This study was designed to evaluate the spatial distribution characteristics of 1432 beautiful leisure villages in China using econometric geography and spatial geographic information system analysis methods, such as nearest distance index, K index, and nuclear density. We also used the grid cost weighted distance algorithm to determine the spatial accessibility of beautiful leisure villages and the overall accessibility of county units. In addition, our evaluations determined the spatial differences in county accessibility using exploratory spatial data analysis (ESDA). Our results showed that the spatial distribution of the beautiful leisure villages in China could be best described using the cohesion type classification and that there were large differences in their distribution between provinces and economic regions. The average accessibility time of beautiful leisure villages was 197.24 min with only 57.19% of these commutes being less than 2 h, and only 17.88% being less than 30 min. The area with the longest accessibility time was located on the Qinghai Tibet Plateau, at up to 1510.03 min. The spatial distribution of accessibility showed obvious traffic directivity producing a positive Moran I value for most counties. There was also a significant positive correlation between the accessibility of beautiful leisure villages and their adjacent areas, and clear patterns of hot spots–sub-hot spots–sub-cold spots—cold spots from east to west. The overall service scope of beautiful leisure villages was characterized by west > east > middle, with topography, population, economy, and location acting as the major factors in the spatial distribution of these beautiful leisure villages in China.

As a typical representative of tourism resources, the spatial distribution of A-level scenic spots has a profound impact on the layout of tourism industry. Scenic spot accessibility is also important for the development of tourism. However, the relationship of regional accessibility and spatial distribution of A-level scenic spots are understudied. The study used quantitative geography and geographic information system spatial analysis methods and analyzed the evolution of spatial distribution and regional accessibility of A-level scenic spots in Guangdong Province from 2001 to 2020. The results present the following: 1. Agglomeration distribution is the main distribution type of A-level scenic spots in Guangdong Province, and the spatial distribution is unbalanced. 2. From 2001 to 2020, the spatial distribution of A-level scenic spots in 21 prefecture-level cities of Guangdong Province has gradually developed from "wide gap" to "relatively reasonable." 3. Distribution density of A-level scenic spots in Guangdong Province has evolved into the main core area of high density. 4. Center of the gravity of A-level scenic spots in Guangdong Province developed from east to west during 2002–2007 and moved to the east after 2007. 5. Accessibility between A-level scenic spots and tourist source areas in Guangdong Province is good, with an evident aggregation phenomenon. This study reveals the spatial distribution evolution law and regional accessibility of A-level scenic spots, which is conducive to healthy, sustainable, and stable development of tourism in Guangdong Province.

Characteristic towns are an essential focus for developing urbanization and implementing rural revitalization in China. Studying the spatial distribution characteristics and influencing factors are of great significance for the scientific and rational cultivation and deployment of characteristic towns and promotion of the integrated development of urban and rural areas. The spatial distribution characteristics and accessibility of 142 characteristic towns in Guangdong Province were investigated using Ripley's K function and kernel density estimation. The results show that (1) the characteristic towns in Guangdong Province present the overall spatial distribution characteristics of “the Pearl River Delta region” and the local distribution characteristics of multicore diffusion in “eastern Guangdong” and “western Guangdong”; (2) the spatial distribution of industrial development, cultural tourism type, innovative and creative characteristic towns shows significant agglomeration, whereas the agglomeration characteristics of agricultural service-oriented and commercial and trade circulation-oriented characteristic towns are not significant; (3) population, economy, resources, and location are the main factors affecting the spatial distribution of characteristic towns in Guangdong Province; (4) the overall spatial accessibility of characteristic towns in Guangdong Province is relatively good. However, the accessibility of characteristic towns of industrial development is better than that of other types of characteristic towns.

Rapid urbanization often leads to numerous ecological issues. Balancing development through urbanization and ecological resilience is crucial to the sustainable development of cities. Here, we combine remote sensing technology to assess urbanization and its effects on the environment from the perspective of resilience. We use the Guangdong Province China, as the study area, to build an urbanization and ecological resilience index, using a coupling coordination and relative development degree model to study the coupling coordination between the two from 2000 to 2020. The results show that (1) according to night light data the level of urbanization in Guangdong Province has increased rapidly. The Pearl River Delta metropolitan area, with Guangzhou and Shenzhen as the center, has bright lights, while the northern part of Guangdong presents many small scattered light pixels. (2) According to our indices, the level of ecological resilience in Guangdong Province has risen steadily. Maoming City has the highest level of ecological resilience in the province, followed by Jiangmen, Dongguan, Shenzhen, and Zhongshan City. (3) The coupling and coordination of urbanization and ecological resilience in Guangdong Province has been steadily improved. Qingyuan City and Shaoguan City have the highest coupling and coordination, while Zhongshan City has the largest increase in coupling and coordination. (4) The relative development of urbanization and ecological resilience in Guangdong Province has almost reached an ideal balance. (5) The model prediction results show that the coupling and coordination degree of urbanization and ecological environment in Guangdong Province will likely improve from 2021 to 2025, but the overall improvement speed will be slow. The degree of urban coupling and coordination in most cities has continued this trend of growth, upgrading to a middling level of coordinated development level.