Scholarlink

<p>Extreme heat events increasingly threaten public health, particularly in rapidly urbanizing areas like Maricopa County, Arizona. This study addresses gaps in identifying communities most vulnerable to extreme heat and heat waves by creating a Heat Vulnerability Index (HVI) that integrates often-overlooked populations. Utilizing US census data, satellite imagery, chronic illness prevalence rates, and unhoused population data, this HVI assesses vulnerability across census tracts in Maricopa County’s diverse urban-rural landscape. Principal components analysis identified nine factors influencing heat vulnerability: (1) socioeconomic disadvantage; (2) isolation; (3) elderly populations; (4) chronic illness; (5) environmental risks; (6) African American race and language barriers, (7) Native American and unemployment status; (8) lack of housing and male; and (9) mobile home residents. Model validation found that heat-related mortality rate increased with heat vulnerability. Despite statistical limitations from data resolution and timeframe, this study integrates unhoused data into vulnerability assessments, emphasizing the need for equitable approaches that include underserved communities to address extreme heat vulnerability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Developing a Novel Heat Vulnerability Index for Maricopa County, Arizona

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

查看全文
  • Kevin Y. Chen,
  • Sarah L. Jackson,
  • Ethan A. Forbes,
  • Rita V. Burke

摘要

Extreme heat events increasingly threaten public health, particularly in rapidly urbanizing areas like Maricopa County, Arizona. This study addresses gaps in identifying communities most vulnerable to extreme heat and heat waves by creating a Heat Vulnerability Index (HVI) that integrates often-overlooked populations. Utilizing US census data, satellite imagery, chronic illness prevalence rates, and unhoused population data, this HVI assesses vulnerability across census tracts in Maricopa County’s diverse urban-rural landscape. Principal components analysis identified nine factors influencing heat vulnerability: (1) socioeconomic disadvantage; (2) isolation; (3) elderly populations; (4) chronic illness; (5) environmental risks; (6) African American race and language barriers, (7) Native American and unemployment status; (8) lack of housing and male; and (9) mobile home residents. Model validation found that heat-related mortality rate increased with heat vulnerability. Despite statistical limitations from data resolution and timeframe, this study integrates unhoused data into vulnerability assessments, emphasizing the need for equitable approaches that include underserved communities to address extreme heat vulnerability.