## 4 Discussion and Conclusions  
This study carried out a systematic examination of temperature patterns across a variety of urban areas in Iowa based on extensive in-field temperature monitoring on selected hot days in afternoon, evening, and night times and highly detailed spatial statistical modeling. The three selected times for data collection were based on similar studies from the existing literature. This sampling effort led to a unique and robust dataset of air temperature measurements (more than 110,000 temperature points were collected) in ten different areas of diverse urban features. Even though methods of air temperature collection measured by mobile devices have been researched in previous studies, this study represents a novel effort to collect data across multiple urban areas in a single state. Driving routes were designed to capture temperature data across a variety of urban land uses thus providing the necessary data required for the development of geospatial models with high predictive power throughout most, if not all urban areas. However, as all data were acquired by driving on paved roads within a 1 h timeframe, it is uncertain how well the recorded temperatures could represent the whole city's thermal environment at a particular time. Research has shown that road length and number of stops while driving could play a role in the temperature measurements [39]. Modeled results generated by random forest algorithms resulted in coefficients that varied from an R\({}^{2}\) of 0.879 to 0.997 (all statistically significant), with most coefficients resulting in R\({}^{2}\) higher than 0.95 and standard error no higher than 0.011, which seemed consistent with other studies utilizing mobile sensors for temperature data collection, LiDAR data and/or aerial imagery to derive independent variables, and the use of RF as a machine learning algorithm [17; 18; 23]. The results indicate that the chosen independent variables and neighborhood distances were effective in generating detailed and accurate temperature raster surfaces in urban environments. Predicted surface temperatures in this study were found to be within a very similar range of collected air temperature, showing the expected consistency between collected air temperature and predicted air temperature. Regarding the time of day when air temperature was modeled and predicted in each urban area, the results exhibited similarities with previous studies [18; 33], which found the highest predictive power during the evening or night periods. Surprisingly, the consistency of certain independent variables, such as NDVI, as the most significant factors in explaining the variation in the models across different study areas suggests that, theoretically, even fewer independent variables could have been employed. NDVI not only appears as the most important variable, it also appears as the most reoccurring important variable for the second, fourth, and fifth most important variable across all 24 models. In comparison to the study conducted by [PERSON] and [PERSON] [17], the most important variable for the three models provided (morning, afternoon, and evening), was building height, while in studies such as [PERSON] et al. [23] and [PERSON] et al. [18] found the most important variables in explaining the modeled temperature were relative humidity and NDVI. Regarding the five neighbor distances employed in this research, 800 m is the one that appeared the most frequently as the first, second, third, fourth, and fifth most important variable. Out of all neighbor distances, 50 m had the least common appearance as the most important variable, regardless of the position (first, second, third, fourth, or fifth most important variable), with only four appearances. These results are somewhat similar to other studies [18] in which neighborhood distances between 800 and 1000 m were most commonly shown to be the most important overall (53.3% of all five most important variables of all models), especially for morning and evening data.