## 4 Conclusions  
We applied k-means clustering to a set of 51 geospatial layers, selected because of their relevance to geospatial acoustic modeling in the continental United States, and custom-scaled for such models [26]. The resulting 8-cluster model identified eight geospatially distinct regions in the continental United States, which differ by land cover, vegetation, anthropogenic activity, climate, etc. Land cover and largescale patterns of vegetation are influential in the clustering, with seven of the eight clusters most strongly correlated with a distinct type of land cover, five of which are vegetation communities. Importantly, each cluster corresponds to a different type of land cover and/or climate. Because the 51 geospatial layers used in clustering are relevant to geospatial acoustic modeling, the geospatially distinct environments corresponding to each cluster may aid in determining acoustically distinct environments. Indeed, it is reasonable to suppose that differences in land cover, climate, anthropogenic activity, etc. likely result in different acoustic sources and propagation effects (i.e., different acoustic environments). Characterizing acoustic environments has potential applications in land use planning (e.g., urban planning and wildlife policy and management). More broadly, characterization of acoustic environments is an important aspect of describing and classifying geospatial environments and landscapes. These results can be applied to continental-scale modeling of outdoor acoustic environments by identifying underrepresented clusters in the training data set for targeted data collection. Figure 6: Distribution of subclusters within (a) the training data and (b) the CONUS. K-means clustering was implemented in this study, in part, because of its simplicity, interpretability, and low computational costs. These characteristics are all desirable in data analysis, and k-means is available in multiple coding, software, and platform settings. Further, k-means is one of the most used clustering algorithms in data analysis [31] and produces human-interpretable clusters on our data set, which can help characterize acoustic environments and direct data collection efforts for continental-scale models of outdoor sound levels. There may be value in exploring additional clustering algorithms, such as DBSCAN, Gaussian mixture models, and mean-shift clustering, or more advanced methods of determining the number of clusters [39; 40; 41]. However, none of these are computationally trivial given the size of the dataset, and the results of k-means clustering (and subclustering) provide sufficient information to guide current acoustic data collection methods. Therefore, future research will focus on applying these clustering results for targeted data collection for improving continental-scale models of outdoor sound levels. Conceptualization, R.R.J., L.K.H. and K.P. ; methodology, K.P., M.K.T. and M.C.C. ; software, K.P. ; validation, K.P. and M.C.C. ; formal analysis, K.P. and M.C.C. ; investigation, K.P. and M.C.C. ; data curation, S.V.L. and K.P. ; writing--original draft preparation, K.P., R.R.J. and L.K.H. ; writing--review and editing, M.K.T., K.L.G., S.V.L. and M.C.C. ; visualization, K.P. and M.C.C. ; funding acquisition, M.K.T., K.L.G. and S.V.L. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the College of Physical and Mathematical Sciences at Brigham Young University and a U.S. Army Small Business Innovation Research (SBIR) contract to Blue Ridge Research and Consulting, LLC, contract number W911W6-18-C-0028. Geospatial data and partial acoustic data are available at [[https://irma.nps.gov/DataStore/Reference/Profile/2217356](https://irma.nps.gov/DataStore/Reference/Profile/2217356)]([https://irma.nps.gov/DataStore/Reference/Profile/2217356](https://irma.nps.gov/DataStore/Reference/Profile/2217356)), accessed on 3 June 2020, or by request from the Natural Sounds and Night Skies Division of the National Park Service.