###### Abstract  
Applying machine learning methods to geographic data provides insights into spatial patterns in the data as well as assists in interpreting and describing environments. This paper investigates the results of k-means clustering applied to 51 geospatial layers, selected and scaled for a model of outdoor acoustic environments, in the continental United States. Silhouette and elbow analyses were performed to identify an appropriate number of clusters (eight). Cluster maps are shown and the clusters are described, using correlations between the geospatial layers and clusters to identify distinguishing characteristics for each cluster. A subclustering analysis is presented in which each of the original eight clusters is further divided into two clusters. Because the clustering analysis used geospatial layers relevant to modeling outdoor acoustics, the geospatially distinct environments corresponding to the clusters may aid in characterizing acoustically distinct environments. Therefore, the clustering analysis can guide data collection for the problem of modeling outdoor acoustic environments by identifying poorly sampled regions of the feature space (i.e., clusters which are not well-represented in the training data). k-means; clustering; ambient noise; sound mapping; GIS +
Footnote †: journal: Medicalon overall sound levels. Organisms, on the other hand, may have an ecological effect on shaping and contributing to the acoustic landscape. Many invertebrate and vertebrate species use extensive acoustic displays that periodically influence outdoor sound levels [8; 9]. In contrast, vegetation may contribute more to overall outdoor sound levels than animals; not by generating sound, but rather by attenuating or diffusing sound [10]. Geography, geographic data, and geographic techniques have been used in a variety of applications to study both biotic and abiotic components of environments. These applications include using Geographic Information Systems (GIS) to create, store, analyze, and visualize data in a geographic context. GIS lends itself to a variety of domains and aids in interpreting environments. Indeed, GIS has been used by urban land planners to quantify aesthetic values of landscapes [11]. Additionally, GIS has been used in general landscape analysis (e.g., [12]) and to model specific landscape features, such as the visual landscapes in the arid northwest Egypt [11]. Machine learning techniques--both supervised and unsupervised--have been implemented within GIS to map and model various landscape features. Examples of studies which utilize both supervised machine learning and GIS include the use of a decision tree to identify brown bear habitat [13] and the application of four supervised machine learning algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) to model maize aboveground biomass [14]. Unsupervised machine learning methods on the other hand have been used to study hydrologic catchments in Turkey via k-means clustering [15] and prepare mineral prospectivity maps in central Iran using self-organizing maps and fuzzy c-means [16]. Additionally, supervised and unsupervised machine learning techniques have been used in combination to study urban evolution in Athens, Greece using fuzzy clustering and neural networks [17], classify wetlands in Estonia using k-means clustering and support vector machines [18], and more (e.g., [19; 20]). The ability to adequately study and model ambient sound across large geographic regions is dependent on GIS. Sound mapping is becoming so prevalent in GIS that an open-source sound mapping toolbox has been created for outdoor sound propagation modeling in Esri's ArcGIS software [21]. Some studies have combined GIS and geographic data with land use regression models to estimate traffic noise [22; 23] and overall environmental noise [24]. GIS and supervised machine learning models have also been used over continental scales to predict average outdoor sound levels [25; 26].