This study optimized the slope and slope length factor (LS) and crop management factor (P) of the RUSLE model and then introduced the gravity centre model to analyze the spatial–temporal variation patterns of soil erosion in Yellow River Basin from a new perspective. Results showed that: (1) The improved model of RUSLE with optimized factors of LS and P had better applicability in Yellow River Basin; (2) The average erosion intensity was 2777.5 t/a, which belonged to moderate erosion. The soil erosion intensity of the Yellow River Basin showed an overall trend of increasing firstly (1990–2005) and then decreasing (2005–2015). (3) During 1990–2015, the gravity centre of soil erosion moved to the southwest, indicating that the increment and increasing rate of soil erosion in the southwest parts of the Yellow River Basin were greater than that in the northeast parts. (4) The intensity of soil erosion aggravated with the increasing slope. The sandy soil, chestnut soil, light-grey calcium soil and fluvo aquic soil had severe erosion intensity due to the regional climate and their own physical–chemical structure. The woodland and shrubbery land were more susceptible to soil erosion.

The spatial distribution and accessibility of urban public service facilities affect socioeconomic factors in the lives of residents, especially housing prices. Given that most previous studies focus on the accessibility of a certain, single type of facility and its impact on housing prices, this research uses improved two-step floating catchment area (2SFCA) methods by considering the differences in the service capacity of different types of public service facilities in real life to evaluate their accessibility to residential communities in Jinan city based on 3117 facilities covering 11 different kinds of facilitates. Then, we assess the spatial distribution of the impact of the accessibility of different public service facilities on housing prices in Jinan city through a local indicator of a spatial association (LISA) cluster diagram generated based on the bivariate local Moran’s index. Our objectives are to assess the accessibility of multiple public service facilities using an improved 2SFCA method and to explore the spatial correlations between the accessibility of public service facilities and housing prices. The results show that the housing prices in Jinan are clustered and that the areas with high housing prices are mainly concentrated in the Lixia District and the center of the downtown area. The accessibility of medical, shopping, educational and bus stop facilities in the Lixia District is better than that in other districts. The accessibility of shopping, medical and tourist attraction facilities has the most significant impact on housing prices, and the number of communities in which the accessibility of these public service facilities and housing prices form a positive correlation cluster accounts for 50.5%, 47.9% and 45.8% of all communities, respectively. On the other hand, educational accessibility and bus stop accessibility have nothing to do with housing prices, and the number of communities in which the accessibility of these public service facilities forms a not-significant cluster with housing prices accounting for 51.1% and 56.5% of the total, respectively. In this study, the combined 2SFCA method is used to improve the method for evaluating the accessibility of a variety of public service facilities, and its applicability is verified by practical application. By analyzing the spatial correlation between accessibility and housing prices, we expand our understanding of accessibility and show that it plays a central role in housing prices, which will help to improve the spatial pattern of urban public places in the future, provide support for decision makers and provide a reference for the government and real estate developers.

Urban vibrancy is an important indicator of urban prosperity. Evaluating the correlation between urban vibrancy and housing prices is increasingly necessary. However, most studies have ignored the temporal characteristics of urban vibrancy and only considered its spatial nature. This study optimizes the identification model of urban vibrancy centers and proposes a novel model for evaluating the time-dynamic accessibility of urban vibrancy centers. The model takes into account real-time changes in travel costs and analyses its spatial correlation with housing prices. The case study of Chengdu shows significant variations in the spatiotemporal distribution of urban vibrancy centers. High-vibrancy areas are concentrated near commercial complexes. The time distribution of urban vibrancy accessibility is more concentrated at night (18:00-24:00), and the high-accessibility areas are located around the old city and the newly planned Central Business District in the south. Housing prices and urban vibrancy centers accessibility are mainly negatively correlated spatially. The study emphasizes the temporal nature of urban vibrancy and accessibility and enriches the evaluation model of urban vibrancy centers. It also contributes to the improvement of the framework of urban vibrancy research and deepens the understanding of the important variable of "time" in urban planning.

Agricultural non-point source (NPS) pollution directly affects the quality of soil and water, ecological balance and human health, and is a key challenge to achieve sustainable environmental development and efficient resource management. Taking the Nansi Lake Basin (NLB) as the study area, this study explores the main sources of agricultural NPS pollution and its influencing factors, aiming to provide scientific basis for the management of water resources in the basin. Current studies usually use the runoff pollution partitioning method to estimate agricultural NPS pollution loads in runoff, but the accuracy of the analyses is limited by the incompleteness of water quality monitoring data, especially the lack of complete runoff records in some years. To compensate for this deficiency, this study simulated the river runoff based on the Long-Term Hydrological Impact Assessment (L-THIA) model, and applied the simulation results to the quantitative calculation of agricultural NPS pollution loads after verifying the model reliability through accuracy calibration. Based on L-THIA model, the spatial and temporal distribution data of agricultural NPS pollution in the basin from 2010 to 2020 were obtained, the distribution characteristics of chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) were quantitatively assessed, and the impacts of natural and socio-economic factors on them were analyzed. A regression model was developed to simulate future agricultural NPS pollution through multiple regression analysis. The results showed that the total agricultural NPS pollution in the NLB showed an increasing trend during the study period. In particular, among the socio-economic factors, COD and NH₃-N were significantly correlated with fertilizer application, pesticide use, rural employment and total population. Among the natural factors, topographic index, watershed area and gully density were positively correlated with pollutants, while slope and soil organic matter were negatively correlated. The results of this study raise awareness of the contribution of influencing factors and allow researchers and planners to focus on the most important NPS pollution sources and influencing factors. The study provides an important reference for the prevention and control of agricultural NPS pollution in the NLB, which is of great practical importance.