Kernel Density Research Articles

Spatial Distribution Characteristics of Leisure Urban Spaces and the Correlation with Population Activity Intensity: A Case Study of Nanjing, China

The spatial distribution of Leisure Urban Spaces (LUSs) is closely linked to urban sustainability and residents’ quality of life. This study uses the Central Urban Area of Nanjing as the study area. Using POI and AOI data, the locations of LUS were precisely identified and categorized, including parks, squares, waterfront spaces, and leisure blocks. GIS spatial analysis methods, the nearest neighbor index, standard deviation ellipse, and kernel density estimation were used to analyze these spaces’ form, directivity, and density. Population activity intensity (PAI) data at various time points, collected by Baidu heat map, are correlated with LUS distribution through multiple linear regression analysis. (1) Parks and squares exhibit significant clustering tendencies, whereas waterfront spaces show weaker clustering, and leisure blocks are randomly distributed; (2) The central points of all types of LUS are located in the city center, extending from southeast to northwest, with parks and squares offering a broader range of services; (3) The overall LUS layout shows a ‘core and multiple points’ structure, with varying density patterns across different spaces, indicating concentrated and dispersed leisure areas; (4) The correlation between LUS distribution and PAI strengthens throughout the day and is greater on weekends than weekdays. Leisure blocks significantly enhance activity intensity, while parks have a limited effect, and waterfront spaces often show a negative correlation due to their remote locations. These results provide insights for future urban planning in Nanjing and underscore patterns in residents’ leisure activities.

Is it possible to find the single nearest neighbor of a query in high dimensions?

We investigate an open question in the study of the curse of dimensionality: Is it possible to find the single nearest neighbor of a query in high dimensions? Using the notion of (in)distinguishability to examine whether the feature map of a kernel is able to distinguish two distinct points in high dimensions, we analyze this ability of a metric-based Lipschitz continuous kernel as well as that of the recently introduced Isolation Kernel. Between the two kernels, we show that only Isolation Kernel has distinguishability and it performs consistently well in four tasks: indexed search for exact nearest neighbor search, anomaly detection using kernel density estimation, t-SNE visualization and SVM classification in both low and high dimensions, compared with distance, Gaussian and three other existing kernels.

Spatial patterns of Geological Culture Villages (Towns) and their influential factors in China

Geological Culture Villages Towns (GCV(T)s) serve as rich repositories of geological resources, embodying significant historical and cultural values that mirror the dynamic interplay between Earth's evolution and human societies. Despite their importance, the theoretical underpinnings of GCV(T)s have not been thoroughly explored and elucidated. This study explores the relationship between geological culture and societal development, revealing the linkage between geological culture and societal advancement with the aim of promoting a harmonious coexistence between humanity and the environment. Focusing on 96 GCV(T)s in China, this study employs spatial analytical techniques such as the nearest-neighbor index, kernel density analysis, geographic connectivity, and geographic detector to scrutinize the spatial distribution patterns and identify key influencing factors. The findings reveal that (1) GCV(T)s exhibit uneven distribution, with concentrations in the Yangtze River Delta region, Beijing-Tianjin-Hebei region, and Shanxi-Gansu and Ningxia Provinces. (2) These sites tend to be situated in areas characterized by active geological formations, abundant geological relics, higher elevations, gentle topography, and ample rainfall. (3) The establishment and evolution of GCV(T)s are shaped by a complex interplay of geological heritage, natural factors, and human interventions. While the selection and spatial arrangement of these sites are predominantly influenced by geological and natural factors, the innovative development of their geological cultural heritage is driven by humanistic considerations. Overall, the distribution and evolution of GCV(T)s stem from multifaceted interactions among diverse factors. Recognizing this intricate web of influences is crucial for safeguarding geocultural diversity and formulating conservation strategies for geoheritage protection. The development of GCV(T)s transcends mere geoheritage preservation, it embodies a pivotal stride toward safeguarding the varied and multi-dimensional global geoheritage.

Archaeological LiDAR in Mediterranean Karst Landscapes. A Multiproxy Dating Method for Archaeological Landscape and a Case Study From Prehistoric Kras Plateau (Slovenia)

ABSTRACTThe case study area is a small but typical prehistoric landscape in the Kras Plateau on the north coast of the central Mediterranean. The Late Bronze and Iron Age Kras Plateau was an emblematic Mediterranean archaeological landscape dotted with numerous hillforts. Since the mid‐20th century, the landscape had been overgrown with some of the most archaeology‐hostile vegetation, severely impeding landscape archaeology until archaeological LiDAR revealed thousands of archaeological features that attest to a carefully constructed and managed agro‐pastoral landscape. However, these discoveries were hampered by insecure chronology typical of any LiDAR guided analysis. This case study meticulously documented two prehistoric hillforts and a previously unknown agro‐pastoral landscape with hundreds of archaeological features. The focus of the article, however, was on establishing a more precise and objective dating method. We proposed a multiproxy method to date the archaeological landscape. It combines relative dating using remote sensing data and historical maps; dating based on historical context; relative stratigraphic dating; indirect dating based on associated archaeological finds; and dating by association. Particularly, we focused on the association‐based dating of archaeological features. We proposed a method based on the concept of taskscapes that relies on kernel density estimation. Using these methods, we objectively demonstrated that agro‐pastoral landscape features documented with archaeological LiDAR were contemporaneous with Late Bronze Age and Iron Age hillforts and have no connection to the post‐medieval landscape. The latter has important methodological implications for the prehistoric archaeology of Mediterranean karst landscapes, where backdating post‐Medieval landscapes is a common practise.

The impact of digital financial inclusion on bank performance: An exploration of mechanisms of action and heterogeneity.

The use of digital technology by banks and other financial institutions to facilitate financial inclusion is referred to as digital financial inclusion. This fusion of digital finance and traditional banking methods has the potential to impact banks' operational effectiveness. This study uses the panel effects model to examine the link between digital financial inclusion and bank performance in 30 Chinese provinces from 2012 to 2021. This research uses kernel density estimation to examine the spatial-temporal growth patterns of both variables. The mediator variable in examining how digital financial inclusion affects bank performance is risk-taking. Finally, the paper analyses the regional heterogeneity of the impact. It presents the following conclusions: (1) In China, digital financial inclusion and bank performance have constantly increased, with noticeable regional variances in their development levels. This regional inequality has widened gradually since 2018, yet it has not resulted in polarization. (2) The significant positive correlation between digital inclusive finance and banking performance indicates that banking performance tends to increase with the enhancement of digital inclusive finance. (3) Digital financial inclusion impacts bank performance, with risk-taking as a moderator. The spread of digital financial inclusion services enhances banks' willingness to take risks, enhancing overall efficiency. (4) Digital financial inclusion boosts bank performance in the Northwest, South, North, and East regions while lightly inhibiting it in the Central region. Based on the findings, this study makes bank and government suggestions.

Q residual non-parametric Distribution on Fault Detection Approach Using Unsupervised LSTM-KDE

It is well known among practitioner, majority collected data from industrial process plant are unlabeled. The collected historical data if utilize, able to provide vital information of process plant condition. Learning from unlabeled dataset, this study proposed Unsupervised LSTM-KDE approach as a measure to predict fault in industrial process plant. The residual based fault detection approach framework is utilized with long short-term memory (LSTM) as the main pattern learner for nonlinear and multimode condition that usually appear in process plant. Furthermore, kernel density approach (KDE) is used to determine the threshold value in non-parametric condition of unlabeled data. The LSTM-KDE approach later is evaluated with numerical data as well as Tennessee Eastman process plant dataset. The performance also was compared to Principal Component Analysis (PCA), Local outlier factor (LOF) and Auto-associative Kernel Regression (AAKR) to further examine the LSTM-KDE performance. The experimental results indicate that the LSTM-KDE fault detection approach has better learning performance and accuracy compared to other approaches

Extraction of River Water Bodies Based on ICESat-2 Photon Classification

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.

Spatial and temporal ecology of Cerdocyon thous: a mesopredator canid coping with habitat loss, fragmentation, and chronic anthropogenic disturbances

ContextHuman activities are reducing the amount and quality of natural landscapes. Understanding how such changes affect the spatial and temporal ecology of mammal populations will enable us to foresee how communities will be structured in the Anthropocene.ObjectivesHere, we evaluated how the occupancy, intensity of use, and activity patterns of the mesopredator canid Cerdocyon thous are affected by topographic variation, habitat amount, fragmentation and chronic anthropogenic disturbances.MethodsCamera trapping data were obtained between May and September 2014 in 179 sampling points within ten priority areas for conservation in a seasonally dry tropical forest (Caatinga) in Brazil, totaling an effort of 6,701 camera.days. We use occupancy models for analyzed occupancy, generalized linear models (GLM) for the intensity of use and kernel density curve for activity pattern.ResultsCerdoyon thous benefited from human disturbance, showing greater occupancy and intensity of use near anthropogenic habitats, fragmentation, human density and cattle density. Moreover, temporal analyses showed that it modulates its daily activity according to habitat amount, human density, cattle density and altitude. However, C. thous tends to avoid areas with excess of fire, logging and infrastructures.ConclusionsThese results allow us to understand the mechanisms that contribute to the dominance of generalist mesocarnivores in human-dominated environments.

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

Transmission dynamics of tuberculosis in a high-burden area of China: An 8-year population-based study using whole genome sequencing

ObjectivesThis study investigated the transmission patterns of tuberculosis (TB) and its associated risk factors in Hunan province to inform the development of prevention and control strategies in the region. MethodsAn 8-year retrospective population-based genomic epidemiological study was conducted. Genomic clusters were defined using distance thresholds of 12-single-nucletide-polymorphisms. Risk factors associated with TB transmission were analyzed using logistic regression model. Kernel Density analysis was used to locate hotspots where transmission occurred. ResultsAmong 2649 TB cases included in this study, 275 clusters were identified, with an overall clustering rate of 24.7% (654/2649). Nearly 95% (620/654) of clustered strains were isolated from the same county. Of the 275 clusters, 23 (8.4%, 23/275) had differences in drug-resistant profiles, with FQs resistance mutations occurring most frequently (52.2%, 12/23). Multivariate analysis identified male TB patients, those aged 30-60 years, ethnic minorities, nonfarmers, retreated TB patients, and individuals infected with MDR/RR-TB as independent risk factors for TB transmission (P < 0.05). Kernel density analysis showed that among the 5 drug-resistant surveillance sites, Leiyang had the highest clustering rate, followed by Yongshun, Qidong, Hecheng, and Taojiang. ConclusionRecent transmission in the region is predominantly occurring within counties. The risk factors related to TB transmission and the hotspots where transmission occurs can provide a scientific basis for the formulation of targeted TB prevention and control strategies.

Study on the Evolution of Spatiotemporal Dynamics and Regional Differences in the Development of Digital Agriculture in China

In the current study, an index system for digital agriculture growth was constructed. The index encompasses six key dimensions, namely production, operation, service, management, sustainability, and digital information infrastructure. Data from 30 Chinese provinces between 2011 and 2020 were collected and analyzed using the entropy method, Moran index, Dagum’s Gini coefficient, and the kernel density estimate. An in-depth analysis of the development level and spatial patterns, dynamic evolution and intra- and inter-regional differences in China (i.e., eastern, western, and central regions) was conducted. From the result, an overall growing trend of digital agriculture in China was observed, with a relatively more advanced status in the eastern region. A positive spatial dependence, showing a “high-high” and “low-low” (HH, LL) trend, was obtained. However, the regional spatial dependence has generally weakened since 2019. The intra-regional differences were large in western and eastern areas, while the greatest inter-regional differences were unveiled between western and eastern regions. The country’s overall differences mainly stemmed from inter-regional differences. The overall kernel density curves moved to the right over time, showing a trend of a gradual rise in digital agricultural growth, accompanied by a polarization pattern in the western region.

PLS-based hellinger distance method for fault detection in chemical engineering systems

Fault detection is vital in chemical engineering systems to maintain operational efficiency, product quality, and safety through timely identification and correction of deviations from expected behavior. Although partial least squares (PLS) has proven effective in monitoring due to its ability to handle highly correlated variables, traditional detection metrics of PLS may fail to identify small abnormal changes as they rely solely on recent observations. This paper integrates PLS modeling framework with Hellinger Distance (HD)-based fault detection index to overcome the limitations of conventional detection metrics. The utilization of HD is motivated by its sensitivity to quantifying any dissimilarity between distributions, which makes it well-suited for detecting small deviations in process behavior. The HD-based index will be computed between the residuals obtained from the model in the offline stage and the online stage. The HD metric involves careful inspection and comparison of the residuals, which enables it to capture the sensitive details in the data, thus, enhancing the detection of faults. For increased flexibility, kernel density estimation is employed to establish the reference threshold of the PLS-HD approach. The performance of this approach will be evaluated using data from simulated Continuous Stirred-Tank Heater (CSTH) and Continuous Stirred-Tank Reactor (CSTR) processes, by considering various fault types such as bias, freezing, and sensor drift faults. The results demonstrate the superior performance of the proposed PLS-HD approach compared to conventional PLS monitoring methods.

Kernel density estimation for a stochastic process with values in a Riemannian manifold

This paper is related to the issue of the density estimation of observations with values in a Riemannian submanifold. In this context, Henry and Rodriguez ((2009), ‘Kernel Density Estimation on Riemannian Manifolds: Asymptotic Results’, Journal of Mathematical Imaging and Vision, 34, 235–239) proposed a kernel density estimator for independent data. We investigate here the behaviour of Pelletier's estimator when the observations are generated from a strictly stationary α-mixing process with values in this submanifold. Our study encompasses both pointwise and uniform analyses of the weak and strong consistency of the estimator. Specifically, we give the rate of convergence in terms of mean square error, probability, and almost sure convergence (a.s.). We also give a central-limit theorem and illustrate our proposal through some simulations and a real data application.

Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis

The emergent role of nucleic acid-based biomarkers—microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [https://github.com/ABHAYHBB/gQuant-Tool]."

Assessment of Green Innovation Efficiency in Chinese Industrial Enterprises Based on an Improved Relational Two-Stage DEA Approach: Regional Disparities and Convergence Analysis

Industrial enterprises are characterized by significant energy consumption and high emissions. Therefore, the implementation of green innovation by these enterprises is beneficial for promoting sustainable economic development and safeguarding the ecological environment. In this study, a relational two-stage DEA model containing shared inputs and undesired outputs is constructed to evaluate and decompose the green innovation efficiency (GIE) of Chinese industrial enterprises across 30 provinces from 2012 to 2021. Since the objective function of this model is nonlinear, a heuristic search method is employed for its resolution. On the basis of efficiency evaluation, the Gini coefficient, kernel density estimation, and convergence analysis are further employed to investigate the regional disparities and convergence properties in the two-stage efficiency of green innovation. Our findings are as follows: (1) The average GIE of Chinese industrial enterprises demonstrates a fluctuating upward trajectory, with significant regional disparities observed between provinces. (2) Regional disparities in R&amp;D efficiency (RDE) and achievement conversion efficiency (ACE) have diminished in all regions. Super-variable density and interregional differences serve as the primary sources of regional disparities in RDE and ACE, respectively. (3) The presence of absolute and conditional convergence in RDE and ACE is observed across all regions. To improve the GIE of Chinese industrial enterprises, it is imperative to emphasize the heterogeneous impact of economic levels, industrial structure, and the degree of openness across various regions and stages of green innovation.

An urban building use identification framework based on integrated remote sensing and social sensing data with spatial constraints

ABSTRACT Building use identification is crucial in urban planning and management. Current identification methods often rely on a single data source and neglect spatial proximity. In this paper, we propose a stepwise urban building use identification framework that integrates remote sensing and social sensing data with spatial constraints based on the association of buildings with Point of Interest (POI), Area of Interest (AOI) and remote sensing data. First, the study data are preprocessed using geometric correction and POI and AOI reclassification. Then, we identify buildings with the quantitative-density index of the POIs as well as the spatial relationships between the AOIs and the buildings. Next, we generate Traffic Analysis Zones (TAZs) from road network data and utilize the similarity in physical features of buildings from remote sensing data to identify building use within spatial constraints. Finally, POI kernel density estimation is used to determine the semantic features of the buildings, and the similarity of the features between the buildings is utilized to identify the remaining buildings. The specificity of our proposed framework lies not only in the combination of multiple source data at the building-level but also in the introduction of the spatial relationships of AOIs and spatial constraints. Shenyang is selected as an example. The proposed framework identifies buildings as commercial, residential, industrial, public service and scenic spots. The accuracy assessment indicates that the proposed framework performs well, with an Overall Accuracy (OA) of 87.1% and a kappa coefficient (kappa) of 73.4%. The results of the comparison experiments show that the consideration of spatial constraints and the integration of multiple data sources help to improve the accuracy of building use identification. The proposed framework provides a new tool for better identification of urban building use, and the generated data are suitable for in-depth analyses such as building-level urban heat islands.

Research on the coupling measurement of medical education and health resource allocation under the background of healthy China

Objective To analyze the coupling and coordination level of medical education and health resource allocation in China, and to provide scientific basis for promoting the high-quality development of medical education and the efficient allocation of health resources.MethodsBased on the panel data from 2011 to 2021, the coupling coordination degree model was used to measure the coupling coordination index of medical education and health resources in China. The spatial auto-correlation model was used to analyze the development status and distribution characteristics of the coupling coordination degree of the two systems. The kernel density estimation method was used to analyze the dynamic evolution trend of the coupling coordination of the two systems. The QR quantile regression model was used to explore the key factors affecting the coupling coordination degree of the two systems.ResultsDuring the observation period, the coupling coordination degree of the two systems increased from 0.393 to 0.465, with a growth rate of 18.3%. The coupling coordination degree between regions gradually decreased in the eastern-central and eastern-western regions, and there were still large differences between the central and western regions. The coupling coordination degree of the two systems in the region was significantly different in the eastern and western regions, and the central region was relatively similar. There is a positive spatial correlation between the provinces, and 25.81% of the provinces have transitions. Finally, the number of points in the first and third quadrants is higher than that in the second and fourth quadrants. In the process of dynamic distribution, the degree of polarization of the coupling coordination degree curve of the two systems is gradually weakened. Per capita GDP, residents ' income difference and population size are the positive and significant factors driving the coupling and coordinated development of the two systems.ConclusionThe coupling and coordination degree of the two systems of medical education and health resource allocation showed a stable upward trend during the observation period, and the global spatial positive correlation also gradually increased, showing the spatial agglomeration characteristics of ' high-high agglomeration ' and ' low-low agglomeration '. The spatial difference of coupling coordination degree shows a shrinking trend and develops towards equalization. The coupling coordination degree of the two systems is affected by social, economic and demographic factors to varying degrees. Therefore, it is necessary to innovate the coordinated development mechanism of the two systems, promote the two-way flow of medical education and health resource allocation in talents, technology and other elements, and then promote the coupling and coordinated development of the two systems.

Integrated study on the conservation of ecocultural heritage in the Tiantai Mountain area, China

This study deeply analyzes the ecocultural heritage of the Tiantai Mountain area in Eastern China, assesses its current status and explores effective mechanisms for its conservation and construction. Employing remote sensing technology, kernel density analysis and Minimum Cumulative Resistance Model, the study comprehensively evaluated the environmental characteristics of the region and its role in historical and modern conservation efforts. The results demonstrate the unique distribution patterns of ecocultural heritage in various conservation zones, such as restoration, controlled construction and core conservation zone. Furthermore, the study highlights the importance of religious beliefs and clan systems in the preservation of traditional knowledge and practices, with significant impacts of local community participation on conservation strategies. This research provides new theoretical and practical support for the implemention of ecocultural heritage conservation and development within religiously significant areas, highlights the need for conservation measures and synergistic strategies between ecological and cultural practices.