<b>Mapping urban soundscape patterns using user-generated content: A place model </b><b>approach to acoustic environment perception</b>

This is supporting data for this study. It includes POIs, taxonomy, and the results of text mining.<br><b>Sounscape_Keyword_Total_Frequency.csv</b>: The total frequency of soundscape-related keywords extracted from tweets.<b>Soundscape_Keyword_by_Target_Site.zi</b><b>p</b>: The frequency of soundscape-related keywords by soundscape category.<b>Kmeans_Clustering_Proportion_by_Category</b><b>.csv</b>: The proportion of frequency by soundscape category that was used for implementing k-means clustering analysis.<b>Kmeans_Clustering_MainCategrory</b><b>.csv</b>: The main category of each target site by the results of k-means clustering analysis.<b>Points_Of_Interest</b><b>.csv</b>: POIs of target sites that were used for collecting georeferenced tweets.<b>S1_Soundscape_Taxonomy_by_Sense_Of_Place</b><b>.csv</b>: The soundscape taxonomy for extracting keywords from tweets (S1 Table).