Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data
Urban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing the direct effects of urban vitality intensity (VI) and its influencing factors, while paying less attention to the urban v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/6/1056 |
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| author | Zhiran Zhang Jiping Liu Yangyang Zhao Qing Zhou Lijun Song Shenghua Xu |
| author_facet | Zhiran Zhang Jiping Liu Yangyang Zhao Qing Zhou Lijun Song Shenghua Xu |
| author_sort | Zhiran Zhang |
| collection | DOAJ |
| description | Urban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing the direct effects of urban vitality intensity (VI) and its influencing factors, while paying less attention to the urban vitality diversity (VD) and its indirect impact mechanisms. Supported by multisource remote sensing data, this study establishes a five-dimensional urban vitality evaluation system and employs the Partial Least Squares Structural Equation Model (PLS-SEM) to quantify direct and indirect interrelationships between these multidimensional factors and VI/VD. The findings are as follows: (1) Spatial divergence between VI and VD: VI exhibited stronger clustering (I = 1.12), predominantly aggregating in central urban areas, whereas VD demonstrated moderate autocorrelation (I = 0.45) concentrated in mixed-use central or suburban zones. (2) Drivers of vitality intensity: VI are strongly associated with commercial density (β = 0.344) and transportation accessibility (β = 0.253), but negatively correlated with natural environment quality (r = −0.166). (3) Mechanisms of vitality diversity: VD is closely linked to public service (β = 0.228). This research provides valuable insights for city development and decision-making, particularly in strengthening urban vitality and optimizing urban functional layouts. |
| format | Article |
| id | doaj-art-2c497def4de541628dfdce96ee5bf0cc |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2c497def4de541628dfdce96ee5bf0cc2025-08-20T02:43:02ZengMDPI AGRemote Sensing2072-42922025-03-01176105610.3390/rs17061056Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing DataZhiran Zhang0Jiping Liu1Yangyang Zhao2Qing Zhou3Lijun Song4Shenghua Xu5School of Earth Sciences and Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaNo. 2 Gas Production Plant of PetroChina Changqing Oilfield Company, Xi’an 710018, ChinaSchool of Earth Sciences and Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaUrban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing the direct effects of urban vitality intensity (VI) and its influencing factors, while paying less attention to the urban vitality diversity (VD) and its indirect impact mechanisms. Supported by multisource remote sensing data, this study establishes a five-dimensional urban vitality evaluation system and employs the Partial Least Squares Structural Equation Model (PLS-SEM) to quantify direct and indirect interrelationships between these multidimensional factors and VI/VD. The findings are as follows: (1) Spatial divergence between VI and VD: VI exhibited stronger clustering (I = 1.12), predominantly aggregating in central urban areas, whereas VD demonstrated moderate autocorrelation (I = 0.45) concentrated in mixed-use central or suburban zones. (2) Drivers of vitality intensity: VI are strongly associated with commercial density (β = 0.344) and transportation accessibility (β = 0.253), but negatively correlated with natural environment quality (r = −0.166). (3) Mechanisms of vitality diversity: VD is closely linked to public service (β = 0.228). This research provides valuable insights for city development and decision-making, particularly in strengthening urban vitality and optimizing urban functional layouts.https://www.mdpi.com/2072-4292/17/6/1056city vitalitydiversitycommunityPLS-SEM |
| spellingShingle | Zhiran Zhang Jiping Liu Yangyang Zhao Qing Zhou Lijun Song Shenghua Xu Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data Remote Sensing city vitality diversity community PLS-SEM |
| title | Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data |
| title_full | Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data |
| title_fullStr | Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data |
| title_full_unstemmed | Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data |
| title_short | Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data |
| title_sort | community level urban vitality intensity and diversity analysis supported by multisource remote sensing data |
| topic | city vitality diversity community PLS-SEM |
| url | https://www.mdpi.com/2072-4292/17/6/1056 |
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