Spatial Distribution Patterns Research Articles

A novel method for assessing water quality status using MODIS images: A case study of large lakes and reservoirs in China

The changing climate and economic development have exerted significant pressure on the water quality of global lakes and reservoirs (hereinafter referred to as lakes). Existing sporadic in-situ monitoring limits a comprehensive understanding of water quality changes at large spatiotemporal scales. Although remote sensing techniques offer efficient water quality observations, they have primarily focused on monitoring single optical active substances, making it difficult to assess water quality changes caused by chemical indicators such as total phosphorus and total nitrogen. However, changes in chemical indicators within a certain range can cause responses to physical indicators, which are comprehensively reflected in the water-leaving reflectance. Based on this physical mechanism, this study obtained the water quality status categories (from excellent to severe pollution) by calculating the water quality index (WQI) and developed a new remote sensing assessment model of water quality status using moderate resolution imaging spectroradiometer (MODIS) imagery. This model, combining fractional-order derivatives and various-dimensional spectral indices, significantly enhanced the sensitivity of spectral reflectance to water quality states, achieving a maximum correlation of 0.72. To address the applicability of the model at large spatiotemporal scales, this study proposed a classification modeling scheme based on the optimal optical water categories, resulting in a 20.69%–34.48% improvement in model accuracy after classification. Additionally, this study proposed an improved ensemble learning model combining the concepts of Stacking and Bagging, achieving an average accuracy of 82% and enhancing accuracy by 5%–15% compared to the original single model. Subsequently, the model was used for the first time to assess the water quality status of 180 large lakes in China from 2000 to 2022. The results revealed that 76.11% of the lakes exhibited excellent and good water quality, with a spatial distribution pattern showing a “better in the west, worse in the east” pattern. Over the 23-year period, 28.33% and 58.89% of the lakes showed improvement and stability trends in water quality status, with stability and improvement predominating in the western and eastern regions, respectively. The research results provide technical support for the rapid assessment of water quality status and sustainable resource management, highlighting the potential of remote sensing technology in water quality monitoring.

A deep learning-based surrogate model for trans-dimensional inversion of discrete fracture networks

Fractures and their geometrical patterns are usually required to analyze the mechanical and flow properties of porous media in the subsurface. Fracture characterization is therefore regarded of crucial importance for optimizing production management or achieving maximum storage capacity. In this research, we propose to invert the fracture networks under the Bayesian framework for the uncertainty quantification. In particular, the number of fractures in the modelling system is treated as unknown, leading to a trans-dimensional inverse problem, and the reversible jump Markov chain Monte Carlo algorithm is applied to sample the model space with possible model moves proposed in the sampling process. A deep learning network is further applied as a surrogate model in the sampling process for increasing the computational efficiency, instead of using the physical forward simulator. We apply the proposed methodology to estimate the spatial distribution of fracture networks based on the head measurements from the steady-state flow simulation. The prior distributions of fracture parameters such as position, orientation and length are described using the discrete fracture networks approach that is deeply rooted in stochastic modelling. Due to the high non-uniqueness, the correct spatial distribution of fracture patterns cannot be successfully recovered in this case study, even a good match between observed and simulated head data is reached. More analysis could be performed in the future with the production historical data or more informative priors.

Spatiotemporal evolution characteristics and influencing factors of the crop water use efficiency in watersheds based on the water footprint.

Climate change has exacerbated the contradiction between water scarcity and sustainable agricultural development. Assessing the crop water use efficiency and its influencing factors could provide a decision-making reference to realize Sustainable Development Goal 2. By analyzing the temporal and spatial evolution characteristics of the crop water footprint, the blue water footprint, green water footprint, and grey water footprint were introduced into the super efficiency slack-based measure model to evaluate the crop water use efficiency in basins. The influence of the driving factors was examined by using the geographic detector model. The situation in the provinces along the Yellow River Basin from 2005 to 2020 was used as a verification case. The results indicated that (1) during the study period, crop water use in the basin was mainly based on the blue water footprint, accounting for approximately 55% of the total water footprint, the grey water footprint, accounting for approximately 30% of the total water footprint, and the green water footprint, accounting for the lowest proportion, at approximately 15%. (2) The crop water use efficiency exhibited a spatial distribution pattern of high values in the east and low values in the west, with obvious upstream provinces<midstream provinces<downstream provinces regional characteristics. (3) The driving factors of crop water use efficiency changes could be ranked as follows: effective irrigation rate (0.75) > disposable income of rural residents (0.71) > population urbanization rate (0.65) > degree of agricultural mechanization (0.63) > agricultural disaster rate (0.61). Furthermore, the interaction effects between the driving factors were greater than the effects of the single factors. The study provides an important reference for understanding the changes, driving mechanisms, and impacts of crop water use efficiency in basin areas. It promotes green agricultural transformation and development to address climate change and alleviate the pressure on water resources.

The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

The yellowfin tuna represents a significant fishery resource in the Pacific Ocean. Its resource endowment status and spatial variation mechanisms are intricately influenced by marine environments, particularly under varying climate events. Consequently, investigating the spatial variation patterns of dominant environmental factors under diverse climate conditions, and understanding the response of yellowfin tuna catch volume based on the spatial heterogeneity among these environmental factors, presents a formidable challenge. This paper utilizes comprehensive 5°×5° yellowfin tuna longline fishing data and environmental data, including seawater temperature and salinity, published by the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) for the period 2000–2021 in the Pacific Ocean. In conjunction with the Niño index, a multiscale geographically weighted regression model based on geographically weighted principal component analysis (GWPCA-MGWR) and spatial association between zones (SABZ) is employed for this study. The results indicate the following: (1) The spatial distribution of dominant environmental factors affecting the catch of Pacific yellowfin tuna is primarily divided into two types: seawater temperature dominates in the western Pacific Ocean, while salinity dominates in the eastern Pacific Ocean. When El Niño occurs, the area with seawater temperature as the dominant environmental factor in the western Pacific Ocean further extends eastward, and the water layers where the dominant environmental factors are located develop to deeper depths; when La Niña occurs, there is a clear westward expansion in the area with seawater salinity as the dominant factor in the eastern Pacific Ocean. This change in the spatial distribution pattern of dominant factors is closely related to the movement of the position of the warm pool and cold tongue under ENSO events. (2) The areas with a higher catch of Pacific yellowfin tuna are spatially associated with the dominant environmental factor of mid-deep seawater temperature (105–155 m temperature) to a greater extent than other factors, the highest correlation exceeds 70%, and remain relatively stable under different ENSO events. The formation of this spatial association pattern is related to the vertical movement of yellowfin tuna as affected by subsurface seawater temperature. (3) The GWPCA-MGWR model can fully capture the differences in environmental variability among subregions in the Pacific Ocean under different climatic backgrounds, intuitively reflect the changing areas and influencing boundaries from a macro perspective, and has a relatively accurate prediction on the trend of yellowfin tuna catch in the Pacific Ocean.

Release of perfluoroalkyl acids from sediments under the effects of the discharge ratio and flow flux at a Y-shaped confluence

The sediments in riverine environments contain notably high concentrations of perfluoroalkyl acids (PFAAs), which may be released into the water body under different hydrodynamic forces, such as those occurring at Y-shaped confluences. The release of PFAAs may pose a significant risk to the surrounding aquatic ecosystems. However, our understanding of the release and transport of PFAAs from sediments at Y-shaped confluences remains unclear. Thus, in this study, we performed a series of flume experiments to explore the effects of discharge ratio and total flow flux on the release and redistribution of PFAAs. The results indicated that these two parameters significantly affected the hydrodynamic features of confluences and the water physicochemical parameters. PFAA concentrations in the dissolved phase and suspended particulate matter (SPM) rose significantly as the discharge ratio and total flow flux increased. The dissolved phase was the predominant loading form of PFAAs, with short-chain PFAAs being the main kind, while long-chain PFAAs were dominant in the SPM. The spatial distribution pattern of PFAAs in sediments at the confluence exhibited a high degree of correspondence with hydrodynamic zones. The separation zone and maximum velocity zone were consistent with sediment regions with low and high capacities to release PFAAs, respectively. The patterns of variation in PFAA distribution were comparable to those observed in hydrodynamic zones as the discharge ratio and total flow flux varied. Furthermore, these two parameters altered the partitioning behaviors of PFAAs; specifically, the PFAAs in sediments tended to be released into the pore-water, while the liberated PFAAs tended to attach to SPM. Linear regression and correlation analyses suggested that the stream-wise and vertical flow velocity components near the sediment-water interface were the primary contributors to sediment suspension and PFAA exchange between the water column and pore-water. These findings will help us to understand the patterns of PFAA release in sediments at Y-shaped confluences and assist in the management of PFAA-contaminated sediments at these locations.

Tracer movement and residence time distribution simulation: An initiative to improve the wetland water environment in the Helan Mountain impact plain

The hydraulic residence time (HRT) serves as a critical temporal scale for calculating the duration of pollutants within a water body. The spatial and temporal variations of HRT play a crucial role in developing location-specific water management strategies. In our study, we assessed the spatial and temporal distribution patterns of hydraulic residence time in Xinghai Lake, China – a representative case of large shallow lakes regulated by zoning – using a hydrodynamic-tracer model. The findings reveal that manipulating the outflow gates leads to a fluctuation range of 20 days in hydraulic residence time. Our analyses suggest that the operation of outflow gates significantly influences the reduction of hydraulic residence time. Compared with previous research, the spatial and temporal distribution patterns of hydraulic residence time examined in this study demonstrate the effectiveness of zonal regulation in improving water resource management.

Spatial distribution patterns of human resources allocation in maternal and child healthcare institutions in China from 2016 to 2021

BackgroundIn China, economic, urbanization, and policy differences between the eastern and western regions lead to uneven healthcare resources. This disparity is more pronounced in the west due to fewer healthcare personnel per thousand individuals and imbalanced doctor-to-nurse ratios, which exacerbates healthcare challenges. This study examines the spatial distribution of human resources in maternal and child healthcare from 2016 to 2021, highlighting regional disparities and offering insights for future policy development.MethodsThe data were sourced from the “China Health and Family Planning Statistical Yearbook” (2017) and the “China Health and Health Statistics Yearbook” (2018–2022). This study utilized GeoDa 1.8.6 software to conduct both global and local spatial autocorrelation analyses, using China’s administrative map as the base dataset.ResultsFrom 2016 to 2021, there was an upward trend in the number of health personnel and various types of health technical personnel in Chinese maternal and child healthcare institutions. The spatial distribution of these personnel from 2016 to 2021 revealed clusters characterized as high-high, low-low, high-low and low-high. Specifically, high-high clusters were identified in Guangxi, Hunan, Jiangxi, and Guangdong provinces; low-low in Xinjiang Uygur Autonomous Region and Inner Mongolia Autonomous Region; high-low in Sichuan province; and low-high in Fujian and Anhui provinces.ConclusionsFrom 2016 to 2021, there was evident spatial clustering of health personnel and various health technical personnel in Chinese maternal and child healthcare institutions, indicating regional imbalances.

How Social Media Data Mirror Spatio-Temporal Behavioral Patterns of Tourists in Urban Forests: A Case Study of Kushan Scenic Area in Fuzhou, China

Exploring the spatial distribution of tourist attractions and comprehending the spatio-temporal behaviors of tourists within tourist attractions can provide local planning agencies, destination marketing organizations, and government departments with essential evidence for decision-making processes. This study examines the spatio-temporal behavior patterns of tourists in the Kushan Scenic Area by analyzing GPS trajectory data acquired from social media platforms. The investigation primarily utilizes three research methodologies: grid analysis, Markov chain, and K-means clustering. The grid analysis results reveal three spatial distribution patterns within the scenic area, while the outcomes from the Markov chain and K-means clustering delineate six tourist movement patterns, along with three choices regarding travel time. This finding holds significant practical implications for enhancing the attractiveness of scenic areas, optimizing spatial layout, and improving tourists’ experiences.

Assessment and mapping of priority areas for transboundary ecological conservation: Suggestions for the protection of the Altai Mountains in Central Asia

Political boundaries often do not coincide with ecological boundaries, and it is common for the same physical geographical unit to span multiple countries. Exploring establishing transboundary protected areas and ecological networks can effectively protect the integrity of natural ecosystems separated by political boundaries. Based on the integrated evaluation of ecosystem services function, ecological vulnerability, and ecosystem integrity, this study explored a methodological system for evaluating ecological protection priority areas in transboundary areas and selected the Altai Mountains, which span four countries, contain China, Kazakhstan, Russia, and Mongolia, as a typical case to conduct empirical research. The study found that the extremely important areas of ecosystem services function, ecological vulnerability, and ecosystem integrity showed transboundary contiguous characteristics. Furthermore, weighted overlay analysis identified the spatial distribution pattern of ecological protection priority areas in the Altai Mountains. We suggested establishing transboundary protected areas in the four countries’ transboundary areas, building the identified ecological protection priority areas and existing protected areas into a transboundary protected area network, and deepening a multi-party transboundary protection cooperation mechanism. This study can provide a theoretical and practical reference to achieve the 2030 biodiversity conservation goals.

Predicting the distribution pattern changes of dye plant habitats caused by climate change

Climate change has accelerated the habitat loss and fragmentation of wildlife. Dye plants of “Fengxiang dyeing” are important indigenous natural resources for traditional printing and dyeing craft in southwest China, is of practical and cultural importance for dozens of ethnic minorities. However, lack of the spatial distribution information of these plants has hampered holistic and efficient conservation management measures. We analyzed the potentially suitable areas of four dye plants (Liquidambar formosana, Strobilanthes cusia, Persicaria tinctoria and Indigofera tinctoria) necessary for “Fengxiang dyeing” based on their geographical distribution sites under different climatic situations using the maximum entropy (MaxEnt) model. The results showed that temperature, precipitation and elevation were the most important factors affecting the suitable geographical areas of the four dye plants. Under the current climate conditions, the overlapping suitable habitat areas of the four plants were mainly in the four southern provinces of China, including Guizhou, Guangxi, Guangdong and Hainan. L. formosana was used as the base plant for combination with the other three plants under the two future climate scenarios (SSP126 and SSP585), and the overlapping suitable habitat areas of the obtained seven combination patterns were considered suitable for potential craft development. Five patterns showed an increase, while two patterns showed a decreasing trend with the increasing carbon emission. The prediction results showed that the overlapping suitable habitat center of the four plants will gradually move to the northeast, indicating that the overlapping suitable habitat area and craft distribution area will be changed. These results provide the basis for understanding the spatial distribution pattern changes of dye plants caused by climate change and establishing measures for protecting and developing printing and dyeing craft.

Analyzing Spatial–Temporal Characteristics and Influencing Mechanisms of Landscape Changes in the Context of Comprehensive Urban Expansion Using Remote Sensing

The phenomena of global climate change and comprehensive urban expansion have precipitated significant and unprecedented transformations in landscape patterns. To enhance the assessment of these spatio−temporal changes and their driving forces at a regional level, we developed a comprehensive landscape index (CLI) to quantify these patterns and conducted a detailed analysis of the spatio−temporal variations in Minnesota over the last two decades. Our analysis of the CLI was conducted by examining both its quantitative relationships and spatial distribution patterns. The findings indicate a consistent increase in Minnesota’s CLI over this period, marked by an escalation in landscape fragmentation and diversity, alongside a decline in landscape connectivity. Temporally, the CLI experienced a notable shift in 2010. Spatially, the clustering characteristics of landscape patterns have largely remained stable. Our analysis reveals that the CLI is most sensitive to total population (POP) and gross domestic product (GDP) factors, underscoring the significant impact of human activity on landscape patterns. Notably, the explanatory capacity of interactions between factors is substantially greater than that of individual factors, with the GDP and vegetation structure (VS) interaction demonstrating the greatest influence on the spatial distribution of landscape patterns. This highlights the critical role of the interplay between human socio−economic activity and vegetation coverage in shaping landscape configurations.

A Study on Spatial Differentiation of Landscape Pattern Based on Three-Dimensional Morphology of Urban Buildings

Abstract. Urban buildings are an important part of urban morphology, and building height has an important impact on the three-dimensional spatial morphology of cities. At present, research on the two-dimensional morphology of cities is relatively abundant, but there are fewer studies on the characteristics of the three-dimensional undulating morphology of cities and their spatial distribution patterns, and thus knowledge of the degree of utilization of the airspace above the city and its developmental pattern is still relatively lacking.Based on the multi-scale urban agglomeration, the landscape pattern is converted from two-dimensional to three-dimensional, and the overall spatial differentiation can be analyzed more intuitively. The purpose of this paper is to explore the three-dimensional spatial differentiation law of urban architectural landscape pattern. Through in-depth spatial analysis of the architectural landscape in urban areas, it is found that the landscape patterns in different areas show significant differences, and the interrelationship between them and the urban landscape pattern is revealed through the study of three-dimensional characteristics of urban buildings, such as height, density, form and layout. It is found that the different characteristics of urban buildings have a significant impact on the landscape pattern, which leads to the differentiation of urban space. This study provides more comprehensive spatial information for urban planning, contributes to a better understanding of the complexity of urban architectural landscapes, and provides a scientific basis for future urban design and planning.

Additive partitioning of multispecies distributional aggregation of local assemblages.

The distribution of species is not random in space. At the finest-resolution spatial scale, that is, field sampling locations, distributional aggregation level of different species would be determined by various factors, for example spatial autocorrelation or environmental filtering. However, few studies have quantitatively measured the importance of these factors. In this study, inspired by the statistical properties of a Markov transition model, we propose a novel additive framework to partition local multispecies distributional aggregation levels for sequential sampling-derived field biodiversity data. The framework partitions the spatial distributional aggregation of different species into two independent components: regional abundance variability and the local spatial inertia effect. Empirical studies from field amphibian surveys through line-transect sampling in southwestern China (Minya Konka) and central-southern Vietnam showed that local spatial inertia was always the dominant mechanism structuring the local occurrence and distributional aggregation of amphibians in the two regions with a latitudinal gradient from 1200 to nearly 4000 m. However, regional abundance variability is still nonnegligible in highly diverse tropical regions (i.e. Vietnam) where the altitude is not higher than 2000 m. In summary, we propose a novel framework that shows that the multispecies distributional aggregation level can be structured by two additive components. The two partitioned components could be theoretically independent. These findings are expected to deepen our understanding of the local community structure from the perspective of both spatial distribution and regional diversity patterns. The partitioning framework might have potential applications in field ecology and macroecology research.

Research on the spatial pattern distribution of soil selenium using machine learning methods integrating geographic proximity in complex terrain

Research on the spatial pattern distribution of soil selenium using machine learning methods integrating geographic proximity in complex terrain

Spatial distribution patterns and drivers of above- and below- biomass in Chinese terrestrial ecosystems

Unraveling the dynamics of the global carbon cycle and assessing the sustainability of terrestrial ecosystems are critically dependent on a comprehensive understanding of vegetation biomass. This exploration delves into the pivotal role of biomass within vegetation communities, emphasizing its impact on ecosystem health, productivity, and community structure development. These insights are invaluable for advancing ecological science and conservation efforts. The synthesis of aboveground (AGB) and belowground (BGB) biomass data from 4485 and 3442 locations across China, respectively, collates a wide range of published sources. Integrating this extensive dataset with environmental parameters and applying advanced machine learning techniques facilitated an in-depth analysis of AGB and BGB spatial patterns within China. Techniques such as variance decomposition analysis and piecewise structural equation modeling were employed to dissect the factors contributing to the spatial variability of vegetation biomass. Significant spatial heterogeneity in biomass distribution was uncovered, with vegetation biomass in the northwest markedly lower than in the southern and northeastern regions. It was observed that AGB consistently surpassed BGB. Climatic conditions, soil characteristics, and soil nutrients were found to significantly explain 53 % and 48 % of the total variance in AGB and BGB, respectively. Specifically, solar radiation and soil total nitrogen were identified as critical factors influencing variations in AGB and BGB. The findings offer profound contributions to the understanding of the global carbon balance and the evaluation of terrestrial ecosystems sustainability. Moreover, they provide essential insights into the ecosystems' response mechanisms to global changes, serving as a fundamental reference for future studies on terrestrial ecosystem carbon cycling and carbon sequestration potentials.

Novel Use of Fractal Analysis for Quantifying Polymethylmethacrylate Distribution Patterns in Osteoporotic and Malignant Vertebral Compression Fractures Following Vertebroplasty.

Purpose: Fractal analysis is a mathematical tool which allows the evaluation of complex microstructural features within materials that cannot be expressed in traditional geometric terms. The purpose of this study is to quantify the differences in polymethylmethacrylate intravertebral cement spatial distribution patterns following vertebroplasty using fractal analysis through the examination of osteoporotic and malignant compression fractures. Methods: Frontal and lateral post-vertebroplasty radiographs were evaluated from 29 patients with osteoporotic and malignant compression fractures who underwent vertebroplasty. The individually treated vertebra were divided into osteoporotic (n = 35) and malignant groups (n = 41). Images underwent segmentation, thresholding, and binarization prior to fractal analysis. Fractal dimension and lacunarity values were derived from the region of interest in treated vertebrae using the "box-counting" and "gliding-box" techniques respectively using ImageJ. The mean values of both parameters were compared between the 2 groups. Results: The mean fractal dimension was significantly higher in the malignant vertebral compression fracture group (1.53 ± 0.08) compared to the osteoporotic group (1.34 ± 0.17; P < .001). Similarly, mean lacunarity values were significantly higher in the malignant fracture group (0.50 ± 0.09) compared to the osteoporotic group (0.37 ± 0.10; P < .001). Conclusions: Fractal dimension and lacunarity values of cement spatial distribution patterns obtained from the post-vertebroplasty radiographs can differentiate between benign osteoporotic and malignant vertebral compression fractures. This novel technique may be useful for evaluating cement spatial distribution patterns in spine augmentation procedures, although further research is warranted in this area.

Global soil respiration estimation based on ecological big data and machine learning model

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Study on the Spatial Layout and Influencing Factors of Campsites in the Yellow River Basin

Based on the camping sites in the Yellow River Basin published by Amap, this study examines the spatial distribution pattern of camping sites using various indices, including the average nearest neighbor index, geographical concentration index, disequilibrium index, and kernel density estimation. The research findings are as follows: (1) Camping sites exhibit a highly significant agglomeration distribution, and the spatial scale presents a non-equilibrium characteristic of “east dense west sparse”. The distribution density of camping sites shows clear hot and cold spots, forming a general pattern of “one belt, one mass, two points”. The locations of these camping sites commonly follow the rule of “backing mountains, along roads, and accompanied by scenery”. (2) The spatial distribution of camping sites is influenced by both natural factors, such as elevation and air quality, and social factors, such as highway mileage and the number of high-level scenic spots. The impact of social factors is found to be more substantial than that of natural environmental factors.

Interprovincial spatial distribution patterns and socioeconomic factors on traditional Chinese medicine (TCM) service utilization in China

The traditional Chinese medicine (TCM) industry in China exhibits significant regional disparities in health service utilization, the underlying reasons for which are yet to be fully explored. This study employs Geodetector models to analyze the factors affecting TCM service utilization, providing the first examination of spatial distribution patterns and influencing factors for both TCM outpatient (TCMOSU) and inpatient services (TCMISU). The findings of this study reveal spatial disparities across China's provinces, showing a prevalence of TCMOSU in the east and TCMISU decreasing from southwest to northeast. Global Moran's I autocorrelation analysis revealed a positive spatial correlation between TCMOSU and TCMISU across Chinese provinces, suggesting spatial clustering and the potential for interregional collaboration in the development of TCM services. Local Moran's I autocorrelation analysis revealed clusters of TCMOSU in wealthier eastern provinces, such as Jiangsu and Tianjin, and clusters of TCMISU in the southwest. Factor detector analysis revealed that disposable income per capita was the most significant factor linking higher incomes with increased TCMOSU. In contrast, TCMISU was primarily influenced by demographic factors, such as the illiteracy rate and population urbanization rate, emphasizing traditional practices in lower education regions. Interaction detector analysis revealed the joint effects of these factors, demonstrating how regional economic status, health status, and healthcare resource indicators interact with other factors for TCMOSU and how demographic factors significantly influence the prevalence of TCMISU. This study highlights the importance of considering health status together with regional economic, demographic, and healthcare resources when formulating TCM healthcare policies and allocating such resources in China. Promoting the balanced and coordinated regional development of TCM services across the country requires the development of strategies that account for these varied regional characteristics.

Impacts of Urban Form on Carbon Emissions Under the Goal of Carbon Emission Peak and Carbon Neutrality: A Case Study of the Yangtze River Economic Belt

Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.