## 3 Results
Analyzing standard errors for each urban area and time of the day showed that all results presented low standard errors, varying from 0.001 (Waterloo-Cedar Falls/Night) to 0.011 (Sioux City/Afternoon) than 0.011 as seen on Table 4.  
Among the independent variables describing urban morphometric features, NDVI, regardless of the statistic used or neighborhood distance, appeared as the most important, explaining the most variation in temperature in 14 out of 24 models, followed by BH (6 out of 24 models), CDM (3 out of 24), and CC (1 out of 24 models). Among the five neighbor distances being considered, the 800 m distance accounted for 52.5% of all variables, followed by 400 m (20%), 200 m (15.83%), 100 m (8.33%), and 50 m (3.33%). These results indicated that the morphometric feature influence (natural and built-up) in small neighbor distances has a smaller statistical influence on urban temperatures when compared to larger neighbor distances (e.g., the average building height in a 50 m radius seems to have considerably less statistical significance/influence in generating the predictive models than the average for building height in 800 m radius). This finding is particularly noteworthy,  
Figure 5: Measured temperature in Waterloo/Cedar Falls during the evening. Figure 6: Measured temperature in Waterloo/Cedar Falls during the night. as previous studies have shown that certain natural features, such as bodies of water, which are known to mitigate urban heat, typically exert their influence over relatively small areas [38]. Considering the statistical method of central tendency (mean vs. standard deviation) being computed, the mean seems to have a higher statistical influence on the predictive models than standard deviation, appearing in 72.5% as one of the five most important independent variables per model. Analyzing the predictive raster surface models generated for each study area, most of the high predictive results (based on the coefficient R2) resembled findings from other studies [17]. Evaluating the models by study area and time of the day against the collected temperature show similar minimum and maximum temperatures, an expected outcome given the high coefficients (R2) produced by random forest. Utilizing as an example the urban area of Waterloo/Cedar Falls, predictive raster models showed modeled temperature consistent with the temperature collected at all times of the day (Figures 7-9). The proportion of buildings in areas such as downtown Waterloo (eastern part of the map where high temperatures are shown) downtown Cedar Falls (north-center west of the map and southwest of the north cold area seeing on the map), and the University of Northern Iowa campus (west-central area of the map presenting high temperatures) influenced the high temperatures seen in those areas, especially during the nighttime, which is supported by the most important variables for that model (NDVI and BV). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Urban Area** & **Afternoon R2** & **Evening R2** & **Night R2** \\ \hline Burlington & 0.005 & 0.002 & 0.002 \\ \hline Cedar Rapids & 0.006 & 0.004 & 0.002 \\ \hline Council Bluffs & 0.004 & 0.003 & 0.002 \\ \hline Des Moines & 0.004 & 0.002 & 0.002 \\ \hline Fort Dodge & 0.007 & 0.006 & - \\ \hline Marshalltown & 0.008 & - & 0.008 \\ \hline Sioux City & 0.011 & 0.005 & 0.004 \\ \hline WaterlooCF & 0.005 & 0.002 & 0.001 \\ \hline Waverly & 0.007 & 0.009 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Standard Error for every Urban Area/Time of the Day.