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Article

Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity

1
Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
2
The Rural Development Academy, Zhejiang University, Hangzhou 310058, China
3
Shandong (Linyi) Institute of Modern Agriculture, Zhejiang University, Linyi 276000, China
4
Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China
5
Territorial Consolidation Center in Zhejiang Province, Department of Natural Resources of Zhejiang Province, Hangzhou 310007, China
6
Department of Land Resources Management, Zhejiang Gongshang University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(21), 14166; https://doi.org/10.3390/ijerph192114166
Submission received: 14 September 2022 / Revised: 21 October 2022 / Accepted: 25 October 2022 / Published: 29 October 2022
(This article belongs to the Section Health-Related Quality of Life and Well-Being)

Abstract

Evaluating and revealing the spatial differentiations of quality of rural life (QRL) is the basis for formulating rural revitalization planning to promote rural transformation and achieve common prosperity. Taking the Lin’an District of Hangzhou city in China, an economically developed mountainous area, as an example, this study explored the connotation of QRL from the perspective of common prosperity and constructed a QRL evaluation framework involving living, employment, consumption, and leisure aspects. Then, based on multi-sourced data of 270 administrative villages as the assessment unit, we revealed the spatial patterns of QRL and proposed optimization paths to improving QRL. The results showed that (1) differences in the spatial distribution of quality of rural living, employment, consumption, and leisure of Lin’an District were significant, presenting stepped, block clustering, irregularity, and scattered patterns, respectively. (2) The overall QRL was mainly at a low level, clustered spatially, distributed in a strip pattern, and with obvious road directionality. (3) Based on the evaluation results of QRL, we divided the 270 administrative villages into six types of improvement: livability, employment, consumption, leisure, and balanced and lagged development types. This study could provide a scientific cognitive basis for the improvement of QRL and a useful reference for rural revitalization in China.
Keywords: quality of rural life (QRL); common prosperity; improvement path; big data; Lin’an District quality of rural life (QRL); common prosperity; improvement path; big data; Lin’an District

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MDPI and ACS Style

Zhang, J.; Huang, B.; Chen, X.; Zhu, C.; Gan, M. Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity. Int. J. Environ. Res. Public Health 2022, 19, 14166. https://doi.org/10.3390/ijerph192114166

AMA Style

Zhang J, Huang B, Chen X, Zhu C, Gan M. Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity. International Journal of Environmental Research and Public Health. 2022; 19(21):14166. https://doi.org/10.3390/ijerph192114166

Chicago/Turabian Style

Zhang, Jing, Bingbing Huang, Xinming Chen, Congmou Zhu, and Muye Gan. 2022. "Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity" International Journal of Environmental Research and Public Health 19, no. 21: 14166. https://doi.org/10.3390/ijerph192114166

APA Style

Zhang, J., Huang, B., Chen, X., Zhu, C., & Gan, M. (2022). Multidimensional Evaluation of the Quality of Rural Life Using Big Data from the Perspective of Common Prosperity. International Journal of Environmental Research and Public Health, 19(21), 14166. https://doi.org/10.3390/ijerph192114166

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