ArticlePloS one2024
Exploring the spatiotemporal structure and driving mechanism of digital village construction in China based on social network analysis and Geodetector.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
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Abstract
Clarifying the spatiotemporal structure and driving mechanism of China's digital village construction (DVC) is imperative for ameliorating regional disparities and fostering the holistic progression of DVC in China. This study assesses the index of DVC in 30 Chinese provinces from 2011 to 2021 using the Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) and dynamic GRA. It analyzes the spatiotemporal structure of DVC with kernel density, trend surface, and social network analysis techniques. Additionally, it employs Geodetector to elucidate the driving mechanism behind spatial differentiation in China's digital village development network. The results indicate that: (1) Although the index of DVC in China from 2011 to 2021 has shown progressive enhancement, the average DVC index for all regions throughout the years surveyed stands at 0.457, which means that the DVC in China is still at an early stage. (2) The overall network structure analysis suggests that the number of ties in China's DVC spatial correlation network grew slowly but still falls significantly short of the ideal number. Additionally, there is an increase in the network density of China's DVC over the years, providing strong evidence of spatial spillover effects within the network. (3) The block roles of the central and western regions are main inflow and bidirectional spillover while the block roles of the eastern region are agent and main outflow. (4) The main driving factors of DVC in China are investment in information infrastructure and fiscal expenditure on education. Bivariate enhancement effect and nonlinear enhancement were found to exist in all interactions of indicators. These findings offer theoretical insights and practical directives for improving DVC in China and its synergistic effects.
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