Spatial heterogeneity of local flood vulnerability indicators within flood-prone areas in Taiwan

Chang, Hsueh-Sheng; Chen, Tzu-Ling

doi:10.1007/s12665-016-6294-x

Cited by 35 publications

(14 citation statements)

References 34 publications

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“…However, recent advancements in spatially constituted methodologies such as geographically weighted PCA (GWPCA) (Harris et al 2011;Dem sar et al 2013;Gollini et al 2014;Lu et al 2014) provide an opportunity for PCAbased vulnerability assessments to be explicitly spatial, accounting for the effect of surrounding locales on vulnerability in an area. This addresses a common critique of existing vulnerability indexes (Frazier, Thompson, and Dezzani 2014;Chang and Chen 2016).…”

mentioning

confidence: 95%

The Spatially Varying Components of Vulnerability to Energy Poverty

Robinson

Lindley

Bouzarovski

2019

Annals of the American Association of Geographers

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A household's vulnerability to energy poverty is socially and spatially variable. Efforts to measure energy poverty, however, have focused on narrow, expenditure-based metrics or area-based targeting. These metrics are not spatial per se, because the relative importance of drivers does not vary between neighborhoods to reflect localized challenges. Despite recent advancements in geographically weighted methodologies that have the potential to yield important information about the sociospatial distribution of vulnerability to energy poverty, the phenomenon has not been approached from this perspective. For a case study of England, global principal component analysis (PCA) and local geographically weighted PCA (GWPCA) are applied to a suite of neighborhood-scale vulnerability indicators. The explicit spatiality of this methodological approach addresses a common criticism of vulnerability assessments. The global PCA reaffirms the importance of well-established vulnerabilities, including older age, disability, and energy efficiency. It also demonstrates striking new evidence of vulnerabilities among precarious and transient households that are less well understood and have become starker during austerity. In contrast, rather than providing a single estimate of propensity to energy poverty for neighborhoods based on a national understanding of what drives the condition, the GWPCA identifies a diverse array of vulnerability factors of greatest importance in different locales. These local results destabilize the geographical configurations of an urban-rural and north-south divide that typify understandings of deprivation in this context. The geographically weighted approach therefore draws attention to vulnerabilities often hidden in policymaking, allowing for reflection on the applicability of spatially constituted methodologies to wider social vulnerability assessments.

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confidence: 95%

The Spatially Varying Components of Vulnerability to Energy Poverty

Robinson

Lindley

Bouzarovski

2019

Annals of the American Association of Geographers

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“…For a flood study, the factors that significantly affect a flood are vegetation type, soil type, and geological, geomorphological and hydrological characteristics. The acceleration of a flood could be caused by the reckless destruction of nature by human beings (Chang & Chen 2016); indiscriminate development, over-harvesting and deforestation are examples. By using these factors with a geographic information system (GIS), the studies of flood areas calculate and evaluate the effects of floods.…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Lee

Kim

Jung

et al. 2017

Geomatics, Natural Hazards and Risk

275

128

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Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

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“…A ocorrência de fenômenos naturais extremos, como fortes chuvas e inundações, não é considerado como um desastre natural se não produzir impacto em uma população. O impacto de um desastre natural depende não somente das características, probabilidade e intensidade do fenômeno natural, assim como da vulnerabilidade das condições geofísicas, socioeconômicas e ambientais das regiões expostas (KOBIYAMA et al, 2006;BOUWER, 2011;CHANG;CHEN, 2016).…”

Section: Introductionunclassified

Mapeamento De Áreas Suscetíveis a Inundações Na Cidade De Duque De Caxias

Oliveira

Barcellos

Silva

2022

RIC

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O planejamento urbano e sustentável considerando análises de risco torna-se urgente diante da intensificação de fenômenos naturais extremos. Nos últimos anos, mais de 25.000 pessoas foram afetadas por desastres naturais na cidade de Duque de Caxias (Estado do Rio de Janeiro - Brasil). Esta pesquisa buscou confeccionar e fornecer às autoridades governamentais da cidade de Duque de Caxias uma ferramenta auxiliar com o intuito de direcionar as ações voltadas para prevenção de riscos de inundações, planejamento urbano e desenvolvimento sustentável, através da elaboração do mapa de áreas propensas a inundações. O trabalho utilizou o método empírico de combinação da Análise Hierárquica Ponderada (AHP) e a plataforma GIS, dando pesos aos seguintes critérios escolhidos: declividade, altitude e uso e ocupação do solo. Em comparação com os dados de inundações noticiadas entre 1996 e 2015 em Duque de Caxias, os resultados mostraram que, apesar da baixa qualidade de dados para comparação, o mapa conseguiu indicar as áreas mais suscetíveis a inundações, mostrando-se ser uma ferramenta útil para gestão de riscos e planejamento urbano na cidade. A pesquisa mostrou que a utilização da AHP para confecção de mapas de risco à inundação pode ser uma metodologia confiável e aplicável a outras cidades do Brasil.

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Spatial heterogeneity of local flood vulnerability indicators within flood-prone areas in Taiwan

Cited by 35 publications

References 34 publications

The Spatially Varying Components of Vulnerability to Energy Poverty

The Spatially Varying Components of Vulnerability to Energy Poverty

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Mapeamento De Áreas Suscetíveis a Inundações Na Cidade De Duque De Caxias

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