Innovative Research on Rural Housing Renewal and Rehabilitation Models Driven by Rural Electricity Economy

Authors

  • Rong Li School of Information Technology, Nanchang Vocational University, Nanchang, Jiangxi, 330050, China; Hanyang Graduate School of International Studies, Hanyang University, Seoul, 04763, South Korea
  • Chaonan Liu School of Information Technology, Nanchang Vocational University, Nanchang, Jiangxi, 330050, China
  • Bei Li Wuhan Shangguan Information Technology Co., Ltd., Wuhan, Hubei, 430000, China

DOI:

https://doi.org/10.70917/ijcisim-2026-0370

Keywords:

geographically weighted regression, random forest, rural housing reform, spatial influence factors, threshold effect, e-commerce economy

Abstract

Under the background of rapid development of e-commerce economy, rural housing renovation faces new opportunities and challenges. The traditional housing renovation model is difficult to meet the needs of modern rural development, with obvious spatial differentiation characteristics and complex and diverse influencing factors. Exploring the innovative model of housing renovation driven by e-commerce economy is of great significance for promoting rural revitalization, improving rural living environment, and promoting the coordinated development of urban and rural areas. This study adopts a combination of geographically weighted regression (GWR) model and random forest (RF) model to deeply analyze the spatial influencing factors and threshold effects of rural housing renovation driven by e-commerce economy, based on 800 rural housing renovation sample sites in County A. The spatial regression analysis was carried out by constructing the characteristic price model, using ArcMap 10.3 software, and producing partial dependency maps using Python 3.0 software. The results show that: the goodness-of-fit R² of the GWR model reaches 0.8314, which is significantly better than that of the OLS model of 0.7963, and the standard deviation decreases from 0.623 to 0.436; the prediction accuracy of the RF model is as high as 94.1%, and the average absolute error is 996.52 yuan/square meter; the housing location has the greatest influence on the remodeling price, with the contribution of 20.21%, followed by the transportation accessibility (16.32%) and park accessibility (13.63%); spatial autocorrelation analysis shows that the Moran's I value increases from 0.286 to 0.333 in 2021-2024, showing a significant positive correlation clustering characteristics. The study puts forward three countermeasure suggestions to improve the design of housing system, pay attention to the orientation of social opinion, and enhance the economic strength of farmers, which provide theoretical basis and practical guidance for the innovation of rural housing renovation mode.

Downloads

Download data is not yet available.

Downloads

Published

2026-02-17

How to Cite

Rong Li, Chaonan Liu, & Bei Li. (2026). Innovative Research on Rural Housing Renewal and Rehabilitation Models Driven by Rural Electricity Economy. International Journal of Computer Information Systems and Industrial Management Applications, 18, 13. https://doi.org/10.70917/ijcisim-2026-0370

Issue

Section

Original Articles