Jun Cai

Promoting health equity is key to achieving sustainable urban development. The National Sanitary Cities in China (NSCC) policy is a critical development model aimed at improving urban environments and enhancing public health. This study evaluates the selection criteria and policy impact of NSCCs, using the nearest neighbour index, geographic concentration index, imbalance index, and kernel density estimation to analyze their distribution characteristics. Additionally, it explores influencing factors using a geodetector model and spatial overlay analysis. The findings indicate a shift in NSCC selection criteria from urban sanitation to urban health, reflecting China’s strategic focus on achieving health equity. The spatial distribution analysis indicates that NSCCs exhibit a clustered pattern, characterized by dual cores, dual centres, multiple scattered points, and regional extensions. NSCCs are influenced by both natural and socioeconomic factors, with economy and population, technological innovation, and informatization exerting greater influences. This study is valuable for understanding the spatial patterns of NSCCs, providing a scientific basis for promoting equitable and sustainable health resource allocation and policymaking.

Heritage is the commonwealth of all humankind. In the context of the rise in global tourism and the continuous deepening of cultural and tourism integration, tourism has emerged as an effective vehicle for the preservation and development of heritage sites. However, it also imposes adverse effects on the local ecological environment and heritage sites, exerting significant pressure on regional sustainable development. In this study, three cities along Jianmen Shu Road were selected as the study area. A comprehensive evaluation index system was developed for tourism ecological security (TES) based on the Driver–Pressure–State–Impact–Response model, and an in-depth analysis of its spatial–temporal evolution characteristics, spatial–temporal migration trends, and influencing factors was performed. The results show that (1) from 2012 to 2022, the average TES in the study area decreased annually, while it increased in Jiange County, Anzhou District, and Santai County. The TES indices were generally higher in areas with a high density of heritage sites or developed economies. Additionally, the districts and counties along the Jianmen Shu Road route never exhibited a deteriorated state. (2) From 2012 to 2022, TES in the study area exhibited an obvious “northeast–southwest” directional pattern, and its center of gravity followed a “V”-shaped trajectory. Overall, the spatial patterns showed minimal variation and exhibited agglomeration characteristics. (3) From 2012 to 2022, the main factors influencing TES included the density of Jianmen Shu Road heritage sites (S6), the number of 3A and above scenic areas (S5), the proportion of cultural tourism and sports in total expenditure (R3), the Normalized Difference Vegetation Index (NDVI) (S4), and other tourism and environmental factors. Moreover, TES systems are becoming increasingly complex and diverse. Finally, based on the results, a comprehensive conceptual framework of the driving mechanism was developed. Additionally, four targeted and scientifically grounded policy recommendations were formulated for restoring, protecting, and managing the TES in the Jianmen Shu Road Heritage Area. This study provides significant reference value for ecological environment preservation and the high-quality development of cultural tourism integration in heritage areas.

Land utilization—a crucial resource for human survival and development—reflects the outcomes of intricate interactions between human communities and their respective environments. The Jianmen Shu Road Heritage Area presents both opportunities and challenges in terms of protection and development. Any alterations in its land use and landscape patterns directly impact the sustainable development of the regional environment and heritage sites. In this study, we considered three cities along the Jianmen Shu Road, analyzed the evolution characteristics of land use and landscape patterns from 2012 to 2022, and used the multi-criteria evaluation–cellular automata-Markov (MCE-CA-Markov) model to predict the land use and landscape patterns in 2027. The results show the following: (1) From 2012 to 2022, forest land was at its greatest extent, the growth rate of forest land increased, the loss rate of cropland increased, and impervious land continued to expand. (2) From 2012 to 2022, the degrees of fragmentation in cropland, impervious land, and grassland increased; water area had the highest connectivity; forest land had the lowest connectivity; and barren land had the highest degree of separation. The degree of fragmentation and connectivity of the landscape patterns decreased, the degree of complexity increased, and landscape diversity increased and gradually stabilized. (3) Predictions for 2022–2027 indicate that forest land, impervious land, grassland, and barren land will increase, whereas cropland and the water area will decrease. The growth rate of grassland will increase, the loss rates of cropland and water area will decrease, and the growth rates of impervious land and forest land will decrease. (4) Further predictions for 2022–2027 indicate that the density and complexity of the grassland edge will decrease, whereas the fragmentation and complexity of the remaining patches will increase. The degree of fragmentation, complexity, connectivity, and separation of landscape patterns will increase significantly, whereas landscape diversity will remain stable. This study deepens our understanding of how land use and landscape patterns change in the heritage area from a long-term perspective that involves both the past and future. Such research can provide crucial information for tourism management, heritage protection, and spatial planning in the heritage area and, thus, has important management implications for the study area and similar heritage areas in other regions.

Numerous studies have demonstrated the benefits of understory spaces and plants on human well-being, but most spatial research has focused on a horizontal perspective. Additionally, there is a lack of research on the effects of plant color on human recovery, especially with respect to color proportions. This study classifies the leaf colors of trees in autumn, which are observed from a looking-up perspective, into green, red, and yellow. On this basis, we created monochromatic, two-color, and three-color groups with varying color proportions to assess their recovery effects and preferences. A total of 30 participants participated in this experiment, and their physiological, psychological, and preference-related indicators were evaluated. The results revealed that the following. (1) The two-color groups had the greatest reduction effect on systolic blood pressure. Monochromatic groups were most effective at reducing diastolic blood pressure. The three-color groups had the greatest effect on lowering the pulse rate. (2) EEG responses varied by color type. (3) The three-color groups had the best recovery effects on the psychological measures. (4) The three-color groups were most favored by participants, with a red–yellow–green ratio of 0.2/0.4/0.4 being the most preferred. These findings demonstrate the differing recovery potentials of various leaf color proportions from a looking-up perspective. This study can provide valuable references for the planning and design of urban forest parks, supplementing the theoretical foundation and research framework for evaluating and creating environments that meet people’s restorative needs.

Green space integrates diversified urban functions, and the analysis of its utilization, can help improve the vitality of the social and economic development of cities while contributing to the important goal of enhancing urban green space (UGS) planning and management. In this study, the heat value obtained from Baidu heat maps was used as an external representation of spatial vitality, and the factors that influence vitality were analyzed from two dimensions, i.e., the inner and outer space characteristics of green space, using spatial big data such as points of interest (POIs), Open Street Map (OSM) and online review data. The findings indicated that green space and urban resources in Chengdu are highly centralized. That is, a high road network density and nearby transportation facilities make it easier for visitors to reach parks, while peripheral functional density also plays a role in promoting vitality; additionally, reasonable and moderate space and functional layouts are conducive to the development of green space. In addition, our study integrates the tour experience index, which has a strong positive impact on vitality, to better reflect the human-oriented characteristics of green space, which are of great relevance to the construction and renewal of human space in UGSs.

Urbanization increases the scales of urban spaces and the sizes of their populations, causing the functions in cities and towns to be in short supply. This study carries out functional space identification on the Dujiangyan elite irrigation area based on remote sensing data and point of interest (POI) data from Open Street Map (OSM), enabling the use of POI data to analyze rural functional spaces. Research and development and big data can greatly improve the accuracy of spatial function recognition, but research on rural spaces has limitations regarding the amount of available data. The Dujiangyan Irrigation District has low spatial aggregation levels for functions, scattered functions and linear distributions along roads. The mixing degrees of regional functions are low, the connections between functional elements are insufficient, and the comprehensive functional quality is low. The features of various functional elements in the region are significant, mostly in the discrete distribution mode, and functional compounding has become a trend. Therefore, it is necessary to integrate spatial resources and improve the centrality of cities and towns to realize the optimal allocation of resources and enable the development of surrounding cities and towns.

The rapid expansion of urbanization has promoted the prosperity of the economy and society but has also caused rural ecological problems. This study takes the Dujiangyan Irrigation District as an example, to construct ecological and recreation patterns, and it breaks through the pattern construction mode of using a single ecological factor that has been used in previous studies. We analyzed the impact of landscape connectivity and area thresholds at different scales on the selection of source areas, and integrated urban construction and human disturbance factors into resistance surface construction. Finally, a comprehensive landscape network combining “ecology and recreation” was determined through the minimum cumulative resistance model. Multiscale landscape connectivity analysis and area threshold setting greatly promoted the results of source identification. After optimization, we identified four ecological corridors and twenty-seven recreational corridors, and the regional landscape security pattern was significantly improved compared with the previous upper planning content. Therefore, this study provides a reference for regional long-term planning and has reference significance for the spatial protection and utilization of rapidly urbanizing areas.

Natural environments contribute to people's perception, preference and health restoration. Many researchers have focused either on the positive effects of overall rural environments on stress recovery or concentrated on the perception and preference aspects of the rural landscape, but few have integrated perception, preference and stress recovery simultaneously. This paper developed a framework which includes 11 elements and 38 element components related to Linpan, China, and distributed it online as part of a survey. As a result, a total of 324 valid questionnaires were collected. The questionnaire included demographic details, perception and preference degree for Linpan, as well as self-estimations of stress recovery. Stepwise multiple linear regression was employed, and revealed 16 significant predictors for the perception, preference and stress recovery in rural environments. In terms of elements, atmosphere and imagery were the two elements that could be best perceived, while woodland, farmland, water, residence and road were the five most important elements for the preference. Moreover, seven elements were also identified as significant predictors for stress recovery. Among the element components, tranquility as well as road and water proximity were the two significant predictors for perception, while wide visibility as well as woodland and residence blending contributed significantly to stress recovery. The five element components of woodland interior, vegetable field, stream, courtyard space and branch road each had a significantly predictive ability for preference and stress recovery. These findings extend the understanding of the perception, preference and restorative properties of rural environments through the combination of elements and element components in Linpan of Western Sichuan, helping to improve the quality and characteristics of rural external and internal environments and create health-promoting environments.