies and regulations to combat the disaster problems. Among them, the concept of "resilience" has attracted increasing attention worldwide and offers unique advantages for

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“…These facilities are able to accommodate people and provide food and water, as well as basic first aid, pet shelter (as appropriate), health support, and basic disaster services [5]. Numerous studies have made progress in areas such as shelter planning support methods, space suitability assessment, locations optimization, emergency materials and facilities allocation, and sheltering behavior and psychology [6][7][8][9][10][11]. Since the users of the shelters are citizens, a key issue during possible evacuation activity is determining the ISPRS Int.…”

Section: Introductionmentioning

confidence: 99%

“…J. Geo-Inf. 2020, 9, 41 2 of 21 spatial distribution of potential evacuees for emergency shelter demand, which is directly related to the rationality of shelter locations and evacuation efficiency, that is, whether all the shelters in the city can provide refuge for people at a reasonable evacuation distance and time during a disaster [8,9,12]. Mapping the regional distribution of the population is a basis for analyzing the shelter demand [13,14].…”

Section: Introductionmentioning

confidence: 99%

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

Chen

Yao

Zhai

et al. 2020

IJGI

Self Cite

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Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning.

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Urban Resources Selection and Allocation for Emergency Shelters: In a Multi-Hazard Environment

Cited by 31 publications

References 45 publications

A Multi-Objective Stochastic Model for an Earthquake Relief Network

A Multi-Objective Stochastic Model for an Earthquake Relief Network

Measuring the Disaster Resilience of an Urban Community Using ANP‐FCE Method from the Perspective of Capitals

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

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