The socio-spatial dynamics of extreme urban heat events: The case of heat-related deaths in Philadelphia

Johnson, Daniel P.; Wilson, Jeffrey S.

doi:10.1016/j.apgeog.2008.11.004

Cited by 145 publications

(101 citation statements)

References 46 publications

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“…An assessment of differential exposure to urban heat stress by Uejio et al (2011) in Phoenix and Philadelphia found that heat distress calls in the former city and heat mortality in the latter city were correlated with higher proportions of minority residents and lower housing values. Other studies conducted in Philadelphia confirmed that the populations with high levels of social vulnerability also have relatively high levels of exposure to urban heat stress, as measured by satellite-derived land surface temperatures (Weber et al 2015, Johnson andWilson 2009). In a study of Washington, DC, Aubrecht and Ozceylan (2013) created a heat stress vulnerability index (HSVI) composed of census-derived social vulnerability metrics and a heat stress risk index (HSRI), which is a combination of the HSVI and exposure to extreme heat.…”

Section: Literature Reviewmentioning

confidence: 80%

Social vulnerability to floods in two coastal megacities: New York City and Mumbai

Sherbinin¹,

Bardy²

2016

Populationyearbook

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In this paper we assess differential exposure to flooding in two coastal megacities, New York and Mumbai, both of which suffered major flood-related disasters in the past decade. Specifically, we examine whether the most exposed populations are also the most socially vulnerable. First, we developed Social Vulnerability Indices (SoVIs) for each city with census data. We then overlaid the SoVI scores onto flood extent maps for Hurricane Sandy (New York, October 2012) and the Mumbai flash floods (July 2005), as well as for the evacuation zones for New York, to examine patterns of differential exposure. Our results suggest a degree of differential exposure in New York, especially in the highest flood risk areas, and provide fairly clear evidence for differential exposure in Mumbai. However, differences in the input resolution and confidence in the datasets for Mumbai make the results more uncertain. The paper concludes with a discussion of the policy implications and the data needs for urban spatial vulnerability assessments.

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Section: Literature Reviewmentioning

confidence: 80%

Social vulnerability to floods in two coastal megacities: New York City and Mumbai

Sherbinin¹,

Bardy²

2016

Populationyearbook

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“…For example, a vulnerability study in Phoenix, Arizona, demonstrated that pockets of communities within the study area faced greater exposure to heat stress, due to higher social and biophysical vulnerabilities (e.g., age and less vegetation), than the surrounding communities [ 22 ]. Spatial distribution of vulnerable populations overlapping with climate hazard segments within a city has yet to be fully explored [ 28 ]. Less utilized city-specifi c analyses may provide more information on social and biophysical vulnerability than larger, less spatially resolute studies at the regional and national levels [ 53 ].…”

Section: New Spatial Vulnerability Assessment Approachesmentioning

confidence: 99%

“…Incorporating LST, NDVI, and NDBI allows for the consideration of environmental impacts on vulnerability-previous studies have not integrated environmental properties and socioeconomic vulnerability in this way [ 62 ]. These indices can be used to represent the infl uence-either positive or negative-of environmental variables on the vulnerability of local populations [ 28 ].…”

Section: Satellite-based Remote Sensing As a Surveillance Toolmentioning

confidence: 99%

Modeling Vulnerable Urban Populations in the Global Context of a Changing Climate

Lulla

Stanforth

Prudent

et al. 2015

Innovating for Healthy Urbanization

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“…In analyzing the spatial dispersion of insurable counties, the eccentricity index (E) indicates the SDE polarity. A smaller value of E indicates a more polarized distribution, whereas a value close to 1 signifies a more uniform distribution (Johnson and Wilson 2009). To analyze the spatial relationship between SDEs of two categories of indices for the same hazard, the segregation index (S) was defined.…”

Section: Spatial Analysis Using Standard Deviational Ellipse (Sde)mentioning

confidence: 99%

Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China

Zhang

Tao

2017

Int J Disaster Risk Sci

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In this study, two categories of weather indexabsolute index and relative index-for chilling injury and heat damage of three main crops in China were assessed to identify insurable counties. First, correlations between selected weather indices and yield losses were examined for each county. If a correlation was significant, the county was categorized as ''insurable'' for the corresponding hazard or index. Second, the spatial distribution of insurable counties was characterized and finally, their correlation coefficients were analyzed at various spatial scales. The results show that the spatial patterns of insurable areas varied by categories of weather indices, crops, and hazards. Moreover, the weather indices based on relative threshold of temperature were more suitable for chilling injury in most regions, whereas the indices based on absolute threshold were more suitable for heat damage. The findings could help the Chinese government and insurance companies to design effective insurance products.

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The socio-spatial dynamics of extreme urban heat events: The case of heat-related deaths in Philadelphia

Cited by 145 publications

References 46 publications

Social vulnerability to floods in two coastal megacities: New York City and Mumbai

Social vulnerability to floods in two coastal megacities: New York City and Mumbai

Modeling Vulnerable Urban Populations in the Global Context of a Changing Climate

Performance of Temperature-Related Weather Index for Agricultural Insurance of Three Main Crops in China

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