K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments
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 outdo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8123 |
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| author | Katrina Pedersen Ryan R. Jensen Lucas K. Hall Mitchell C. Cutler Mark K. Transtrum Kent L. Gee Shane V. Lympany |
| author_facet | Katrina Pedersen Ryan R. Jensen Lucas K. Hall Mitchell C. Cutler Mark K. Transtrum Kent L. Gee Shane V. Lympany |
| author_sort | Katrina Pedersen |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-11T01:21:10Z |
| format | Article |
| id | doaj.art-fdab04343939451ab0b028653bd0ac39 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:21:10Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fdab04343939451ab0b028653bd0ac392023-11-18T18:08:34ZengMDPI AGApplied Sciences2076-34172023-07-011314812310.3390/app13148123K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic EnvironmentsKatrina Pedersen0Ryan R. Jensen1Lucas K. Hall2Mitchell C. Cutler3Mark K. Transtrum4Kent L. Gee5Shane V. Lympany6Department of Physics and Astronomy, Brigham Young University, Provo, UT 84602, USADepartment of Geography, Brigham Young University, Provo, UT 84602, USADepartment of Biology, California State University Bakersfield, Bakersfield, CA 93311, USADepartment of Physics and Astronomy, Brigham Young University, Provo, UT 84602, USADepartment of Physics and Astronomy, Brigham Young University, Provo, UT 84602, USADepartment of Physics and Astronomy, Brigham Young University, Provo, UT 84602, USABlue Ridge Research and Consulting, LLC, Asheville, NC 28801, USAApplying 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).https://www.mdpi.com/2076-3417/13/14/8123k-meansclusteringambient noisesound mappingGIS |
| spellingShingle | Katrina Pedersen Ryan R. Jensen Lucas K. Hall Mitchell C. Cutler Mark K. Transtrum Kent L. Gee Shane V. Lympany K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments Applied Sciences k-means clustering ambient noise sound mapping GIS |
| title | K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments |
| title_full | K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments |
| title_fullStr | K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments |
| title_full_unstemmed | K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments |
| title_short | K-Means Clustering of 51 Geospatial Layers Identified for Use in Continental-Scale Modeling of Outdoor Acoustic Environments |
| title_sort | k means clustering of 51 geospatial layers identified for use in continental scale modeling of outdoor acoustic environments |
| topic | k-means clustering ambient noise sound mapping GIS |
| url | https://www.mdpi.com/2076-3417/13/14/8123 |
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