Urban–rural integration (URI) is essential to achieving sustainable development. However, the rural areas surrounding large cities typically have a large scale and significant differences in development conditions. It is necessary to formulate rural development policies by category to better promote the integrated development between urban and rural areas, stimulate rural vitality, and create more significant opportunities for rural development. This study constructs an evaluation system for rural areas under URI, using the Xi’an metropolitan area as a case study. A clustering algorithm enhanced by the random forest (RF)–principal component analysis (PCA)–partitioning around medoids (PAM) method is applied to evaluate rural integration comprehensively. Key findings in this study include the following: (i) URI should be decoupled from administrative divisions, considering the complex impacts of multi-town functional spillover; (ii) ecological environment, economic development, public service allocation, and construction land supply are key factors influencing URI; (iii) the overall URI index in the Xi’an metropolitan area presents a “high in the center, low in the east and west” pattern. The rural areas with high URI index are around Xi’an and Xianyang, while other cities show insufficient communication with neighboring villages; (iv) rural areas can be categorized into four types of integration: ecological, ecological–economic, ecological–social–spatial, and ecological–economic–social–spatial, which exhibit an outward expansion of layers and extension along the east–west axis in the spatial structure of integration. Finally, differential development policies and suggestions for promoting urban–rural integration are put forward because of the different types of rural villages. This paper provides a framework for formulating rural development policies, significantly deepening urban–rural integration.

In China, the metropolitan area is an important spatial carrier to promote high-quality urbanization as well as the coordinated development of large, medium, and small cities and regions. The environment is an important factor affecting the quality of life among urban populations, and anthropogenic activity significantly impacts the natural and built environments. Considering the Xi’an metropolitan area as a case study, we selected three subsystems across three cross-sections in 2000, 2010, and 2020: population agglomeration, natural ecological development, and urban environmental development. We used the entropy weight method, coupling the coordination model and correlation analysis to analyze the spatiotemporal patterns, coupling coordination relationship, and related development factors of the “population–environment” system. The results showed that the development of the “population–environment” system in Xi’an exhibits stratification and differentiation between the center, core, and periphery circles. The coupling coordination level of “population–environment” is slowly improving, while most districts and counties have been on the verge of incoordination. Furthermore, the coupling coordination relationship of each district and county could be further classified into five types. Accordingly, this paper discusses corresponding and differentiated strategies to promote the healthy and sustainable development of regional populations and the environment for districts and counties.

The external relations of small towns are an important dimension in the regional urban system. However, the “metropolitan bias” in existing studies results in a lack of empirical verification of their characteristics, hindering effective regional policymaking. Applying Central Flow Theory (CFT), mobile phone data, and a multiscale geographically weighted regression (MGWR) model, this study investigates the spatially non-stationary associations between built environment factors and the “city-ness” and “town-ness” of small towns in the Yangtze River Delta. The results show: (1) Enterprise density in metropolitan shadow areas is positively associated with cross-city jobs–housing separation; in peripheral areas, both enterprise density and housing prices exhibit a strong correlation with intra-municipal jobs–housing separation. (2) Middle schools consistently correlate with localized intra-municipal flows, suggesting a plausible spatial anchoring role; around metropolises, medical and commercial facilities link to recreational flows and commuting town-ness, while in distal small towns, medical facilities coincide with intratown jobs–housing balance, and commercial facilities correlate with localized consumption and cross-town employment mobility. (3) Higher road network density corresponds to a shrinking commuting radius near metropolises and intra-municipal intertown interconnection in distal towns, rather than mere external relation channels. This study empirically supports CFT at the small-town scale, explores plausible mechanisms, and informs differentiated planning strategies.