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

Int J Environ Res Public Health. 2022 Oct 29;19(21):14166. doi: 10.3390/ijerph192114166.

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: Lin’an District; big data; common prosperity; improvement path; quality of rural life (QRL).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Big Data*
  • China
  • Cities
  • Humans
  • Rural Population*

Grants and funding

This research was funded by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, grant number KF-2020-05-073; National Natural Science Foundation of China, grant number 41971236.