The literature on urban–rural integration lacks not only a geographic indicator that incorporates geographical characteristics (e.g., location, topography, and road infrastructure) to characterize urban–rural integration but also methods of measuring the access costs of planning rural transportation infrastructure and individual village settlements (VSs). In response, our urban–rural transportation accessibility (URTA) model, using VSs as the unit of transportation accessibility, offers a new indicator to guide local transportation infrastructure planning and to capture the provincial-level heterogeneity of urban–rural access costs based on geographical characteristics. The model is constructed with data from over 140,000 VSs in the Chinese province of Yunnan and multilevel road network data for Yunnan from 2015 and 2023 based on GIS analysis. Moreover, because mountainous VSs may lack paved roads to connect to the road network, our model integrates results from the minimum cumulative resistance model to simulate least cost paths through the network. Using origin–destination analysis, we also calculated the travel time from each VS to its county center as a measure of URTA reflecting urban–rural integration. Among our findings, significant differences in travel time and path length between developed and impoverished counties reflect disparity in urban–rural integration. Longer travel times indicate less integration, and though times were consistently under 2.5 h in Guandu District, they ranged from 2 to more than 10 h in Gongshan County. Furthermore, on average, reaching county centers from the VSs took 2.13 h in 2015 and 1.46 h in 2023. Although URTA expanded in central Yunnan and improved considerably in the northwest, the southwest showed little change, which highlights significant disparity in urban–rural integration. Those results suggest that our method and indicator, by elucidating urban–rural integration in regions with high geographic spatial heterogeneity, can guide transportation planning in mountainous rural areas worldwide.
Liu et al. (Thu,) studied this question.