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From five parameters at the state level: percentage of Catholic adherents (CATH-ADH), average number of adherents per congregation (ADH/CON), number of congregations per capita (CON/POP) and voting percentages from the 2020 election for Trump (TRUMP) and Biden (BIDEN), we obtain six Pearson correlation coefficients at the national and regional levels. These yield a significant positive correlation between CATH-ADH and BIDEN, as well as for ADH/CON and BIDEN, while the opposite is true for TRUMP. Conversely, there is a negative correlation between CON/POP and BIDEN and a positive one for TRUMP. The interplay between these, as well as important geographical and confessional spatial autocorrelations measured using Moran’s index, show the influence of religion on the 2020 election among two main lines: Catholic support for Biden and Trump support from atomized yet smaller congregations. [ABSTRACT FROM AUTHOR]

While the influence of dispersal on ecological selection is the subject of intense research, we still lack a thorough understanding of how ecological selection operates to favour distinct dispersal strategies in metacommunities. To address this issue, we developed a model framework in which species with distinct quantitative dispersal traits that govern the three stages of dispersal—departure, movement and settlement—compete under different ecological contexts. The model identified three primary dispersal strategies (referred to as nomadic, homebody and habitat-sorting) that consistently dominated metacommunities owing to the interplay of spatiotemporal environmental variation and different types of competitive interactions. We outlined the key characteristics of each strategy and formulated theoretical predictions regarding the abiotic and biotic conditions under which each strategy is more likely to prevail in metacommunities. By presenting our results as relationships between dispersal traits and well-known ecological gradients (e.g. seasonality), we were able to contrast our theoretical findings with previous empirical research. Our model demonstrates how landscape environmental characteristics and competitive interactions at the intra- and interspecific levels can interact to favour distinct multivariate and context-dependent dispersal strategies in metacommunities. This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'. [ABSTRACT FROM AUTHOR]

[Objective] The purposes of this study are to explore the spatiotemporal evolution characteristics and influencing factors of land-use carbon emissions at different spatial scales, and to provide a reference basis for formulating reasonable carbon emission reduction measures and realize the goal of 'dual carbon'. [Methods] The land-use carbon emissions in Guangzhou in 2000, 2005, 2010, 2015 and 2020 were measured at township scale and 1 km grid scale. GIS spatial analysis, spatial autocorrelation. exploratory regression and geographically weighted regression (GWR) model were used to explore the spatiotemporal evolution characteristics and influencing factors of land-use carbon emissions in Guangzhou. [Results] (1) At the two scales, the areas of medium and high carbon emission areas were gradually increasing, while the areas of negative carbon emission areas and low carbon emission areas were gradually decreasing. (2) The land-use carbon emissions in the region generally had strong positive spatial agglomeration characteristics, and the degree of agglomeration tended to strengthen. The high high agglomeration and low-low agglomeration units were dominant, and the distribution of low high agglomeration areas were sporadic and did not change much. (3) The local spatial agglomeration pattern also involved some changes; the distribution of high-high agglomeration areas was expanding gradually; the distribution of low low agglomeration areas were shrinking at township scale, but expanding slightly at 1 km grid scale. (4) Land use structure, investment level and population size had positive effects on land-use carbon emissions, while population density of 95.32% of township units had inhibitory effects. [Conclusion The level of land-use carbon emissions Guangzhou gradually increased and showed positive spatial agglomeration characteristics on the whole, and the impacts of various influencing factors on it were significantly different. The results of this study can provide the reference for Guangzhou to formulate precise carbon emission reduction measures. [ABSTRACT FROM AUTHOR]

The impact of green investment on green ecological development is still controversial. Based on the provincial panel data from 2009 to 2020, this study uses a spatial econometric model to test the impact of green investment on green ecological development and regional differences. Results found that the green investment significantly improved the local green ecological level and had less impact on the green ecological level of surrounding areas. Green innovation significantly affects green investment to promote green ecology in local and surrounding areas, inhibiting the development of green ecology. Environmental regulation improved the effect of local green investment to promote green innovation and has an inhibitory effect on surrounding areas. Moreover, environmental regulation inhibits the effect of local green investment to promote a green ecology. Finally, the promotion effect of green investment on local green ecology is only significant in the eastern region. The effect in the western region is not significant. This study has important practical significance for promoting China's green ecological development. [ABSTRACT FROM AUTHOR]

Copyright of Resources Development & Market is the property of Sichuan Resource Development & Market Magazine Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales. [ABSTRACT FROM AUTHOR]

A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation.We interpolated demographic parameters at unsampled locations using parameter estimates from data‐informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data ('cheaper' and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave‐One‐Out Cross‐Validation (LOOCV) technique. Conventional IPMs and state‐space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage‐Grouse (Centrocercus urophasianus; sage‐grouse) populations located in Nevada, U.S.A. during 2013–2021.SIIPMs outperformed conventional IPMs when fit to data possessing moderate‐to‐high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (λ). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67 km) to sampling locations. Under low autocorrelation‐near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (λ; IPM comparison) and 54.0% (λ; SSM comparison) more precise than contemporary models.Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long‐term monitoring data exists. Specific to sage‐grouse, these results support previous conclusions of broad‐scale spatial autocorrelation in population dynamics and a reproductive‐survival trade‐off previously documented at smaller scales. [ABSTRACT FROM AUTHOR]

In recent years, the geographical factor has been increasingly studied in electoral processes. The purpose of this study is to identify and explore the spatial pattern of votes in the first round of Afghanistan's presidential election in 2019. The votes of 14 election candidates at the district level was collected from Independent Election Commission (IEC). To measure spatial autocorrelation and identify spatial patterns, global and local Moran Index were used. The results of Moran's index are positive for the votes of all candidates, which indicates the spatial cluster pattern. Then clusters and outliers were investigated according to the ethnic map. The results show that the ethnicity factor is important in the formation of spatial clusters of electoral votes in Afghanistan. Spatial approach increases our understanding of the important processes that can be caused by the context conditions in each area. Thus, this research provides an accurate scientific support for further research in the field of political science, political and social geography of Afghanistan. [ABSTRACT FROM AUTHOR]

Introduction: Urban contraction brings about certain impacts on the advancement of urbanization. Methods: This paper explores the coupling coordination (CCD) and geographical distribution of land urbanization (LU) and population urbanization (PU) in Jiangsu Province through a CCD model and a spatial autocorrelation model from the perspective of shrinking cities. The Panel Vector Autoregression (PVAR) model is constructed to deeply investigate the complex interaction between the PU-LU in 13 cities, 2007-2020. Results: The study found that the PU-LU CCD in each of the above-mentioned cities shows a synergistic incremental evolution during the study period in terms of time series. In addition, in terms of spatial characteristics, the CCD of PU and LU shows significant positive global spatial autocorrelation, and the CCD of cities with high population size growth is much larger than that of cities with continuous population size. Last, there is an interactive response relationship between PU and LU. PU influences LU, however, PU itself is influenced by its own system's internal structure. The impact of PU on LU shows a positive cumulative effect of the "inverted U shape." Discussion: Furthermore, this paper proposes that policies be created to ensure the coordinated growth of PU-LU based on the differences in resource endowments of cities with 3 types of urban shrinkage. The link between floating population and construction land for cities should be established through enhancing the extent of intensive land use and reforming household registration. [ABSTRACT FROM AUTHOR]

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C= 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis. [ABSTRACT FROM AUTHOR]

Objective To analyze the epidemiological characteristics, spatial clustering, and influencing factors of tuberculosis recurrence in Kashgar within three years after successful treatment to provide a scientific basis for tuberculosis recurrence management. Methods Taking the tuberculosis patients successfully treated in Kashgar from 2013 to 2020 as the research object, we observed the recurrence epidemic characteristics, conducted spatial analysis to understand the recurrence clustering, and used the Cox regression model to explore the influencing factors. Results Among the 104 899 objects,14 180 experienced recurrences within three years, with a cumulative recurrence rate of 13.52%. Spatial analysis showed a positive spatial correlation of tuberculosis recurrence, with recurrence hotspots mainly concentrated in Shache, Yingjisha, and Bachu counties. The Cox model showed that male (aHR=1.155, 95%CI: 1.118-1.195), Uyghur (aHR=2.186, 95%CI: 1.786-2.681), 45-<60 age groups (aHR=1.631, 95%CI: 1.061-2.507) and ≥60 age groups (aHR=1.782, 95%CI: 1.160-2.738), farmers and herdsmen (aHR=1.104, 95%CI: 1.039-1.173), re-treatment (aHR=1.086, 95%CI: 1.036-1.138), positive aetiology result (aHR=1.377, 95%CI: 1.324-1.432), positive sputum smear after two months of treatment (aHR=1.699, 95%CI: 1.501-1.924), total delay ≥30 days (aHR=1.129, 95%CI: 1.088-1.171) and living in hotspots (aHR=1.470, 95%CI: 1.413-1.530) were independent risk factors for recurrence after successful treatment. Conclusion Priority should be given to improving the monitoring and management of patients in hotspot regions and those at high risk of relapse after treatment to ensure early detection of TB relapse and standardized care. [ABSTRACT FROM AUTHOR]

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height. [ABSTRACT FROM AUTHOR]

The principle of independence is a fundamental yet often disregarded assumption in statistical inference. It is observed that the implications of correlations, if not considered, can lead to a conservative estimation of Type I error in the presence of positive linear correlations when utilizing the Kolmogorov-Smirnov (KS) test. Conversely, negative linear correlations may engender a liberal estimation of Type I error. To address the impact of spatial autocorrelation in the analysis of Positron Emission Tomography (PET) images, we have proposed an innovative methodology to reconstruct a grid map of human heart scans using spherical coordinates. We have examined the distribution of the KS test statistic under spatial autocorrelation through Monte Carlo (MC) simulation and have introduced a KS test with a spatial adjustment. The newly proposed KS test with spatial adjustment demonstrates a controlled Type I error and power that is not inferior when compared to the original KS test. This suggests its potential utility in the analysis of spatially autocorrelated data. [ABSTRACT FROM AUTHOR]

An important quantitative geography application area derives from the periodically repeating apportionment process of redrawing legislative district boundaries to guarantee equal voter representation through identical or equivalent population counts, a situation confronting the U.S. Supreme Court with gerrymandering rulings on a decennial basis. This remains a small geographic sample size domain, with states having between one and fifty-two (i.e., California) congressional precincts in 2023. Texas constitutes an informative case study because its sample size of thirty-eight exceeds the frequently touted minimum of thirty, and its 2020 increase was two seats, more than any other state, some of which experienced decreases. Hence, although all fifty states also have undergone internal population redistribution, the Texas redrawn boundaries offer the greatest opportunity for dramatic borderline changes. This article appraises outcomes of this Texas gain within its legal constraints as they pertain to a novel uniform distribution explicitly embracing spatial autocorrelation, a fundamental georeferenced data property. Its name is sui-uniform to differentiate it from the prevalent auto- model convention; this article furnishes its first empirical application. After accounting for spatial autocorrelation, the inferential conclusion for Texas is as preferred: Superpopulation geotagged voting-age populations conform to a uniform distribution, whereas racial and ethnic subpopulations do not. [ABSTRACT FROM AUTHOR]

Development of Depok City has led to changes in land use, with the emergence of sprawl such as ribbon development and leapfrog development. Negative impacts of urban sprawl in Depok City include traffic congestion, floods, air pollution, and water contamination. The aim of this research is to explore the relationship between urban sprawl and the environmental carrying capacity conditions in the water sector in Depok City. The analytical methods used in this study are spatial distribution analysis, spatial autocorrelation, and Spearman's rank correlation. The research findings indicate that the urban sprawl development occurring in the city of Depok over the past 10 years shows indications of a decreasing trend in urban sprawl, with the discovery of the Low Sprawl index dominating 84.43% during the second period, from 2018 to 2023. However, during the period of 2013 to 2018, findings show that the Strong Sprawl tendency was more dominant, likely leading towards the outskirts of Depok city due to relatively inexpensive land prices and unimplemented zoning regulations. Furthermore, the typology of urban sprawl that occurred experienced a shift from a tendency towards ribbon development to leapfrog sprawl, which attempts to fill the empty spaces resulting from ribbon development during the period from 2013 to 2018. On the other hand, the urban sprawl development during the period from 2018 to 2023 indicates a significant relationship with the Environmental Carrying Capacity Status/D3TLH - Water Sector, albeit with a weak negative direction. These indications also suggest that neither non-sprawl, low sprawl, nor strong sprawl are the main and strong determinants of exceeding 81% of the Environmental Carrying Capacity Status/D3TLH - Water Sector in Depok city. [ABSTRACT FROM AUTHOR]

Objectives To explore the spatial distribution characteristics, trend changes, and spatial clustering of esophageal cancer among residents in China at the county (city, district) scale, a spatial epidemiological approach was used, with the aim of providing localized evidence for the prevention and treatment of esophageal cancer in China. Methods The data source was the incidence (crude rate) and mortality (crude rate) of esophageal cancer from 2005 to 2016 in the 2008-2019 edition of China Cancer Registration Annual Report published by the National Cancer Center. The Joinpoint model was used for time trend analysis. The tumor registration area in 2016 was selected as the study area for spatial feature analysis, with a total of 487 counties (cities and districts), covering 27.6% of the national population. Spatial autocorrelation analysis was performed to reveal spatial distribution characteristics by using Arcgis 10.6 software, and spatial scanning statistics was used to analyze spatial clustering characteristics by using SaTScan 9.5 software. The log-likelihood ratio (LLR) and relative risk (RR) were calculated in different windows, and the region with the largest LLR value represented the most likely cluster. Results From 2005 to 2016, the incidence and mortality rate of esophageal cancer in China showed a trend of increasing at first and then decreasing. The incidence and mortality rate of esophageal cancer in 2016 were characterized by spatial positive correlation. High incidence and high mortality were mainly concentrated in the areas through which the Huaihe River flowed. The primary clusters (taking high incidence rate as an example LLR=6 374.41, RR=2.37, P＜0.001) were mainly distributed in Jiangsu, Anhui and Shandong in eastern China and eastern Henan and southern Hebei in central China, and secondary clusters (taking high incidence rate as an example LLR=1 971.19, RR=1.91, P＜0.001) in Gansu, Ningxia Hui Autonomous Region, Shaanxi, Sichuan and other central and western regions. Conclusions The incidence and mortality of esophageal cancer in China have decreased since 2010. The disease burden of esophageal cancer has obvious spatial differences, and measures should be taken according to local conditions in high-risk cluster areas such as the Huaihe River basin. [ABSTRACT FROM AUTHOR]

Based on 20 autosomal microsatellite markers, the results of analysis of the genetic structure and spatial autocorrelation in the populations of grey wolf (Canis lupus Linnaeus, 1758) on the territory of the Russian Federation and the Republic of Kazakhstan are described. Using molecular markers, hidden genealogical patterns are revealed, which have the most pronounced effect on distances up to 150 km but may be traced over much wider spaces, up to 700–1600 km. Our research has shown that identification and exclusion of closely related genotypes has limited effect on the results of the analysis of intrapopulation genetic diversity. Meanwhile, this procedure is recommended for a more adequate assessment of the population structure since it allows for streamlining some statistical approaches. Thus, the obtained data demonstrate the integrating effect of natal migration, which works against the differential effect of philopatry. Finally, it is also shown that the exclusion of closely related individuals can lead to lower values of genetic distances between populations. [ABSTRACT FROM AUTHOR]

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary productivity (GPP) is a critical part of land carbon fluxes. Accurately quantifying GPP in terrestrial ecosystems and understanding its spatiotemporal dynamics are essential for assessing the capability of vegetation to absorb carbon from the atmosphere. Nevertheless, traditional remote sensing estimation models often require complex parameters and data inputs, and they do not account for spatial effects resulting from the distribution of monitoring sites. This can lead to biased parameter estimation and unstable results. To address these challenges, we have raised a spatial autocorrelation light gradient boosting machine model (SA-LGBM) to enhance GPP estimation. SA-LGBM combines reflectance information from remote sensing observations with eigenvector spatial filtering (ESF) methods to create a set of variables that capture continuous spatiotemporal variations in plant functional types and GPP. SA-LGBM demonstrates promising results when compared to existing GPP products. With the inclusion of eigenvectors, we observed an 8.5% increase in R2 and a 20.8% decrease in RMSE. Furthermore, the residuals of the model became more random, reducing the inherent spatial effects within them. In summary, SA-LGBM represents the first attempt to quantify the impact of spatial autocorrelation and addresses the limitations of underestimation present in existing GPP products. Moreover, SA-LGBM exhibits favorable applicability across various vegetation types. [ABSTRACT FROM AUTHOR]

Urban green infrastructure construction and economic growth are necessary ways and important supports to promote sustainable development. Exploring their coupling coordination relationship is important for achieving high-quality economic development. This study uses the entropy method, coupling coordination degree, kernel density estimation, the Dagum Gini coefficient, and spatial autocorrelation to explore the spatial-temporal pattern characteristics and coupling coordination relationship between green infrastructure construction and economic development for 273 cities in Chinese mainland in 2010–2020. The results show that the level of China's green infrastructure construction and economic development gradually increased during 2010–2020. There were significant regional differences in space, exhibiting a decreasing spatial pattern from east to west. The coupling coordination degree was constantly improving. The overall Gini coefficient shows an upward trend. Among the four regions, eastern China has the greatest intraregional variation. The uneven level of coupled coordination is mainly from interregional variation. There was a significant positive spatial autocorrelation relationship, and cities that had a higher degree of coupling coordination tended to agglomeration development. Meanwhile, it also had certain spatial heterogeneity. China's entire level of coupling coordination degree still has much room for improvement. The study is of great significance in reducing disparities between regions and strengthening regional spatial coordination development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Geary's c is a prominent measure of spatial autocorrelation in univariate spatial data. It uses a weighted sum of squared differences. This paper develops Geary's c for multivariate spatial data. It can describe the similarity/discrepancy between vectors of observations at different vertices/spatial units by a weighted sum of the squared Euclidean norm of the vector differences. It is thus a natural extension of the univariate Geary's c. This paper also develops a local version of it. We then establish their properties. [ABSTRACT FROM AUTHOR]

The detrimental effects of accelerated urbanization on ecosystem services underscore the necessity of investigating the interactive coercion between the two. This paper employs multi-source data to analyze the urbanization subsystems and modified equivalent factors in order to calculate the urbanization level (UL) and ecosystem service value (ESV) of the Poyang Lake urban agglomeration (PYLUA) from 2005 to 2020 at the administrative, watershed, and grid scales. Bivariate spatial autocorrelation, multi-scale geographically weighted regression (MGWR), and others are applied to explore the interrelationships and impact effects between the two and to conduct zoning management studies. The results indicate that (1) the UL has significantly increased from 2005 to 2020, with a marginal expansion in its spatial distribution, while the ESV shows a generally smooth trend, with high-value and low-value areas present around Poyang Lake and in the metropolitan area, respectively; (2) the UL and ESV are negatively correlated at all the scales, and this negative effect has increased significantly over time; (3) in the OLS model, only land urbanization and population urbanization negatively affect the ESV at the 1% level of significance, while in the MGWR, all the urbanization subsystems negatively affect the ESV at the 1% level of significance and have their own optimal scales; and (4) the UL and ESV are interrelated and divide the PYLUA into five zones: Coordinated Development Zone, Co-Loss Zone, Development Lag Zone, Ecological Loss Zone and Development Potential Zone. These zones identify areas where negative effects are more pronounced and propose corresponding management strategies for each subzone. These results can serve as a foundation for coordinating regional urbanization and preserving the environment. [ABSTRACT FROM AUTHOR]

Background: In 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. Methods: The 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. Results: From 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. Conclusions: From 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. [ABSTRACT FROM AUTHOR]

[Objective]To study the impact of different spatial weight matrices on the spatial correlation model of land use. [Method]Based on the land-use variables of the administrative village of Yuanmou County, this paper first analyzed the spatial autocorrelation of land-use types and their driving factors under different weight matrices, and established an algorithm based on the queen weight matrix, rook weight matrix and distance threshold weight matrix. Spatial autoregressive model for the evolution of the spatio-temporal pattern of cultivated land. Parameters such as goodness of fit, maximum likelihood logarithm, Akaike information criterion, Schwartz information criterion, number of influence factors and spatial autocorrelation of model residuals were selected as model evaluation indicators. The differences between the classic linear regression model, the spatial lag model and the spatial error model were compared and analyzed. [Result]In 2018, land use types and land use drivers in Yuanmou County showed a strong positive spatial correlation under different spatial weight matrices. By comparing the classic linear regression model, spatial lag model and spatial error model of the three weight matrices, it was found that in the same spatial autocorrelation model, the fitting effect of the spatial autoregressive model based on the distance threshold weight matrix was better; based on the same space, the weight matrix and the spatial error model fit better. [Conclusion]The spatial correlation was related to the spatial weight matrix. The spatial error model based on the distance threshold matrix had the best fitting degree and the strongest interpretation ability, which could better represent the spatial evolution of the land use pattern in the mountainous plateau of Yunnan. [ABSTRACT FROM AUTHOR]

A notable finding is that Kerala’s capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas. [ABSTRACT FROM AUTHOR]

The Shibing Karst constitutes a pivotal component of the "South China Karst," and its ecosystem health integrity crucially influences the Outstanding Universal Value (OUV) of the corresponding Natural World Heritage (NWH). Consequently, robust ecosystem health assessment (EHA) is imperative for the judicious conservation and management of this heritage, as well as for the sustainable progression of the region. This research assessed the health of the Shibing Karst ecosystem from 2004 to 2020 by employing changes in landscape patterns through the Vigor-Organization-Resilience-Ecosystem Services (VORS) model. Spatial autocorrelation was employed to discern the spatial coherence and evolutionary patterns of ecosystem health, whereas a geo-detector ascertained the pivotal determinants impacting regional ecosystem vitality. The findings revealed that: (1) The landscape patterns distribution in the study area exhibited considerable constancy, primarily comprising forest land, with a rising trajectory in construction land and water, juxtaposed with a recession in shrubland, grassland, paddy land, and dryland expanses. (2) From 2004 to 2020, the ecosystem of the study area maintained its health and remained stable, with mean values of 0.8303, 0.7689, 0.6976, and 0.7824, respectively, showing an evolutionary trend of an initial downtrend trend followed by an upswing, with 2016 marking a pivotal juncture. (3) Spatial clustering analysis highlighted significant clustering characteristics of ecosystem health, with a nominal decrease in the Global Moran's I index from 0.666 to 0.665, which is indicative of a subtle decrease in clustering over time. High-high clustering areas were predominantly located within the World Heritage Site (WHS), while low-low clustering areas were mainly distributed in the southeastern part of buffer zone. (4) Land use and cover change (LUCC) and Ecosystem Services (ESs) were identified as the primary indexes of EHA, with Ecosystem Resilience (ER), Ecosystem Vigor (EV), and Ecosystem Organization (EO) exerting relatively mild influences. This study provides a scientific framework for policymakers in local governance to devise strategies for ecosystem conservation and management, enhances the analytical perspective on the integrity and conservation of Karst Natural World Heritage (KNWH). [ABSTRACT FROM AUTHOR]

Hungary is one of the five countries in the World which were most affected by the pandemic in terms of registered COVID-19 mortality up to 2023. Our research aims to identify those socioeconomic variables that explain the geographical distribution of registered district-level COVID-19 mortality in Hungary. Using OLS and spatial regression, we found that the higher share of elderly people and respiratory death rate were associated with a more severe mortality burden. Educational attainment was negatively associated with COVID-19 mortality. Variables related to healthcare access were not found to be significantly associated with district-level COVID-19 mortality. Our results indicated that the spatial term of COVID-19 mortality is significant. Positive spatial autocorrelation can be observed in some less developed districts and a few inner peripheral areas where COVID-19 mortality was relatively high, and relatively developed areas like the agglomeration area of the capital in which COVID-19 mortality was low. [ABSTRACT FROM AUTHOR]

Territorial economic and social disparities remain a major problem for the European Union today, especially in Eastern Europe. The aim of this study is to analyse the impact of the economic and social shocks of the 2000s (the economic and financial crisis of 2008-09 and the COVID-19 pandemic) on the economies of four Central Eastern European countries (Czech Republic, Slovakia, Poland, Hungary). The study presents county-level differences in gross value added with classical descriptive statistics, inequality indices, convergence analyses and spatial autocorrelation. The results show that the impact of the shocks of the 2000s varies across counties, which led to different paths of recovery. Spatial autocorrelation is significant, but patterns remain stable through the period of exogenous shocks. [ABSTRACT FROM AUTHOR]

Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM2.5, PM10, O3, NO2, SO2, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran's I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM2.5 and PM10 in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O3. Seasonal peaks for O3 occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies. [ABSTRACT FROM AUTHOR]

We leverage a national panel of US municipalities to show that behavioral finance helps explain the number of months of expenses that municipalities save in cash and investment reserves. We hypothesize that municipal managers may be using numerical anchoring based on historical values to target the number of months of savings to hold and that they may also be engaged in social learning to target months of savings based on the behavior of neighboring municipalities. We test for these effects by combining two innovative techniques, a two‐stage regression designed to test for anchoring of present financial values based on theoretically unimportant historical values, and a measure of the spatial autocorrelation of savings to test for social learning. The results suggest that, in deciding how much to save, municipal managers are influenced by the levels of savings they held in the past and the savings levels of their neighbors, and that they underreact to changes in theoretically relevant economic fundamentals. Further tests also suggest that the smallest cities by population are more influenced by the behavior of their neighbors than their past savings, whereas the largest cities show the opposite result, effectively choosing themselves as their own role models. [ABSTRACT FROM AUTHOR]

• Considerable differences between the countries in terms of road safety performance were observed. • A robust linear relationship was identified between road fatality risk indices in the US and England. • A significant and non-linear association was detected between the regional motorization rate and fatality risk. • Hot spots regarding road fatalities and fatality risk indices were identified. • The fatality per number of registered motor vehicle rate provides more accurate results in regional studies. Introduction: There is regional diversity inside countries regarding road safety indices (RSIs), and countries rarely have been compared based on these indicators. Thus, regional RSIs of England, the United States, Egypt, and Turkey were evaluated. Regional data were collected from the statistical center of each country. The adopted regional RSIs include road fatalities, health risk (HR) or fatalities per population, and traffic risk (TR) or fatalities per number of vehicles. The associations between variables were examined using correlation and regression analysis. The spatial distributions of subdivisions were evaluated using Moran's I, the local Moran index. Results : Considerable differences between the countries were observed, including differences in the spatial distribution of regions and associations between RSIs. Significant relationships were detected between road fatality, population, and the number of motor vehicles. Higher exposure rates mean higher fatalities in regions. A robust linear relationship between the HR and TR indices was identified in developed countries. There is a nonlinear and significant association between motorization rates and TR indices of regions, and fatality risk decreases as the motorization rate increases. There is a considerable gap between developed and developing countries regarding regional RSIs, and the transferability of road safety models from one country to another is challenging. Huge hotspots regarding RSIs were observed in Turkey and the United States. The locations of hot spots in terms of the risk indices were identical in the developed countries. [ABSTRACT FROM AUTHOR]

Copyright of Economía, Sociedad y Territorio is the property of El Colegio Mexiquense and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article analyzes spatial autocorrelations and the formation of clusters of exports, based on research and development (R&D) intensity in Portugal. The central idea is that exports show relative interdependence and spillover effects among nearby regions and a direct relationship with R&D expenditures. It adopts the new taxonomy of the OECD, separating exports by manufacturing and non-manufacturing activities by technological intensity. Methodologically, it applied exploratory spatial data analysis (ESDA), using global Moran's index and LISA. The results showed the presence of positive spatial autocorrelation of exports and the formation of a cluster of the high–high type for the Área Metropolitana do Porto and Região de Aveiro. There is also a positive spatial autocorrelation for exports associated with R&D expenditures as well as the formation of a regional cluster with high–high pattern for the Região de Aveiro. This outcome can be explained, in part, by nationally and internationally recognized universities and research centers surrounding the region, favoring knowledge spillovers across the regions. [ABSTRACT FROM AUTHOR]

Copyright of Continue Education Research is the property of Harbin Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Species richness, defined as the number of species within a specific area, exhibits spatial variation at most spatial scales. The drivers behind these patterns, especially for marine invertebrates, remain a subject of ongoing debate. In this study, we explore the determinants of species richness for shallow-water gorgonians (Cnidaria: Anthozoa) in the Eastern Tropical Pacific, utilizing a comprehensive database of geographic occurrences and generalized linear mixed models analyses to investigate the influence of seven environmental variables in the context of four theoretical frameworks (Species-Energy, Environmental Heterogeneity, Climate Seasonality, and Past Climatic Stability hypotheses). Our methodology aims to account for potential spatial autocorrelation, enhancing the reliability of results. Our findings indicate a robust positive association between sea bottom temperature and species richness, supporting the Species-Energy hypothesis. Temperature, a key factor for marine biodiversity, likely enhances gorgonians’ metabolic, ecological, and mutation rates. Topographic ruggedness, climatic stability, and light availability were significant predictors of species richness, though their effects varied between subsets of analyzed taxa. Consequently, although our analysis supports the Environmental Heterogeneity and Past Climatic Stability hypotheses, their importance appears secondary. Overall, this research highlights the critical role of contemporary factors (predominantly temperature and topographic ruggedness) and historical factors, particularly climatic stability over the last 20,000 years, in influencing the distribution of gorgonian species richness in the Eastern Tropical Pacific. [ABSTRACT FROM AUTHOR]

With the advancement of the rural revitalization strategy, counties have emerged as vital platforms for supporting rural revitalization, underscoring the increasing importance of sustainable development in their economies. It is imperative to evaluate the sustainable development potential of county economies and implement precise measures accordingly. This paper selects relevant economic development indicators from 2016 to 2020 for 76 counties in Gansu Province, constructs an evaluation system for assessing their sustainable development potential, and employs methods such as the Theil index, spatial autocorrelation, principal component analysis, and cluster grouping classification. The evaluation considers three aspects: the county economic development gap, the development potential score, and cluster analysis. The findings reveal that the economic development of Gansu Province's counties exhibits spatial characteristics of multi-point flowering and scattered distribution, with relatively weak communication and development between surrounding counties. Over the five-year period, significant disparities in economic development among Gansu Province's counties are evident. Liangzhou District ranks highest in county economic development, while other counties with robust economic development are primarily concentrated in the Lanzhou–Baiyin metropolitan area, the Tianshui metropolitan area, the west entrance of the Hexi Corridor economic belt, and the Qingyang–Pingliang group area. These counties exhibit a spatial pattern of agglomeration towards the central and eastern regions. Moreover, some counties in the southeastern region of Longdong demonstrate promising economic development potential, forming an initial scale of contiguous development. This indicates favorable prospects for sustainable development in the region's county economy. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Genotypic distributions affect the persistence of coral populations, and mapping these distributions is important for population management. Many studies have examined genetic connectivity among sites, but within-site spatial genotypic patterns based on clonal distribution and kinship are poorly understood. Such patterns are an important index for understanding the potential for population recovery at small spatial scales. Here, we studied within-reef spatial genotypic distributions and clonality of a broadcast-spawning coral, Galaxea fascicularis, by using mitochondrial DNA (mtDNA) and 15 nuclear microsatellite markers. Specimens were collected at shallow reefs (< 3 m) at four sites in the Ryukyu Archipelago, Japan. Among 289 colonies analyzed, we detected two common mtDNA types (mt-L, 174 colonies; mt-S, 113 colonies) and one rare type (mt-L + , 2 colonies). The proportion of duplicate clonal colonies differed across sites and reef topographies; the maximum distance between clonemates was approximately 120 m. Pairwise kinship among colonies tended to decrease with distance at the ramet level (i.e., including clonal replicates), but not at the genet level. Ramet-level kinship varied among sites rather than between mtDNA types. Genet-level kinship (i.e., excluding clonal replicates) was similar among sites. These results for clonality and kinship suggest that both sexual and asexual reproduction contribute to population recovery after disturbances and maintain genetic diversity in local populations. However, the extent of sexual and asexual reproduction differs across sites. Our results will contribute to more effective management of marine reserves by emphasizing the importance of clonal distributions and genetic kinship at each reef site. [ABSTRACT FROM AUTHOR]

As a carrier of cultural characteristics of historic districts, the soundscape has unique advantages in shaping regional cultural personality, and mastering its spatiotemporal characteristics is crucial for preserving soundscape heritage with natural and humanistic environments as its kernel. Focusing on the Three Square and Seven Alleys historic and cultural district in Fuzhou, this paper analyzes the spatial and temporal patterns of the physical acoustic indicators of the soundscape, the spatial dependence of the sound source harmony, and the spatial relationship between the two. It was found that the physical acoustic indicators showed dynamic changes in spatial and temporal scales and reflect specific human activity and behavioral patterns; sound source harmony showed spatial autocorrelation in both global and local models, with prominent spatial characteristics; and the physical acoustic indicators may negatively affect soundscape perception. The study emphasizes the importance of the regional cultural connotation of soundscape in urban planning. It provides a scientific basis for the planning, designing, and managing of soundscape resources in historic and cultural districts and world heritage sites. [ABSTRACT FROM AUTHOR]

The authors present an analysis of the correlation between demographic and territorial indicators and greenhouse gas (GHG) emissions, emphasizing the spatial aspect using statistical methods. Particular attention is given to the application of correlation techniques, considering the spatial correlation between the involved variables, such as demographic, territorial, and environmental indicators. The demographic data include factors such as population, demographic distribution, and population density; territorial indicators include land use, particularly settlements, and road soil occupancy. The aims of this study are as follows: (1) to identify the direct relationships between these variables and emissions; (2) to evaluate the spatial dependence between geographical entities; and (3) to contribute to generating a deeper understanding of the phenomena under examination. Using spatial autocorrelation analysis, our study aims to provide a comprehensive framework of the territorial dynamics that influence the quantity of emissions. This approach can contribute to formulating more targeted environmental policies, considering the spatial nuances that characterize the relationships between demographics, territory, and GHGs. The outcome of this research is the identification of a direct formula to obtain greenhouse gas emissions from data about land use starting from the case study of Pavia Province in Italy. In the paper, the authors highlight different methodologies to compare land use and GHG emissions to select the most feasible correlation formula. The proposed procedure has been tested and can be used to promote awareness of the spatial dimension in the analysis of complex interactions between anthropogenic factors and environmental impacts. [ABSTRACT FROM AUTHOR]

Hungary is one of the five countries in the World which were most affected by the pandemic in terms of registered COVID-19 mortality up to 2023. Our research aims to identify those socioeconomic variables that explain the geographical distribution of registered district-level COVID-19 mortality in Hungary. Using OLS and spatial regression, we found that the higher share of elderly people and respiratory death rate were associated with a more severe mortality burden. Educational attainment was negatively associated with COVID-19 mortality. Variables related to healthcare access were not found to be significantly associated with district-level COVID-19 mortality. Our results indicated that the spatial term of COVID-19 mortality is significant. Positive spatial autocorrelation can be observed in some less developed districts and a few inner peripheral areas where COVID-19 mortality was relatively high, and relatively developed areas like the agglomeration area of the capital in which COVID-19 mortality was low.

The changes in the landscape structure within resource-dependent urban areas is influenced by a confluence of factors, potentially resulting in a decreased landscape ecological quality and a heightened ecological vulnerability. Landscape ecological quality directly influences the flow and transmission of ecological functions, rendering it a pivotal consideration in the development of landscape ecological networks. In this study, an investigation was conducted utilizing land-use data spanning from 2005 to 2020, encompassing the city of Linfen in Shanxi Province, China. A landscape ecological risk assessment model was formulated to analyze the spatiotemporal attributes of landscape ecological risk during this timeframe. Acknowledging the underlying connection between ecosystem resilience and landscape ecological quality, the 2020 landscape ecological risk assessment served as the basis for the extraction of ecological source areas using the Minimum Spanning Tree with Path Algorithm (MSPA), and the corridors were established using the Minimum Cumulative Resistance (MCR) model. This framework facilitated the construction of a landscape ecological network, and the complex network analysis was employed to scrutinize the network’s topological properties. The regions within the study area exhibiting high and extremely high ecological risk displayed an initial increase followed by a subsequent decrease, culminating in an overall reduction in risk levels. The areas surrounding urban development and mining activities displayed relatively heightened risk levels, while the regions characterized by forests and grasslands experienced relatively lower risks. The shifts in risk within the study area were predominantly attributed to various factors, including mining activities, urban expansion, governmental policies, alterations in land-use types, and village migrations. Through the optimization of the landscape ecological network using edge addition strategies and a subsequent comparison of network robustness pre- and post-optimization, it was discerned that the edge addition strategy notably improved the efficiency of energy transfer and interconnectivity among network nodes. This ecological risk assessment model, in conjunction with the development of a prospective ecological network, lends a valuable theoretical support to the enhancement of landscape structure within resource-dependent urban areas and the establishment of comprehensive ecological security systems.

Abstract Background In 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. Methods The 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. Results From 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. Conclusions From 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.

Abstract The Shibing Karst constitutes a pivotal component of the "South China Karst," and its ecosystem health integrity crucially influences the Outstanding Universal Value (OUV) of the corresponding Natural World Heritage (NWH). Consequently, robust ecosystem health assessment (EHA) is imperative for the judicious conservation and management of this heritage, as well as for the sustainable progression of the region. This research assessed the health of the Shibing Karst ecosystem from 2004 to 2020 by employing changes in landscape patterns through the Vigor-Organization-Resilience-Ecosystem Services (VORS) model. Spatial autocorrelation was employed to discern the spatial coherence and evolutionary patterns of ecosystem health, whereas a geo-detector ascertained the pivotal determinants impacting regional ecosystem vitality. The findings revealed that: (1) The landscape patterns distribution in the study area exhibited considerable constancy, primarily comprising forest land, with a rising trajectory in construction land and water, juxtaposed with a recession in shrubland, grassland, paddy land, and dryland expanses. (2) From 2004 to 2020, the ecosystem of the study area maintained its health and remained stable, with mean values of 0.8303, 0.7689, 0.6976, and 0.7824, respectively, showing an evolutionary trend of an initial downtrend trend followed by an upswing, with 2016 marking a pivotal juncture. (3) Spatial clustering analysis highlighted significant clustering characteristics of ecosystem health, with a nominal decrease in the Global Moran's I index from 0.666 to 0.665, which is indicative of a subtle decrease in clustering over time. High-high clustering areas were predominantly located within the World Heritage Site (WHS), while low-low clustering areas were mainly distributed in the southeastern part of buffer zone. (4) Land use and cover change (LUCC) and Ecosystem Services (ESs) were identified as the primary indexes of EHA, with Ecosystem Resilience (ER), Ecosystem Vigor (EV), and Ecosystem Organization (EO) exerting relatively mild influences. This study provides a scientific framework for policymakers in local governance to devise strategies for ecosystem conservation and management, enhances the analytical perspective on the integrity and conservation of Karst Natural World Heritage (KNWH).

The world today is experiencing rapid urbanization, leading to prominent urban ecological problems. Evaluating urban ecological quality is crucial for monitoring environment-centered initiatives. The Akkulam-Veli Lake Basin, in Thiruvananthapuram, Kerala, India, is threatened by rapid urbanization. The present study proposes a comprehensive evaluation of the ecological quality by the Remote-Sensing Ecological Index (RSEI), based on the four indexes: greenness (NDVI), wetness (WET), dryness (NDSBI), and heat (LST), from 2000 to 2023. The interrelationship between RSEI and land use has been studied to properly analyze human activities’ effects on the study region’s ecological quality. Results indicated that the mean values of RSEI experienced a drastic decline from 0.733 in 2000 to 0.645 in 2015, and further declined to 0.611 in 2023, indicating an overall deterioration in the status of ecological quality in the Akkulam-Veli Lake Basin. Moreover, Moran’s I index of 0.556 in 2000, 0.554 in 2015, and 0.552 in 2023 show that the sample RSEI values have a strong positive autocorrelation with adjacent values in the study area. The study provides evidence for the need for systematic urban planning and serves as a scientific reference for policymakers to manage the ecological qulaity of urban areas effectively.

Background Health care workers are vital service providers in primary healthcare, playing a crucial role in safeguarding public health, disease prevention, and chronic disease management. At present, research on healthcare resources mainly focuses on the quantity and structure, and there are few studies on the spatial distribution characteristics of healthcare resources. Objectives To analyze the spatial development trend of healthcare human resources in primary healthcare institutions nationwide, and to provide reference for optimizing the spatial allocation of healthcare resources and upgrading the level of primary healthcare services. Methods The data for this study were obtained during 2013-2017 China Health and Family Planning Statistical Yearbook, the 2018-2022 China Health and Wellness Statistical Yearbook, and the annual data of the National Bureau of Statistics (NBS) by province and the Ministry of Civil Affairs of China. From February to June 2023, the health density index (HRDI) was used to comprehensively reflect the demographic and geographic distribution of primary health care in China from 2012 to 2021, spatial autocorrelation analysis was applied to calculate the global Moran's I index to characterize the spatial distribution of primary health care resources in China from 2012 to 2021 analysis, and calculate the local Getis's G index to analyze the hotspots of primary care resource allocation in China from 2012 to 2021. Results The number of practicing physicians and nurses per 1 000 population at the grassroots level has increased in all regions of China, and the ratio of healthcare to nursing is still low. The high HRDI values for the number of practicing (assistant) physicians and nurses were in the eastern region, while the western region had lower HRDI values. The global Moran's I index for the number of practicing (assistant) physicians turned from negative to positive in 2016. The Moran's I index for nurses was always negative, but the overall trend was gradually approaching 0. Western regions such as Xinjiang and Xizang have low health density indices and are cold spots for resource distribution. Conclusion The allocation of medical and nursing resources to primary health care organizations has been increasing year by year, but there are obvious differences in allocation between provinces. The spatial correlation between physicians has gradually increased, and nurses have become more dispersed in their spatial distribution. It is recommended that the reserve of primary care physicians and nurses be strengthened, and that the ratio and collaboration between physicians and nurses be improved; that the planning of health care resources take into account both demographic and geographic configurations, and that Internet-based health care be promoted to improve the accessibility of health care services; and that the investment of resources in central and western China be increased, and that the construction of health care organizations be pushed forward in order to enhance the capacity of grass-roots-level services.