## 3 Results and Discussion
### Eight-Cluster Model  
Letting \(k\) equal eight, k-means clustering was applied to the 51 geospatial layers for all of the CONUS using a 270-m spatial resolution. Each cluster was assigned a color and a map of the resulting clusters is shown in Figure 2.  
To identify geospatial characteristics of the eight clusters, we calculated the correlation between each cluster and the geospatial layers. Table 1 shows the top three distinct/unique correlated geospatial variables as ranked by the magnitude of the Pearson correlation coefficient. Correlation coefficients for each cluster were calculated using the scaled geospatial layers and Boolean values to denote whether a site resided within the given cluster. Note that for geospatial variables corresponding to multiple layers due to differing areas of analysis (e.g., barren land cover at 200 m and 5 km areas of analysis), only the largest magnitude correlation among all layers is reported. Figure 1: Silhouette (**left**) and elbow (**right**) analyses showing the average silhouette score and average inertia, respectively, as the number of clusters is varied. From Figure 2 and Table 1, we see that Cluster 1 is strongly correlated with water and is therefore prevalent along the coasts and larger bodies of water e.g., the Great Salt Lake. Cluster 2 represents evergreen forests and areas with higher degrees of slope, such as the Sierra Nevada. Cluster 3 is impacted by winter dew point temperatures and wetlands--thus representing relatively humid areas such as those in the Gulf and Atlantic coastal plains. Herbaceous vegetation and low amounts of winter precipitation are represented in Cluster 4 throughout the northern and southern plains. Cluster 5 correlates with both deciduous \begin{table}
\begin{tabular}{c c c c} \hline
**Cluster Number** & **Rank 1** & **Rank 2** & **Rank 3** \\ \hline
1 & Water (0.91) & DistCoast (\(-\)0.44) & DistRoadsAll (0.35) \\
2 & Evergreen (0.78) & Slope (0.46) & TMinSummer (\(-\)0.46) \\
3 & TdewAygWinter (0.50) & Wetlands (0.49) & TMinWinter (0.42) \\
4 & Herbaceous (0.88) & PPTWinter (\(-\)0.28) & DistAirPHigh (0.28) \\
5 & Deciduous (0.84) & MixedForest (0.29) & PPTSummer (0.27) \\
6 & Cultivated (0.89) & Shrubland (\(-\)0.32) & DistRailroads (\(-\)0.29) \\
7 & Developed (0.76) & RddMajor (0.70) & RddAll (0.67) \\
8 & Shrubland (0.90) & PPTSummer (\(-\)0.49) & TdewAygSummer (\(-\)0.44) \\ \hline \end{tabular}
\end{table} 
Table 1: Top three distinct/unique correlated geospatial variables ranked by magnitude for each cluster. Figure 2: Continental United States (CONUS) cluster assignments after clustering 51 geospatial features with a 270-m spatial resolution. and mixed forest environments while Cluster 6, which includes central California and much of the Corn Belt, is most strongly correlated with cultivated (crop) land. Cluster 7 is heavily influenced by developed or urban areas. Finally, Cluster 8 is characteristic of shrubland with low summer precipitation and low summer dew point average, such as the Great Basin Desert. Seven of the eight clusters' most impactful variables are land cover-related and five of those seven are strongly correlated with vegetation. This indicates that land cover (especially vegetation) may be the most important factor when differentiating between the clusters. However, other variables, such as dew point temperature and precipitation are also important in determining cluster assignments (Table 1). In particular, the only cluster with a non-land cover variable ranked as the most correlated variable was Cluster 3 (winter dew point average temperature ranked only slightly higher than wetlands land cover). Viewing zoomed-in maps can allow for further insights and understanding of the clustering model. Figure 3 shows a zoomed-in cluster map of the Great Lakes region and Northeast. The upper Midwest and northeastern United States are dominated by Clusters 5, 6, and 7. Cluster 5 is heavily impacted by deciduous forest and Cluster 6 is mostly influenced by cultivated (crop) land.