Under the background of urban–rural integration, balanced development between urban and rural areas has been increasingly advocated. Rural population loss (RPL) is not only due to the laws of social and economic development but also the comprehensive action of natural, social, and economic factors. Taking 774 administrative villages in Laiyang County, which is in a hilly region, as our research area, we comprehensively used spatial analysis and geographic detectors to explore the spatial characteristics and driving factors of RPL, which was significantly correlated with rural planning. The research demonstrated that: (1) The rural population in Laiyang County generally had a low level of RPL (1.9%), but each village varied greatly. The village with the greatest RPL had a rate of 56%. The RPL between urban and rural areas, towns and streets, and villages and villages were unbalanced, and rural population flow mainly occurred between urban and rural areas. (2) RPL in Laiyang County was generally low in the central urban area and high in the northern and southern areas. Population loss presents agglomeration globally and high–low agglomeration locally. (3) The distance from village to county, elevation, cultivated land quantity, collective economic income, village area, and ecological service value were the key factors influencing RPL in Laiyang County. When comparing the dominant factors, the interaction between collective income and elevation was the strongest. Exploring the spatial characteristics and influencing factors of RPL provided us with ideas for the classified promotion of rural revitalization, preparation of rural development planning, and promotion of the integrated development of urban and rural areas.

The establishment of a comprehensive framework to identify village development types is crucial to formulate plans for rural development and promote rural revitalization. This study proposed a natural–socioeconomic framework to identify the types of villages based on field survey, statistical data, and multi-source remote sensing images. The framework was constructed by combining the two-dimensional natural suitability/restriction evaluation and the four-dimensional socioeconomic development level evaluation. Then, the modified multiplication-weighted summation method and the coupling coordination degree algorithm were employed to identify the villages’ development types. A total of 774 villages of the Laiyang County, eastern China were used as the study areas to examine the framework. The results demonstrated the following. (1) There were 243,318 and 151 villages with high, moderate, low natural suitability, and 62 villages with natural restrictions; and 158,366 and 250 villages with high, moderate, and low economic development level, respectively. The distribution characteristic of natural evaluation was “high in the southwest and low in the northeast”, and the socioeconomic development level was generally centered on the urban area, which presented a “high–medium–low” circle-layer distribution structure. (2) There were 247 villages with high-level coupling coordination, 464 villages with intermediate coupling coordination, 1 village with low-level coupling coordination, and 62 villages with disordered coupling. (3) Based on the coupling coordination evaluation results, villages in the study area were grouped into five types: urbanization development (31%), construction development (16%), agglomeration linkage development (27%), decrease and improvement development (18%), and relocation and integration development (8%). The framework of villages’ development types identification established in this study can enrich the theory of rural geography, and the applied research results can provide a basis for rural revitalization and development planning.

Under the background of urban-rural integration, the frequent flow of urban and rural elements has promoted the close connection between rural transformation (RT) and construction land transition (CLT). How is CLT related to RT? By taking the plain area Linqing County in China as the study area, basing the study on the RT and CLT coupling framework of relevance theory, building an RT and CLT evaluation index system, using the multi-factor evaluation method for the evaluation of RT and CLT from 2010 to 2018, and using the grey correlation model to measure the RT and CLT coupling relationship, the results showed that the level of RT was from 0.04 to 97.42, and the level of CLT was from 14.89 to 82.47, showing the trends of gradual increase and fluctuating increase, respectively. Taking 2013 and 2016 as the time point, RT could be divided into the initial stage, growth stage I, and growth stage II, corresponding to the initial stage, high growth stage, and stable development stage of CLT. The coupling degree between the two was in the range of 0.6–0.8 and was in the stage of a medium to high coupling degree. The correlation degree between the subsystem of RT and CLT was over 0.65. In the subsystem of CLT, the correlation degrees between quantitative structure transition and RT and efficiency transition and RT were both lower than 0.65, which were relatively low. Controlling the scale of construction land and taking efficiency transition are the effective methods to guide the deep exploration of potential and are the inevitable way to strengthen the relationship between CLT and RT and promote urban-rural integration development.