## 4 Discussion and Conclusions
However, studies demonstrating the distance-decay effect of natural features in mitigating urban temperatures [40] suggest that additional research is required to provide further valuable insight. The use of the RF algorithm in predicting temperature across all urban areas resulted in significant R\({}^{2}\) coefficients, where 83% of the models exhibited values higher than 0.95, and 62.5% showed values over 0.97, all being statistically significant (\(p\)-value \(<\) 0.001). This indicates that the approach of creating detailed geospatial temperature patterns byincorporating a diverse range of morphometric and natural features as independent variables (Canopy Cover, Canopy Density Metric, Building Height, Building Volume, and Normalized Difference Vegetation Index) in the urban environment proved to be effective. The highly statistically significant predictive power of the multiple developed models also offered valuable insights on the impacts that man-made and natural features (the foundation to derive most independent variables) have in contributing or preventing urban heat across the state of Iowa. Continued investment in understanding urban temperature patterns will facilitate the development of preventive measures to be taken at different scales, as well as mitigative actions that can potentially lead to reducing deleterious public health effects and significantly enhancing the overall quality of life for both individuals and the community as a whole. The results of the modeling efforts provide highly detailed spatial data and maps that could be used in a variety of public health mitigation efforts (e.g., location of cooling centers) and urban planning efforts. Urban planners, landscape architects, zoning specialists, and public health experts are just a few of the professionals that can potentially make use of the content provided in this study to develop ways of mitigating urban heat (e.g., by implementing highly vegetated areas) and establishing or improving environmentally equitable housing to prevent and respond to heat-related health issues. Even though the goal of this study was to collect air temperature data during hot days in different study areas, temperature patterns for urban areas during cold days are seldom explored by existing literature. While extensive research has been conducted on temperature patterns in urban environments, the focus has predominantly been on urban heat during extremely hot weather conditions. The lack of research in areas where temperatures frequently drop to sub-zero temperatures during winter warrants the need for additional studies. While the derived spatial independent variables and neighborhood distances used in this study produced strong results, other variables and distances could be explored in future studies. For example, variables that can portray the heterogeneity of the urban fabric and meteorological conditions (e.g., sky view factor, humidity, the proportion of impervious surfaces, albedo of built-up structures) or those that indicate various heat sources such as heat released by buildings or motor vehicles [39] could be considered. Inversely, new studies can instead focus on a limited number of variables that consistently presented higher statistic influence in the air temperature models (e.g., NDVI at certain neighbor distances). Conceptualization of the study: J.D., J.T.D. and B.L. ; methodology: all authors contributed to the development of the methodology; software: [PERSON] developed sensor Python scripts and post-processing temperature data Python scripts while [PERSON], with assistance from [PERSON], was primarily responsible for the development of automated GIS processing (Python, arcpy) scripts; validation, [PERSON] was primarily responsible for spatial modeling and testing with input from all authors.