The safety of public spaces on university campuses directly affects the mental and physical well-being of both faculty and students, significantly contributing to the stability and growth of the institution. Therefore, evaluating and optimizing the sense of security in these public areas is paramount. This study adopts a human-centric approach and selects five physical environmental metrics to construct a comprehensive safety perception model encompassing both day and night, using the analytic hierarchy process. By leveraging evaluation outcomes, a maximum coverage location-allocation model constrained by a facility service radius is employed to optimize the layout of the public amenities on campus, supported by empirical research conducted at the Wuchang Campus of the Wuhan Institute of Technology. The results indicate that a notable decrease in public areas provides a low sense of security to users and that an increase in public areas provides users with a high sense of security. The results demonstrate a considerable enhancement in both the daytime and nighttime sense of security due to the implemented optimization measures. This research contributes to a deeper understanding of the constituent elements shaping the sense of security in campus public spaces, offering concrete strategies for their evaluation and enhancement, thereby fostering a more scientifically grounded layout of campus public facilities and advancing the creation of a harmonious and secure campus environment.

Introduction: Machine learning techniques, renowned for their ability to process complex datasets and uncover key ecological patterns, have become increasingly instrumental in assessing ecosystem services.Methods: This study quantitatively evaluates individual services—such as water yield, carbon storage, habitat quality, and soil conservation—on the Yunnan-Guizhou Plateau for the years 2000, 2010, and 2020. A comprehensive ecosystem service index is employed to assess the overall ecological service capacity, revealing spatiotemporal variations in services and exploring the trade-offs and synergies among them. Additionally, machine learning models identify the key drivers influencing ecosystem services, informing the design of future scenarios. The PLUS model is used to project land use changes by 2035 under three scenarios—natural development, planning-oriented, and ecological priority. Based on the land use simulation results for these scenarios, the InVEST model is applied to evaluate various ecosystem services.Results: During 2000-2020, ecosystem services on the Yunnan-Guizhou Plateau exhibited significant fluctuations, driven by complex trade-offs and synergies. Land use and vegetation cover were the primary factors affecting overall ecosystem services, with the ecological priority scenario demonstrating the best performance across all services.Discussion: The research integrates machine learning with the PLUS model, providing more efficient data interpretation and more precise scenario design, offering new insights and methodologies for managing and optimizing ecosystem services on the Yunnan-Guizhou Plateau. These findings contribute to the development of more effective ecological protection and sustainable development strategies, applicable to both the plateau and similar regions.

Introduction: In recent years, Laos, with its unique geographical advantages, has vigorously developed overseas trade and cultural tourism industry, which has promoted local economic development and introduced numerous ecological and environmental problems.Method: Therefore, this research took Savan District as the research area, built the landscape ecological risk assessment model to reveal the spatiotemporal evolution characteristics of Savan District’s ecological risk from 2000 to 2020. Then, the Geodetector model was utilized to explain the driving forces behind changes in the landscape’s ecological risk. Finally, the Markov-PLUS coupling model was used to simulate and predict the changes in the land use pattern and ecological risk in the Savan District in 2030.Results: The following results are presented. 1) The landscape types in Savan District are mainly cultivated land and forest land, with the lowest degree of landscape fragmentation and loss, and the highest degree of landscape loss in unused land. 2) From 2000 to 2020, the average ecological risk index of the whole study area was low and showed a decreasing trend yearly, with the area of low-risk areas accounting for more than 90%. 3) In 2030, the average ecological risk index of the entire Savan District and the two provinces will rise, meanwhile the low-risk areas in the midwest regions and high-risk areas in the northwest will increase.Discussion: This study can guide the future coordinated development of the social economy and ecological environment in Savan District, Laos, and has good reference significance for ecological construction in similar areas in Southeast Asia.

Using Zhijiang City, Hubei Province as an example, this study constructed an ecological risk assessment model based on land use data from the three phases of 2000, 2010, and 2020. We then determined the ecological compensation priority sequence based on the ecosystem services value (ESV) and the economic status of the research area. The findings revealed that there was a significant spatial differentiation in ESV during the study period, with the ESV being higher south of the Yangtze River than north. Overall, in Zhijiang City, the ESV generally decreased over the course of the study period, with a decrease of CNY 812 million in 20 years. The loss of wetland and grassland was the greatest, and was most obvious between 2010 and 2020, whereas the loss of construction land was the lowest among the different types of landscapes. The ecological risk index of Zhijiang City showed a declining trend between 2000 and 2020. The extent of high-ecological risk areas shrank by 55.83 km², and their predominant landscape types were grassland and forest land. The low-ecological risk area expanded by 340.50 km² and was primarily distributed in construction land along the Yangtze River Basin. The ecological compensation priority sequence was divided into five levels in each town in Zhijiang City, with Gujiadian Town and Baiyang Town receiving the highest grades and being designated as priority compensation areas. The study intends to serve as a model for the construction of ecological cities, ecological environmental protection, and sustainable development in the Yangtze River Basin.