## 2 Materials and Methods
### Data  
The spatial distribution of temperature in the City of Richmond is derived from a field sample conducted on 13 July 2017. On this day, one of the hottest of the summer, teams of researchers, students, and volunteers walked, biked (three bicycles), or drove (nine cars) throughout 12 areas of the city with vehicle-mounted sensors that recorded their spatial coordinates at one-second intervals and corresponding afternoon (3:00-4:00 p.m.) temperature using a type \"T\" thermocouple and data logger [27]. The tens of thousands of observations generated were then fed into a machine learning algorithm to model temperature at 10-m\({}^{2}\) resolution based on the associations between these observations and spectral data from the Sentinel-2 satellite constellation [27]. A full description of the temperature data collection, quality assurance, and modeling is available in [PERSON] et al.,  
Figure 4: Zoning categories in Richmond, VA that resulted from the 1942 zoning ordinance and areas with greater than 25% Black population in 1940. Source: Richmond Planning and Development Review. 2019 [27]. Our study relates to [PERSON] et al., 2019 by expanding on their urban heat island location prediction technique to determine if the hottest areas of Richmond, VA are occupied by certain socioeconomic, racial, or ethnic groups. The temperature map was then aggregated by census block groups [28] using ArcGIS Pro and areas of extreme high temperature were classified as urban heat islands (Figure 5). Impervious surface and canopy cover data were retrieved from the National Land Cover Database (NLCD) [23]. Green space data were sourced from an ArcGIS map of parks and open space created for the Richmond 300 Master Plan [29]. The \"parks and open space\" layer used to represent green space encompasses recognized parks, sports fields, farmer's markets, community gardens, cemeteries, and other vegetated areas. These maps were overlaid with a map of Richmond's census blocks [28] using ArcGIS Pro, and the percentage of each census block comprising impervious surface, canopy cover, and green space was calculated (Figure 6). Figure 5: Average afternoon temperature by census block group in Richmond, VA in July 2017. Images on the left provide an example of how data from one of the bike-route transects was used to create the temperature model for the city. Values from the temperature model were then aggregated by census block. Source: [PERSON] et al., 2019. Redlining data were obtained from the \"Mapping Inequality\" database of the University of Richmond's Digital Scholarship Lab [30], with the city divided into polygons designated by one of four HOLC security ratings: A = Best, B = Still Desirable, C = Definitely Declining, D = Hazardous. The security ratings were meant to indicate the residential desirability of a neighborhood, as well as the \"threat of infiltration of foreign-born, negro, or lower-grade population\" [30]. The proportion within each census block of each HOLC rating was calculated. In subsequent analyses, Grade A \"Best\" and Grade B \"Still Desirable\" sections were combined, while Grade C \"Definitely Declining\" and Grade D \"Hazardous\" sections were combined. Demographic data were collected to assess any associations between demographic factors and temperature data. We aggregated data from American Community Survey's (ACS) most recent 5-year estimates (2014-2018) at the block group level [31]. We collected data for the following attributes: total population (table A00001), race and ethnicity (tables A03001 and A04001), household income (table A14006), highest educational attainment (table A12002), transportation to work for workers 16 years and older (table A09005), poverty status in 2018 (tables A13003A, A13003B, and A13003C), per capita income (table A14024), and number of housing units in each structure (table A10032). Race and ethnicity were normalized as a percent of the total population.