Community Resilience to Climate Change: Theory, Research and Practice

151 human health, in part due to the exposure overnight, when physiological responses [50,51,52] can be acute among those with pre- existing health conditions. As a result, this study utilizes the evening temperature model in an attempt to identify areas with prolonged exposure to high temperatures. The 7 pm model has an R2 of 0.9715 and an RMSE of 0.2078. Using “zonal statistics” in ArcGIS (ESRI, Redlands, CA, USA), average UHI temperature was calculated for each census block, ranging from 26.4 to 30.1 °C. This aggregation method simplifies the UHI dataset, however this alteration of the raw data is deemed worthwhile in order to assess relationships with demographic data. Additionally, this is a common practice in geographic analysis [15,53,54]. In the context of this study, “refuge” refers either to public cooling facilities, or availability of central air conditioning in one’s home; in other words, the availability of coping mechanisms. Public heat refuge data were obtained from the Multnomah County Office of Aging, Disability and Veterans Services [55]. These include three County cooling centers; 33 places to play in the water; 59 libraries; and 73 community centers. The heat refuges were geocoded using Google Earth. Nine out of 33 places to play in the water are not free for personal use, but are treated as such for the purpose of this study. Residential Central Air Conditioning (CAC) data were obtained from the Multnomah County Assessment Office [55]. 2.3. Analysis This study assessed multiple facets of vulnerability through the use of mixed spatial and statistical methods, with the aim of identifying not only those hottest areas of the city, but also trends of socio-demographic disparity. Elements considered include exposure of sensitive populations, as well as their ability to cope with heat by accessing refuge. First, heat exposure was determined by mapping UHI data at the census block group (CBG) level. Using the “raster” package in R statistical software, the mean of all pixels falling geographically within an individual CBG polygon was appended onto that polygon’s data table, resulting in a visual representation of spatial temperature distribution. This indicated areas of the city most exposed to extreme heat. Second, the relationship between various socio-demographic groups and high-exposure areas was assessed using the Student’s t-test method, where α = 0.05 for all tests. This method reveals which sensitive groups, if any, are disproportionately exposed to extreme heat conditions. In each case, two groups are compared: those with low adaptive capacity characteristics, and those with high adaptive capacity characteristics. Indicators included in this analysis, bifurcations of each, and hypotheses tested are as follows. H0: Average of low income population − Average of high income population = 0 H1: Average of low income population − Average of high income population > 0 where: Average of low income population = Mean temperature of low income block group Average of high income population = Mean temperature of high income block group H0: Average of non-white population − Average of white population = 0 H1: Average of non-white population − Average of white population > 0 where: Average of non-white population = Mean temperature of block groups with large non-white population Average of non-white population = Mean temperature of block groups with small non-white population H0: Average of low education population − Average of high education population = 0 H1: Average of low education population − Average of high education population > 0 where: Average of low education population = Mean temperature of block groups with large population with less education Average of low education population = Mean temperature of block groups with small population with less education H0: Average of isolated elderly population − Average of accompanied elderly population = 0 H1: Average of isolated elderly population − Average of accompanied elderly population > 0 where: Average of isolated elderly population = Mean temperature of block groups with large population of isolated elderly Average of isolated elderly population = Mean temperature of block groups with small population of isolated elderly H0: Average of low English proficiency population − Average of English proficiency = 0 H1: Average of low English proficiency population − Average of high English proficiency population > 0 where: Average of low English proficiency population = Mean temperature of block groups with large population low English proficiency Average of low English proficiency population = Mean temperature of block groups with small population with low English proficiency H0: Average of population in affordable housing − Average of population in non-affordable housing = 0