文献类型：期刊文献

英文题名：Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data

作者：Zhu, Likai[1];Guo, Yuanyuan[1];Zhang, Chi[1];Meng, Jijun[2];Ju, Lei[1];Zhang, Yuansuo[3];Tang, Wenxue[1]

第一作者：Zhu, Likai

通讯作者：Zhu, LK[1]

机构：[1]Linyi Univ, Coll Resources & Environm, Shandong Prov Key Lab Water & Soil Conservat & En, Linyi 276000, Shandong, Peoples R China;[2]Peking Univ, Coll Urban & Environm Sci, Minist Educ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China;[3]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China

第一机构：Linyi Univ, Coll Resources & Environm, Shandong Prov Key Lab Water & Soil Conservat & En, Linyi 276000, Shandong, Peoples R China

通讯机构：[1]corresponding author), Linyi Univ, Coll Resources & Environm, Shandong Prov Key Lab Water & Soil Conservat & En, Linyi 276000, Shandong, Peoples R China.

年份：2020

卷号：12

期号：24

起止页码：1-25

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20205109623620);Scopus(收录号：2-s2.0-85097582300);WOS:【SSCI(收录号:WOS:000603254200001)，SCI-EXPANDED(收录号:WOS:000603254200001)】；

基金：This research was funded by the National Natural Science Foundation of China (grant no. 42001373, 41701220 and 41871074), and the Shandong Provincial Natural Science Foundation, China (grant no. ZR2019BD040). Chi Zhang is supported by the Taishan Scholars Program of Shandong, China (grant no. ts201712071). Yuansuo Zhang is supported by the Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (grant no. CIT&TCD20180326).

语种：英文

外文关键词：livability; livability assessment; big geospatial data; remote sensing; Linyi city

摘要：With rapid urbanization, retrieving livability information of human settlements in time is essential for urban planning and governance. However, livability assessments are often limited by data availability and data update cycle, and this problem is more serious when making an assessment at finer spatial scales (e.g., community level). Here we aim to develop a reliable and dynamic model for community-level livability assessment taking Linyi city in Shandong Province, China as a case study. First, we constructed a hierarchical index system for livability assessment, and derived data for each index and community from remotely sensed data or Internet-based geospatial data. Next, we calculated the livability scores for all communities and assessed their uncertainties using Monte Carlo simulations. The results showed that the mean livability score of all communities was 59. The old urban and newly developed districts of our study area had the best livability, and got a livability score of 62 and 58 respectively, while industrial districts had the poorest conditions with an average livability score of 48. Results by dimension showed that the old urban district had better conditions of living amenity and travel convenience, but poorer conditions of environmental health and comfort. The newly developed districts were the opposite. We conclude that our model is effective and extendible for rapidly assessing community-level livability, which provides detailed and useful information of human settlements for sustainable urban planning and governance.