Analyzing Rural Community Growth and Shrinkage: Insights from Household Electricity Data in Xinxing County, China

Currently, rural areas in China are facing numerous challenges in the process of modernization, while macro-scale research often overlooks the complexity of internal changes within these rural communities. This study aims to examine the dynamic patterns of growth and shrinkage in rural areas from a village-level perspective and to explore the factors driving these changes. By analyzing household electricity consumption data from 174 villages in Xinxing County, Guangdong Province, this paper employs the K-Means clustering algorithm to categorize villages into agglomerated, stable, and shrinking types, and constructs a regression model to investigate the impact of multiple dimensions, including geographic environment, public service provision, infrastructure construction, and industrial development level, on rural development. The findings reveal significant spatial differentiation in rural development, with agglomerated villages primarily located around county towns and townships, while shrinking villages are mainly situated in border areas. Among the factors influencing rural community development, the importance of healthcare services, internet infrastructure, and industrial development is increasingly recognized. The innovation of this paper lies in proposing a method for measuring rural development types based on household electricity consumption data, which uncovers differentiation patterns in rural areas that are difficult to observe in macro-scale studies. These results provide a new perspective for understanding the subtle differences in rural development and offer empirical support for formulating policies aimed at sustainable development of rural communities.