## 4 Discussion  
The main goal of this study was to determine if there was any correlation between the hottest areas of the city and certain demographic, socioeconomic, and land use factors. Percent impervious surface and percent canopy cover were determined to be the two \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Q1 Average** & **Q4 Average** & \
\begin{tabular}{c} **Temperature Difference** \\ \({}^{\circ}\)**C (Q4-Q1)** \\ \end{tabular} &
\begin{tabular}{c} \(t\)**-Test** \\ \(p\)**-Value** \\ \end{tabular} \\ \hline Median household income (\$) & 19,046 & 80,909 & \(-0.5\) & \(1.72\times 10^{-5}\) \\ \hline Drove alone to work (\%) & 46.12 & 88.77 & \(-0.46\) & \(0.0001\) \\ \hline Non-Hispanic white population (\%) & 4.6 & 87.81 & \(-0.33\) & \(0.0038\) \\ \hline Bachelor’s degree or more (\%) & 2.94 & 72.88 & \(-0.29\) & \(0.0106\) \\ \hline White population (\%) & 3.28 & 90.24 & \(-0.28\) & \(0.0091\) \\ \hline Black population (\%) & 5.44 & 93.64 & 0.26 & \(0.0124\) \\ \hline Hispanic population (\%) & 0 & 14.95 & 0.09 & \(0.4284\) \\ \hline \hline \end{tabular}  
* Q1 and Q4 averages indicate the average value of the variable within those quartiles. The temperature difference (\({}^{\circ}\)C) shows the heat difference between the Q1 average temperature and the Q4 average temperature. Positive values indicate that Q4 blocks are warmer than Q1 blocks and negative values indicate that Q1 blocks are warmer than Q4 blocks.  
\end{table} 
Table 2: Quartile analyses of the seven variables associated with afternoon temperature from the 2010 American Community Survey (ACS), ordered from most to least significant. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **South (206 East)** & **South (206 West)** & **North (207)** \\ \hline Mean afternoon temperature (\({}^{\circ}\)C) & 35.69 & 35.7 & 35.96 \\ \hline Below poverty level (\%) & 8.3 & 15.67 & 39.27 \\ \hline Per capita income (\$) & 40,508 & 37,757 & 17,104 \\ \hline Median household income (\$) & 66,620 & 45,673 & 22,135 \\ \hline White population (\%) & 48.46 & 73.11 & 17.1 \\ \hline Black population (\%) & 34.46 & 19.34 & 75.52 \\ \hline Redlining Category D (\%) & 67.8 & 38.37 & 56.89 \\ \hline Property value (\$) & 232,000 & 229,950 & 129,888 \\ \hline \hline \end{tabular}  
* South Church Hill is divided into two census blocks, since both the eastern and western blocks that comprise the area are included.  
\end{table} 
Table 3: Comparison of temperature, socioeconomic, and demographic data between North and South Church Hill. strongest variables associated with afternoon temperature, meaning that the higher the proportion of impervious surfaces or the less canopy cover an area has, the warmer the area is likely to be. These factors have direct mechanistic influences on temperature, and the strength of these observed associations was not surprising. The next four strongest correlates each were associated with approximately 0.5 \({}^{\circ}\)C in temperature difference and included a variety of social and historical factors including the redlining HOLC security ratings of the census blocks. Our results are representative of national patterns showing the lingering effects of redlining. In a related study, redlined communities were found to be hotter than non-redlined neighbors in 94% of the 108 U.S. cities studied [21]. This extreme heat, in turn, poses a significant threat in terms of adverse health effects for the communities living there [33]. One group that is highly impacted is pregnant women, specifically Black women, living within urban heat islands [34; 35; 36]. In a meta-analysis conducted to evaluate the association between heat exposure and birth issues, it was found that the chance of a premature birth increases by 11.6% for every 5.6 \({}^{\circ}\)C increase in temperature [37]. As climate change inevitably worsens over the coming years, such issues will be exacerbated.