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.
Funding: This study was supported by the National Natural Science Foundation of China (Grant No. 42261055) and the Guangxi Science and Technology Major Special Project (Guike AA23062039-1), awarded to Jie Li. Jie Li was responsible for funding acquisition, project administration, and supervision of the study. None of the authors received a salary from any of the funders. The authors further declare that they have no competing interests.
Owing to their entrenched duality, urban and rural areas typically show pronounced disparity in terms of economic development, infrastructure, and public services [1–4], which has not only driven imbalances in economic growth but also spawned a range of secondary social problems [5–7].
Urban–rural integration, as an essential approach to promoting coordinated regional development, has become a sustained focus of scholarly research. In most studies, researchers have primarily adopted economic and social perspectives to construct multidimensional indicator systems to assess the level of integration. For example, some have examined the spatial configuration of urban and rural areas based on the distribution of population and enterprises [8], while others have analyzed the driving effects of the digital economy on urban–rural integration [9]. Beyond that, in their evaluation frameworks, several scholars have incorporated indicators related to quality of life [10], poverty alleviation outcomes [11], and overall regional development [12]. Although those contributions have provided valuable insights into the socioeconomic foundations of urban–rural integration, they have also often overlooked the profound influence of geographical characteristics on such integration.
A well-developed road network is not only a vital foundation for smooth socioeconomic connectivity but also a key indicator for evaluating the level of regional development. In turn, some scholars have sought to assess urban–rural integration from geographical perspectives. For instance, Avery et al. [13] have emphasized the importance of road network connectivity in enhancing urban–rural integration in order to achieve sustainable regional development. Lu et al. [14], meanwhile, have employed metrics of road network density to evaluate regional transportation connectivity and its implications for urban–rural spatial structure. Still, other scholars have examined the evolution of road networks and their role in shaping spatial linkages between urban and rural areas and highlighted the significance of transport infrastructure in advancing urban–rural integration [15–17]. Although approaches to date have advanced the spatial assessment of such integration, many continue to rely on simplified representations of spatial relationships. That limitation has reduced the accuracy of urban–rural integration assessments and impeded the quantification of practical challenges that geographical characteristic pose for urban–rural integration.
Distance, as a fundamental variable in geography [18], encompasses not only static spatial measures, such as Euclidean distance, which indicate the proximity between locations [19], but also travel-time-based accessibility, which more directly reflects the ease of inter-regional connectivity [20]. On that basis, many studies have focused on urban aspects to explore how transportation accessibility supports regional economic development and spatial organization. For example, the development of so-called one-hour economic circles has been shown to strengthen the influence of cities on surrounding areas by reducing travel times [21–23]. Transportation planning has also used improved transportation accessibility to promote the clustering of firms and optimize spatial layouts [24–27], while GIS-based measures have been applied to assess transport efficiency and regional development [28].
By contrast, rural areas are often widely dispersed across rugged, underdeveloped mountainous regions—examples include the Alps, Himalayas, Andes, Atlas Mountains, and Rocky Mountains—where obstacles to construction and low economic returns have made transportation infrastructure relatively weak and consequently exacerbated regional poverty and underdevelopment. Research focusing on rural aspects has thus primarily examined how improving transportation can promote rural development, including in terms of poverty reduction [29–31], gender disparity [32], public service delivery [33], and emergency response capacity [34]. Although a subset of studies emphasizing road network planning and infrastructure improvements have highlighted the importance of transportation accessibility from different aspects [35–37], most have examined only urban or only rural development. Moreover, few have established a direct connection between transportation accessibility and urban–rural integration or produced measures to characterize how spatial heterogeneity in transportation accessibility reflects disparity in levels of integration between urban and rural areas. That limitation poses problems for accurately assessing the spatial characteristics and heterogeneity of urban–rural integration. To address that gap in the literature, in our study we considered the topographic conditions in measuring urban–rural transportation accessibility (URTA) as a potential indicator for characterizing the degree of urban–rural integration.
Our study focused on the Chinese province of Yunnan, a region marked by rugged mountainous terrain with widely dispersed village settlements (VSs). With geographic characteristics representative of many mountainous areas worldwide, Yunnan is an ideal case for examining URTA and urban–rural integration. Using data from 2015 and 2023, we developed an URTA model to comprehensively measure transport accessibility by incorporating geographic location, topography, modes of transportation, and road conditions. The model calculates the travel time cost from each VS to its county administrative center in order to assess the level of transportation accessibility across all VS in the province. By comparing changes in URTA and their spatial distribution between 2015 and 2023, the model not only visually presents the difficulty of traveling from each VS to its county administrative center at the local scale but also characterizes spatial autocorrelation and heterogeneity among regions at the provincial scale, for a novel geographic indicator that can help to assess the level of urban–rural integration.