Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data
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 sp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/24/4026 |
| _version_ | 1827700497367171072 |
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| author | Likai Zhu Yuanyuan Guo Chi Zhang Jijun Meng Lei Ju Yuansuo Zhang Wenxue Tang |
| author_facet | Likai Zhu Yuanyuan Guo Chi Zhang Jijun Meng Lei Ju Yuansuo Zhang Wenxue Tang |
| author_sort | Likai Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:13:54Z |
| format | Article |
| id | doaj.art-b043a79c8aaf4a6688f286938157ca04 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:13:54Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b043a79c8aaf4a6688f286938157ca042023-11-21T00:00:28ZengMDPI AGRemote Sensing2072-42922020-12-011224402610.3390/rs12244026Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial DataLikai Zhu0Yuanyuan Guo1Chi Zhang2Jijun Meng3Lei Ju4Yuansuo Zhang5Wenxue Tang6Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, ChinaShandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, ChinaShandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, ChinaKey Laboratory of Earth Surface Processes of Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaShandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, ChinaCollege of Applied Arts and Science, Beijing Union University, Beijing 100191, ChinaShandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, ChinaWith 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.https://www.mdpi.com/2072-4292/12/24/4026livabilitylivability assessmentbig geospatial dataremote sensingLinyi city |
| spellingShingle | Likai Zhu Yuanyuan Guo Chi Zhang Jijun Meng Lei Ju Yuansuo Zhang Wenxue Tang Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data Remote Sensing livability livability assessment big geospatial data remote sensing Linyi city |
| title | Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data |
| title_full | Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data |
| title_fullStr | Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data |
| title_full_unstemmed | Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data |
| title_short | Assessing Community-Level Livability Using Combined Remote Sensing and Internet-Based Big Geospatial Data |
| title_sort | assessing community level livability using combined remote sensing and internet based big geospatial data |
| topic | livability livability assessment big geospatial data remote sensing Linyi city |
| url | https://www.mdpi.com/2072-4292/12/24/4026 |
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