An article: Global regionalization of heat environment quality perception based on K-means clustering and Google trends data is published in Sustainable Cities and Society

KIM Yesuel and KIM Youngchul (2023) Global regionalization of heat environment quality perception based on K-means clustering and Google trends data, Sustainable Cities and Society, 96: 104710, September. https://doi.org/10.1016/j.scs.2023.104710 

This study performs perception-based regionalization research of the thermal environment using Google Trends search query volume data. Global Google Trends data for 12 terms related to the thermal environment were collected from 2016 to 2022 and analyzed by time series and geographical units. To propose a global regionalization map, we used K-means clustering on the geographical Google Trends dataset and determined the optimal number of five clusters using the elbow method.