Exploring the spatial association between the distribution of temperature and urban morphology with green view index
學年 112
學期 2
出版（發表）日期 2024-05-14
作品名稱 Exploring the spatial association between the distribution of temperature and urban morphology with green view index
作品名稱（其他語言）
著者 Ta-Chien Chan; Ping-Hsien Lee; Yu-Ting Lee; Jia-Hong Tang
單位
出版者
著錄名稱、卷期、頁數 PLOS ONE 19(5) , p. e0301921
摘要 Urban heat islands will occur if city neighborhoods contain insufficient green spaces to create a comfortable environment, and residents' health will be adversely affected. Current satellite imagery can only effectively identify large-scale green spaces and cannot capture street trees or potted plants within three-dimensional building spaces. In this study, we used a deep convolutional neural network semantic segmentation model on Google Street View to extract environmental features at the neighborhood level in Taipei City, Taiwan, including the green vegetation index (GVI), building view factor, and sky view factor. Monthly temperature data from 2018 to 2021 with a 0.01° spatial resolution were used. We applied a linear mixed-effects model and geographically weighted regression to explore the association between pedestrian-level green spaces and ambient temperature, controlling for seasons, land use information, and traffic volume. Their results indicated that a higher GVI was significantly associated with lower ambient temperatures and temperature differences. Locations with higher traffic flows or specific land uses, such as religious or governmental, are associated with higher ambient temperatures. In conclusion, the GVI from street-view imagery at the community level can improve the understanding of urban green spaces and evaluate their effects in association with other social and environmental indicators.
關鍵字
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128421 )

SDGS 永續城市與社區,氣候行動,夥伴關係,良好健康和福祉,優質教育