Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA

Karanja, Joseph; Wanyama, Dan; Kiage, Lawrence M.

doi:10.1016/j.scitotenv.2021.151432

Cited by 5 publications

(6 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there were geospatial inconsistencies as well. Although prior studies constructed several indices mostly when evaluating methodological approaches [45][46][47]51], analyses overwhelmingly interpret one HVI despite exploring how results compare with other models being a fundamental composite indicator aspect [52]. This is especially problematic because subjective methodological decisions at various HVI construction stages can substantially impact assigned vulnerability scores and associated spatial distribution like this analysis of Southeast Florida demonstrates.…”

Section: Discussionmentioning

confidence: 99%

“…Although HVI has grown considerably in number over the past two decades, these tools have notable shortcomings that must be addressed. Many HVI [40][41][42][43][44][45][46][47] prioritize physical exposure and sociodemographic variables while health and adaptive capacity are underrepresented [20,48]. Neglecting either aspect misses crucial information.…”

Section: Introductionmentioning

confidence: 99%

“…Another shortcoming stems from overreliance on one HVI that may be flawed. With the exception of studies mostly evaluating different methodological ap-proaches [45][46][47]51], heat vulnerability analyses overwhelmingly interpret one HVI despite exploring links to other models being an integral composite indicator component [52]. Considering multiple HVI enhances analysis comprehensiveness and provides a more realistic picture of vulnerability [47,51].…”

Section: Introductionmentioning

confidence: 99%

“…With the exception of studies mostly evaluating different methodological ap-proaches [45][46][47]51], heat vulnerability analyses overwhelmingly interpret one HVI despite exploring links to other models being an integral composite indicator component [52]. Considering multiple HVI enhances analysis comprehensiveness and provides a more realistic picture of vulnerability [47,51]. Furthermore, when validation data is unavailable like this analysis of Southeast Florida and most former heat vulnerability studies [48], it can potentially bolster conviction about highly susceptible areas.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gauging Heat Vulnerability in Southeast Florida: A Multimodal Approach Integrating Physical Exposure, Sensitivity, and Adaptive Capacity

Cresswell

Mitsova

Liu

et al. 2023

IJGI

View full text Add to dashboard Cite

Urbanization and warming climate suggest that health impacts from extreme heat will increase in cities, thus locating vulnerable populations is pivotal. However, heat vulnerability indices (HVI) overwhelmingly interpret one model that may be inaccurate or methodologically flawed without considering how results compare with other HVI. Accordingly, this analysis applied a multimodal approach incorporating underrepresented health and adaptability measures to analyze heat vulnerability more comprehensively and better identify vulnerable populations. The Southeast Florida HVI (SFHVI) blends twenty-four physical exposure, sensitivity, and adaptive capacity indicators using uncommon statistical weights removing overlap, then SFHVI scores were compared statistically and qualitatively with ten models utilizing alternative methods. Urban areas with degraded physical settings, socioeconomic conditions, health, and household resources were particularly vulnerable. Rural and agricultural areas were also vulnerable reflecting socioeconomic conditions, health, and community resources. Three alternative models produced vulnerability scores not statistically different than SFHVI. The other seven differed significantly despite geospatial consistency regarding the most at-risk areas. Since inaccurate HVI can mislead decisionmakers inhibiting mitigation, future studies should increasingly adopt multimodal approaches that enhance analysis comprehensiveness, illuminate methodological strengths and flaws, as well as reinforce conviction about susceptible populations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gauging Heat Vulnerability in Southeast Florida: A Multimodal Approach Integrating Physical Exposure, Sensitivity, and Adaptive Capacity

Cresswell

Mitsova

Liu

et al. 2023

IJGI

View full text Add to dashboard Cite

show abstract

“…Previous studies have used spatial weighting in various applications, such as landscape ecology, urban population estimation, and heat vulnerability interpolation. (11)(12)(13) Empirical analysis related to the regional supply and demand of older adult welfare services has been conducted for a long time to prevent the omission or duplicate provision of services. (14,15) One study showed that it was cost-effective to highlight the equity of welfare services as a matter of spatial justice.…”

Section: Introductionmentioning

confidence: 99%

Spatial Weighting to Explore Priority Districts for Older Adult Classes in Age-friendly Smart City

Um¹

2022

Sensors and Materials

View full text Add to dashboard Cite

We explored priority districts for older adult classes using a spatial weighting technique in an age-friendly smart city initiated by the World Health Organization (WHO) and the Korean government. In the past, the target districts for establishing older adult classes for people aged 60 years and above in South Korea were determined on the basis of the experience or intuition of business operators or the empirical knowledge of a few experts. A hotspot map provides a basis for the public to evaluate the area-wide distribution of older adult classes. Exploring priority districts based on geographically weighted regression (GWR) could offer objective and quantitative evidence for the location feasibility of older adult classes rather than the existing text-based data. The priority ranking presented by each administrative district plays a crucial role in arranging older adult classes in consideration of the unique characteristics of an agefriendly smart city, such as the central business district or outskirts of a metropolitan city. Although South Korea has been selected as a case study for this paper, exploring priority districts for older adult classes utilizing a spatial weighting technique is relevant to other countries attempting to introduce wearable healthcare devices connected to a body sensor network (BSN) for people aged 60 years and above in age-friendly smart cities.

show abstract

Temporal and spatial variations of eco-environment in Association of Southeast Asian Nations from 2000 to 2021 based on information granulation

Liao

2022

Journal of Cleaner Production

View full text Add to dashboard Cite

Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA

Cited by 5 publications

References 44 publications

Gauging Heat Vulnerability in Southeast Florida: A Multimodal Approach Integrating Physical Exposure, Sensitivity, and Adaptive Capacity

Gauging Heat Vulnerability in Southeast Florida: A Multimodal Approach Integrating Physical Exposure, Sensitivity, and Adaptive Capacity

Spatial Weighting to Explore Priority Districts for Older Adult Classes in Age-friendly Smart City

Temporal and spatial variations of eco-environment in Association of Southeast Asian Nations from 2000 to 2021 based on information granulation

Contact Info

Product

Resources

About