Regional land use transitions driven by the adaptive reconciliation of existing land use conflict with socioeconomic development can lead to positive economic effects as well as new land use conflict. Although research on land use transition has progressed considerably, limited studies have explored the spatiotemporal dynamic pattern of land use conflict during the land use transition period. Previous evaluation approaches on land use conflict that mainly focus on status or potential conflict lack conflict intensity evaluation during the land use transition process. A new spatially explicit evaluation framework of land use conflict that directly examines three aspects of conflict, namely, ecological and agricultural (EAC), agricultural and construction (ACC), and ecological and construction (ECC) land conflicts based on ecological quality and agricultural suitability, is proposed in this study. The spatiotemporal dynamic pattern and driving factors of land use conflict in the Yangtze River Economic Belt of China in the period of 2000–2018 are evaluated. The results indicated that comprehensive land use conflict (CLUC) intensity slightly decreased by 9.91% and its barycenter showed a trend toward the west during 2000–2018. ACC is the most drastic conflict among the three aspects of conflict. The mean intensity of ACC reduced remarkably by 38.26%, while EAC increased by 33.15% and ECC increased by 28.28% during the research periods. The barycenter of EAC moved toward the east while the barycenter of ACC and ECC moved toward the west. The changes in the intensity and spreading pattern of land use conflict indices demonstrated the changes in the pattern of territorial space development. Total population, population density, per capita GDP, number of mobile phone users, and road density were strong drivers that influenced the land use conflict of territorial space. Multiple policy recommendations including improving territorial space planning and governance ability, and improving land use efficiency, were proposed to manage and resolve the land use conflict of territorial space. The results and conclusions of this study will help improve future regional land use policies and reduce land use conflict.

This study takes 1597 key villages and towns part of rural tourism in China as research objects and uses the ArcGIS spatial analysis method, combined with the geodetector and the multiscale geographically weighted regression model, to analyze the intensity and spatial differentiation of factors influencing the spatial distribution of these villages and towns. (1) The key villages and towns of rural tourism exhibit clustering distribution patterns with more locations in the east than in the west. The center of gravity of this distribution shifts to the northwest, displaying a “belt–point” trend with scattered hot spots in Beijing, Tianjin, Hebei, and the Yangtze River Delta, while cold spots are primarily concentrated in the northwest. (2) Spatial variation in the key villages and towns of rural tourism is due to multiple factors, of which population density, resident consumption expenditure, and per capita GDP display the highest explanatory powers for the spatial distribution of tourism. (3) The primary influencing factors are spatial differences in their roles and substantial local imbalances. Positive and negative correlation analysis units exhibit the aggregation characteristics of being blocked and banded. These results can provide valuable guidance for the development of rural tourism, promoting its sustainable development and contributing to the revitalization of rural areas.

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban areas with varying urbanization levels. However, many existing experimental methods and conventional models overlook the crucial aspects of spatial autocorrelation and heterogeneity between soil heavy metals and influencing factors. This neglect poses significant environmental concerns, as rapid monitoring of soil heavy metals and accurate identification of their determinants become imperative. This study investigated four environmentally sensitive and potentially harmful soil heavy metals, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb), in two urban areas in China with varying urbanization levels. Enshi (a prefecture-level city) and Wuhan (a provincial capital city) were selected for comparison of the spatially variable relationships between soil heavy metals and their influencing factors. We employed a global stepwise linear regression (STR) model and a local spatial model-geographically weighted regression (GWR) to map the spatial distribution of soil heavy metals based on 121 auxiliary variables, including terrain, geophysical, socioeconomic factors, and remote sensing data. Our results showed that: (1) soil heavy metals exhibited strong spatial aggregation in the prefecture-level city (Enshi) but, nonetheless, have strong spatial heterogeneity in the provincial capital city (Wuhan) due to elevated anthropogenic disturbances; (2) GWR accurately mapped the spatial distributions of As (r = 0.47 and 0.66), Cd (r = 0.74 and 0.53), Cu (r = 0.60 and 0.54), and Pb (r = 0.44 and 0.50) based on auxiliary variables in different cities and also can clearly reveal the spatially variable relationships with main influence factors; (3) human activities were the primary driving factors influencing As and Pb, while natural environment variables were identified as the main potential sources of Cd and Cu. This study demonstrates a methodology to explore spatially variable characteristics of soil heavy metals and their spatial varying relationships with influence factors. The comparative analysis between two cities provides insights that can greatly enhance quantitative source apportionment and support sustainable management strategies for controlling soil heavy metal pollution across varied urban environments.

Herein, the spatial evolution characteristics of high-level Grade A tourist attractions in the Yangtze River Delta (YRD) urban agglomeration, from 2001 to 2021, are studied by comprehensively applying the nearest neighbor index, kernel density analysis, standard deviation ellipse, and spatial autocorrelation. High-level Grade A tourist attractions are investigated using the random forest model as the driving mechanism of the spatial pattern. Results show that 1) the spatial distribution of high-level Class A tourist attractions in the YRD city cluster has grown to be an agglomeration, and the high-density areas have evolved from “point-like dispersion to regiment-like combination,” gradually forming a B-shaped core density structure. 2) The spatial distribution comprises an overall “northwest–southeast” direction, a small counterclockwise rotation, the distribution of the center of gravity to the southwest migration, and the center of gravity from the territory of Suzhou City to the territory of Huzhou City. 3) The high-level Class A tourist attractions in the YRD city cluster as a whole show a strong positive spatial correlation, and the significantly clustered areas include three types: high-high (H-H), low-low (L-L), and low-high (L-H). 4) The spatial distribution of high, A-level tourist attractions in the YRD city cluster results from the combined action of the natural environment, resource endowment, socioeconomy, and policy background. Each element has a nonlinear and complex influence on the distribution of scenic spots.

Along with the rapid development of urbanization and industrialization, the carrying capacity of territorial space has been confronted with a serious crisis. Faced with many uncertain risks and unknown disruptions, it is important to proactively address the uncertainty of future developments in planning and to improve territorial spatial resilience (TSR). Based on the connotation of TSR, we build an assessment framework for TSR containing urban, agricultural and ecological space from three dimensions, including element, structure and function. Using a variety of methods such as the source-sink landscape index, land suitability assessment, and cropland pressure index, we assessed the TSR of the Yangtze River Economic Belt (YREB) from 2000 to 2020 and comprehensively analysed its spatial and temporal evolutionary characteristics. Through data analysis, we observe that the urban spatial resilience (RU) decreases and then increases, while the agricultural spatial resilience (RA) and the ecological spatial resilience (RE) show an increasing trend. The spatial clustering in TSR is apparent, and the distribution of hot and cold spots in RA and RE is reversed in the east–west direction. The changes in TSR are influenced by a combination of RU, RA and RE, which show unique geographical characteristics. Based on the average level and overall evolution of TSR, we divided the study area into five type zones and proposed development strategies for each of them.

The spatiotemporal transformations of rural residential lands and populations reflect changes in rural human–land relations. This study uses high-precision rural residential land patches and population distribution data to detect the area, population density, and spatial heterogeneity of newly added rural residential land (NARRL) in China from 2000 to 2020 through spatial local clustering and geographically weighted regression. The patch results were summarized into county-level units for regional comparison, spatial clustering identification, and policy recommendations. The main conclusions are as follows: (1) The total rural residential area increased by 13.86% between 2000 and 2020. The average population density of NARRL (APDNARRL) at patch scale is 701.64 person/km², significantly exceeding the 507.23 person/km² of the remaining patches. (2) There are obvious spatial differences in the distribution of APDNARRL as per county-level statistics. There are significant differences in APDNARRL on both sides of the Hu Huanyong Line; the APDNARRL on the left is significantly lower than that on the right. (3) Spatial heterogeneity was found to be among the driving factors of APDNARRL. This study also detected the number and location of hollowing counties; it is significant for monitoring dynamic changes in rural residential lands, revealing their spatial distribution patterns and driving factors, thus improving the optimization of rural land resources.