Department of Geography and Environmental Planning, Towson University, 8000 York Road, Towson, MD, 21252, USA.
Knowledge Exchange for Resilience, Arizona State University, Tempe, AZ, 85287, USA.
J Urban Health. 2021 Jun;98(3):344-361. doi: 10.1007/s11524-021-00520-7. Epub 2021 Mar 25.
The objective of the present study was to examine the effects of a confluence of demographic, socioeconomic, housing, and environmental factors that systematically contribute to heat-related morbidity in Maricopa County, Arizona, from theoretical, empirical, and spatial perspectives. The present study utilized ordinary least squares (OLS) regression and multiscale geographically weighted regression (MGWR) to analyze health data, U.S. census data, and remotely sensed data. The results suggested that the MGWR model showed a significant improvement in goodness of fit over the OLS regression model, which implies that spatial heterogeneity is an essential factor that influences the relationship between these factors. Populations of people aged 65+, Hispanic people, disabled people, people who do not own vehicles, and housing occupancy rate have much stronger local effects than other variables. These findings can be used to inform and educate local residents, communities, stakeholders, city managers, and urban planners in their ongoing and extensive efforts to mitigate the negative impacts of extreme heat on human health in Maricopa County.
本研究旨在从理论、实证和空间角度探讨导致亚利桑那州马里科帕县与热相关疾病的人口、社会经济、住房和环境因素综合作用的影响。本研究利用普通最小二乘法（OLS）回归和多尺度地理加权回归（MGWR）分析健康数据、美国人口普查数据和遥感数据。结果表明，MGWR 模型在拟合优度方面明显优于 OLS 回归模型，这意味着空间异质性是影响这些因素之间关系的一个重要因素。65 岁以上的人口、西班牙裔人口、残疾人口、无车人口和住房入住率的局部效应比其他变量强得多。这些发现可以为当地居民、社区、利益相关者、城市经理和城市规划者提供信息和教育，以帮助他们持续和广泛地努力减轻极端高温对马里科帕县人类健康的负面影响。
