A GIS Approach to Analyzing the Spatial Pattern of Baseline Resilience Indicators for Community (BRIC)

Sung, Chien Hao; Liaw, Shyue Cherng

doi:10.3390/w12051401

Cited by 24 publications

(11 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Resilience has spatial differences. The resilience of most urban areas is higher than that of rural areas ( 42 , 87 ), and according to the research of Sung and Liaw in Taiwan, topography is the most important factor causing social and economic differences as the socio-economic resilience of mountainous areas is often relatively low ( 87 ). Compared with urban areas, the topography in rural areas of China is complex and changeable.…”

Section: Resultsmentioning

confidence: 99%

Seismic Resilience of Rural Water Supply Systems; Factor Analysis of Cases Set in Sichuan Province, China

Zhou

Kalonji

Chen

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

The seismic resilience of water supply systems can be impacted by numerous factors, but what these factors are in the rural context of China is unknown. In this study, 41 potential influencing factors of seismic-resilience for rural water supply system (RWSS) were obtained through a literature review and semi-structured expert interview, comprising 26 general influencing factors (GFs) and 15 water supply safety influencing factors (SFs). This study verified and ranked these factors through a questionnaire survey delivered to RWSS stakeholders in Sichuan Province, China. Based on 123 valid, returned questionnaires, these factors are divided into 9 factor groups through factor analysis performed on GFs and SFs, respectively, of which “economic resilience” and “organizational resilience in disaster prevention stage” are shown to be the most important factor groups. Additionally, it found that the experience of earthquake events significantly affects the perceptions of stakeholders on the importance of certain factors. Specifically, stakeholders who have experienced an earthquake prioritize the post-earthquake resilience of the system, while those who have not experienced an earthquake prioritize the absorption capacity of the system in the disaster prevention stage. Thus, it is not appropriate to use fixed weights to evaluate the seismic resilience of RWSSs. Significantly, this outcome differs from existing findings on the resilience of Urban Water Supply Systems (UWSSs), where “technical resilience” is the key dimension. These findings can help decision-makers fully understand the factors affecting the seismic resilience of RWSSs in China, and in doing so, augment the strengthening of rural water supply.

show abstract

Section: Resultsmentioning

confidence: 99%

Seismic Resilience of Rural Water Supply Systems; Factor Analysis of Cases Set in Sichuan Province, China

Zhou

Kalonji

Chen

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…The global spatial autocorrelation of SFQ grades can be used to study the aggregation or dispersion degree of SFQ on the whole. This paper uses the global Moran's I index to calculate the spatial autocorrelation characteristics of SFQ grades [51,52], and the calculation formula is as follows.…”

Section: Global Spatial Autocorrelation Analysis Methodsmentioning

confidence: 99%

“…The autocorrelation characteristics among the spatial factors are mainly determined by global spatial autocorrelation and local spatial autocorrelation indexes. Here, the global spatial autocorrelation reflects the regional global spatial autocorrelation, while the local spatial autocorrelation further measures the correlation between the attribute eigenvalues of each geographic unit and those of its adjacent spatial units [51,52].…”

Section: Spatial Autocorrelation Characteristics Of Sfqmentioning

confidence: 99%

Spatial Structure Characteristics of Slope Farmland Quality in Plateau Mountain Area: A Case Study of Yunnan Province, China

Chen

Shi

2020

Sustainability

View full text Add to dashboard Cite

As an important part of farmland, the slope farmland is widely distributed in the central and western plateau mountain region in China. It is necessary to scientifically evaluate the slope farmland quality (SFQ) and analyze the spatial structure characteristics of SFQ to ensure reasonable utilization and partition protection of slope farmland resources. This paper takes the typical plateau mountain region—Yunnan Province in China—as an example and systematically identifies the leading factors of SFQ. The sloping integrated fertility index (SIFI) is adopted to reflect the SFQ. The evaluation system is built to quantitatively evaluate the SFQ and the spatial structure characteristics of SFQ were analyzed by a geostatistical model, autocorrelation analysis and spatial cold–hot spot analysis. The results show that the SFQ indexes in Yunnan Province are between 0.36 and 0.81, with a mean of 0.59. The SFQ grade is based on sixth-class, fifth-class, seventh-class and fourth-class land. The SFQ indexes present a normal spatial distribution, and the Gaussian model fits well with the semi-variance function of the spatial distribution of SFQ indexes. Furthermore, the spatial distribution of SFQ indexes is moderately autocorrelated. The structural factors play a major role in the spatial heterogeneity of SFQ indexes, but the influence of random factors should not be ignored. The spatial distribution of SFQ grades has a significant spatial aggregation characteristic, and the types of local indicators of spatial association (LISA) are based on high–high (HH) aggregation and low–low (LL) aggregation. The cold spot and hot spot distributions of SFQ grades display the significant spatial difference. The hot spot area is mainly distributed in Central Yunnan and the Southern Fringe, while the cold spot area mainly distributes in the Northeastern Yunnan, Northwestern Yunnan and Southwestern Yunnan. This study could provide a scientific basis for SFQ management and ecological environment protection in the plateau mountain region.

show abstract

“…The Baseline Resilience Indicators for Communities (BRIC) model proposed by Cutter et al [ 17 ] is widely applied and provides a series of secondary indicators to evaluate community resilience. BRIC is a quantifiable index and has been applied in several different regions, such as Taiwan, Norway, and Australia [ 17 , 18 , 19 , 20 ]. The model has mainly been used to identify appropriate strategies for building and enhancing community resilience.…”

Section: Introductionmentioning

confidence: 99%

The Influence of COVID-19 on Community Disaster Resilience

Xiang

Proverbs

et al. 2020

IJERPH

View full text Add to dashboard Cite

Global pandemics, such as the Coronavirus Disease 2019 (COVID-19), have serious harmful effects on people′s physical health and mental well-being. It is imperative therefore that we seek to understand community resilience and identify ways to enhance this, especially within our cities and communities. Therefore, great emphasis is now placed on how cities prepare for and recover from such disasters, and community resilience has emerged as a key consideration. Drawing upon research on the theory of resilience, this study seeks to identify the factors that influence community resilience and to analyze their causation toward helping to manage the risks associated with the COVID-19 pandemic. Seventeen factors from the five dimensions of social capital, economic capital, physical environment, demographic characteristics, and institutional factors are used to construct an index system. This is used to establish the structural level and importance of each factor. Data were collected using a questionnaire survey involving 12,000 members of key community groups in the city of Wuhan. An interpretative structural model (ISM) combining the analytic hierarchy process (AHP) method was then used to obtain the multi-level hierarchical structure composed of direct factors, indirect factors, and fundamental factors. The results show that the income level, vulnerability of the population, and the built environment are the main factors that affect the resilience of communities affected by COVID-19. These findings provide useful guidance toward the effective planning and design of urban construction and infrastructure. The results are expected to be useful to inform future decision-making and toward the long term, sustainable management of the risks posed by COVID-19.

show abstract

A GIS Approach to Analyzing the Spatial Pattern of Baseline Resilience Indicators for Community (BRIC)

Cited by 24 publications

References 56 publications

Seismic Resilience of Rural Water Supply Systems; Factor Analysis of Cases Set in Sichuan Province, China

Seismic Resilience of Rural Water Supply Systems; Factor Analysis of Cases Set in Sichuan Province, China

Spatial Structure Characteristics of Slope Farmland Quality in Plateau Mountain Area: A Case Study of Yunnan Province, China

The Influence of COVID-19 on Community Disaster Resilience

Contact Info

Product

Resources

About