Introduction: Environmental and worker health issues causing from pesticide exposure remain a significant concern in the agricultural sector. Some research has found that longer exposure to pesticides can probably cause neurological disorders. This study aims to analyze the relationship between exposure intensity, individual characteristics, and the incidence of neurological disorders with the spatial distribution pattern in the farming region. Methods: This research is a cross-sectional study with a spatial approach were carried out involving 75 chili farmers in the Bandungan region. Information about frequency exposed to pesticides, individual characteristics, and symptoms of neurological disorder was compiled by structured questionnaires, observation and analyzed based on the Romberg test. The data was analyzed using statistical and spatial tests with Moran's Index. Results and Discussion: The data indicated that most of the farmers (at least 50%) showed symptoms of neurological problems. The highest number of cases was found among farmers who were older than 50 (66.7%), with an overweight body mass index (BMI) (80%), and incomplete use of safety equipment (59%). Autocorrelation spatial showed the result of a notable significance clustering of neurological disorders (Moran’s I Z-score = 3.94, p ≤ 0.01), with the high-risk location in Kenteng Village. Based on bivariate tests, it showed that the increase in risk of neurological disorders is influenced by increasing age, BMI, and the complete use of safety equipment. Conclusion: Therefore, efforts to promote, collaborate, and implement the use of complete PPE are needed to reduce the health risks of farmers exposed to pesticides.

The progress and spatiotemporal heterogeneity evaluation of China's ecological civilization

Delineating major and trace element hotspots in groundwater through spatial autocorrelations and a double-clustering approach: The role of geologic and anthropogenic factors.

Air quality is a core element concerning public health and sustainable development. As the backbone of new quality productive forces, the digital economy is quietly emerging as a significant driver of air quality improvement. Based on prefectural-level city data in China from 2020–2023, and after confirming the spatial correlation between the digital economy and air quality, this paper first employs bivariate Moran's index to examine the spatial association characteristics between them. Subsequently, variance inflation factors are used to diagnose multicollinearity among annual variables. By constructing an ordinary least squares regression model, we investigate the overall impact effect. We then introduce a geographically weighted regression model to identify and estimate the spatial heterogeneity inherent in their relationship. Furthermore, the influence coefficient of the digital economy is utilized to analyze the spatial pattern and evolutionary trend of its impact on air quality. The results indicate a significant spatial dependence between the development of the digital economy and urban air quality, with this correlation pattern exhibiting a directional shift during the sample period. The geographically weighted regression model demonstrates superior goodness-of-fit and overall model adaptability compared to the ordinary least squares model, underscoring the robust spatial non-stationarity of the digital economy's impact on air quality. Further analysis reveals a clear East-West differentiation pattern in the spatial distribution of the digital economy's influence coefficients. This study not only provides spatial empirical support for exploring the complex relationship between the digital economy and environmental quality but also offers certain policy reference value for promoting regionally coordinated emission reductions and accelerating the green digitalization process.

ABSTRACT Based on provincial panel data from China spanning the period 2015 to 2022, this study examines the spatial spillover effects of local government debt (LGD) on total factor productivity (TFP), including its heterogeneity and underlying mechanisms. The Moran index results indicate a positive spatial correlation of TFP across different provinces in China. The heterogeneity analysis reveals that the impact and spatial effects of LGD on TFP vary across regions and debt types. Specifically, both total local government debt and explicit debt exert a positive influence on TFP within the region and exhibit positive spatial spillover effects on neighboring provinces. In contrast, implicit debt has an adverse effect, diminishing TFP in the region and its neighboring provinces. Mechanism analysis shows that an increase in explicit debt significantly enhances TFP through public investment, although its effects are constrained by capital misallocation. The expansion of implicit debt pushes up land prices and suppresses TFP but also boosts TFP through employment creation. The findings have important practical implications for preventing and resolving LGD risks and promoting high-quality economic development.

The coupling and coordination relationship between provincial carbon emissions and new quality productivity in China is a key path to achieve the "dual carbon" goals and promote high-quality development in a coordinated manner. Based on panel data from 30 provinces in China from 2012 to 2022, a coupling coordination degree model, spatial autocorrelation analysis, and β convergence model were constructed to systematically measure the dynamic coordination and regional convergence characteristics of carbon emission intensity and new quality productivity. The results showed that: ① The national coupling coordination degree increased from 0.529 to 0.664, upgrading from "barely coordinated" to "primary coordinated, " with an average annual growth rate of 2.55%. ② The spatial differentiation presented a pattern of "high in the southeast and low in the northwest, " and the global Moran index verified a significant positive spatial correlation. The coupling coordination degree H-H agglomeration area expanded from 7 to 11 provinces, reflecting the radiation effect of the Yangtze River Delta and Pearl River Delta extending to the central and western regions, while the northwest and northeast L-L agglomeration areas are still constrained by "high carbon lock-in" and ecological vulnerability. ③ There were absolute β and conditional β convergences in the coupling coordination degree, and the convergence speed of underdeveloped provinces was significantly faster than that of developed provinces. The advantage of latecomers and the diffusion effect of technology drove the narrowing of regional differences. Based on this, it is recommended to strengthen the global technology sharing network, promote industrial structure transformation through differentiation, and improve cross regional ecological compensation mechanisms to promote the coordinated transition of low-carbon development and new quality productivity.

To analyze the spatial and spatiotemporal distribution of infant mortality in the state of Sergipe from 2019 to 2023. Ecological study. This ecological study used publicly available data on infant deaths in Sergipe between 2019 and 2023, obtained from the Mortality Information System. Variables related to the child, mother, and healthcare factors were considered. Bayesian mortality rates were calculated and mapped across municipalities. Moran's Index was applied to assess spatial correlation. Between 2019 and 2023, Sergipe recorded 2547 infant deaths. The leading causes were perinatal conditions (Chapter XVI) and abnormal findings in examinations (Chapter XVIII), predominantly in the northern region. Higher mortality was observed among Black female infants with very low or low birth weight, as well as clusters of deaths among adolescent mothers in the Itabaiana region, and adult or late-age mothers in Propriá, which also showed higher mortality among preterm infants. The municipalities of Propriá and Nossa Senhora do Socorro reported more deaths associated with cesarean deliveries. Aracaju, São Cristóvão, and Nossa Senhora do Socorro formed high-mortality clusters, with a risk 641.77 times greater; conversely, Nossa Senhora da Glória, Lagarto, and Estância exhibited a 66% lower risk. Infant mortality in Sergipe is unevenly distributed, concentrated in high-risk clusters, and reflects regional disparities as well as specific maternal and neonatal factors.

Abstract In the analysis of large spatial datasets, identifying and treating spatial outliers is essential for accurately interpreting geographical phenomena. While spatial correlation measures, particularly Local Indicators of Spatial Association (LISA), are widely used to detect spatial patterns, the presence of abnormal observations may frequently distort the landscape and conceal critical spatial relationships. Traditional influence function (IF) methodologies, commonly used in statistical analysis to measure the impact of individual observations on statistical measures, are not applicable in the spatial context because in this case the influence is determined not only by the value observed in a spatial unit, but also by its location, its connections with neighboring regions, and by the values observed in neighboring observations. In this paper, we introduce a local version of the influence function (called “Local Influence Function”, or LIF for short) that accounts for spatial dependence in robustness analysis. In the paper, we first derive the analytical expression of the local influence function and we suggest a way of summarizing it in a single parameter. We then show, through the analysis of both simulated and real-world datasets, how the LIF provides a more nuanced and accurate description of the effects of spatial outliers in the analysis of spatial correlation and we discuss its interpretation in comparison with the local Moran index.

ABSTRACT New quality productivity not only helps to optimize the allocation of resources among regions, but also promotes economic development. Therefore, studying the influence of new quality productivity on the spatial spillover effect of resource mismatch is of great significance for formulating scientific and reasonable regional economic policies and promoting high-quality economic development. This paper analyses regional differences between new quality productive forces and resource misallocation using the Moran Index and Trend Surface Analysis. A spatial Durbin model is constructed to further explore the spatial effects of new quality productive forces on resource allocation. The conclusions are as follows: (1) From 2012 to 2022, the new quality productive forces show an obvious growth trend, while the resource misallocation exhibits an overall decreasing trend. (2) Spatially, new quality productive forces are highest in the Central region, followed by the South. Resource misallocation is highest in the West, followed by the East, and higher in the Central and North than the South, with a pronounced gap between the East and West. (3) There is a significant negative correlation between new quality productive forces and resource misallocation. Additionally, there is a significant spatial spillover effect where new quality unproductive forces inhibit resource misallocation.

Spatial Analysis in Surgical Research.

Risk classification and zoning management of heavy metal pollution in paddy soils: A model coupling soil health and source apportionment.

Under the impetus of the digital economy, the digital transformation of industrial chains and supply chains has become inevitable. Data assets effectively enhance the operational efficiency of industrial chains and supply chains, digital technologies can effectively enhance the competitiveness of enterprises in industrial chains and supply chains, and digital technologies effectively promote the transformation and upgrading of industrial chains and supply chains. This paper constructs an evaluation indicator system from four dimensions: research and design modernization, production and manufacturing modernization, logistics and transportation modernization, and sales and marketing modernization. Based on data from 30 provinces in China from 2015 to 2024, the paper uses the entropy method and spatial Moran index to analyze the digital economic development levels and spatial correlations of the 30 provinces, and examines the impact of the digital economy on the modernization level of the housing industrial chain and supply chain, as well as its underlying mechanisms. The research results show that the development of the digital economy exhibits a stable growth trend, while the modernization level of the housing industry chain and supply chain shows an overall fluctuating upward trend, with the overall modernization level of the housing industry chain and supply chain first declining and then rising. Additionally, the promotional effect of the digital economy exhibits significant spatial heterogeneity, with the development level of the digital economy generally showing a single threshold effect on the modernization level of the housing industry chain and supply chain, and a double threshold effect in western regions.

Currently, there are no lead-contamination models to describe the locations of aggregation/non-aggregation sites (i.e., hot-and-cold spots) in Hillsborough County, Florida, USA. We generated a Global Moran's index (I) of spatial autocorrelation to identify hot-and-cold spots of lead contamination in Hillsborough County. The data used were based on lead sampling of water sources within the internal environments of elementary, middle, and high schools. A second-order eigenfunction eigen-decomposition algorithm embedded in a regional convolutional neural network (R-CNN) machine learning [ML] in a geo-spatial artificial intelligence [geo-AI] smartphone application yielded a model that identified Kingswood Elementary School as the most clustered geographic location with the highest lead contamination concentration levels. The spatial autocorrelation model also identified Essrig Elementary School as the least clustered geographic capture point. The Moran's I diagnostic summary plot revealed a final Z-score of 8.347 and a p-value of 0.000. Mapping hotspots of lead concentration using a second-order eigenfunction eigen-decomposition algorithm within an instantaneous R-CNN, ML, geo-AI, iOS pipeline can allow school administrative boards and policymakers to allocate resources to at-risk areas with lead contamination.

This study investigates natural radioactivity levels of 238U, 232Th, and 40K in soil samples from the North Oil Company (NOC) area in Kirkuk, Iraq, and assesses associated radiological hazards. The activity concentrations were measured using gamma spectrometry with NaI(Tl) and were found to be 28.51 ± 1.42, 20.22 ± 1.01, and 331.51 ± 16.73Bq kg⁻¹ for 238U, 232Th, and 40K, respectively, which are all lower than the global average. The radiological hazard indices include radium equivalent activity (Raeq), absorbed dose rate (ADR), annual effective dose equivalent (AEDE), and excess lifetime cancer risk (ELCR). All indices were within acceptable limits. The geo-accumulation index (Igeo) indicated little contamination, with most values negative, indicating little or no anthropogenic effect. The kriging interpolation indicated localized hotspots in the spatial distribution, whereas Moran's index test indicated random spatial patterns. The Monte Carlo Simulation indicated uncertainties with the ELCR, but the average value of 1.88 × 10- 4 is considered slightly elevated. The multivariate statistical analyses, including Spearman correlation and cluster analysis, showed links between radionuclides and risk indices. The study showed that the current radiological risks in the NOC area are low, but require monitoring due to oil extraction activities potentially impacting people. This study provides fundamental knowledge that can be applied to evaluating the environment and public health aspects surrounding oil production.

Digital village, green agriculture, and farmers’ well-being are three subsystems of the current giant system of rural social development in China, and their coupled and coordinated development serves as a crucial prerequisite for promoting the comprehensive revitalization of China’s rural areas and the modernization of agriculture and rural areas. Drawing on the complex adaptive system theory, this paper establishes a coupling coordination degree analysis model for the digital village-green agriculture-farmers’ well-being system and develops an evaluation index system for these three subsystems. Based on panel data from 30 Chinese provinces spanning 2011 to 2021, this study constructs an evaluation index system for Digital Village, green agriculture, and farmers’ well-being. Employing the entropy weight method, coupling coordination degree model, convergence coefficient, Theil index, and Moran index, it analyzes the comprehensive development level of each system, along with the convergence effects, regional disparities, and spatial characteristics of their coupled and coordinated development. The findings indicate: (1) The comprehensive development level index values of the three subsystems are on the rise, and their coupling degree, coordination degree, and coupling coordination degree are generally improving, achieving a transformation from moderate imbalance to intermediate coordination. (2) The coordinated development of the coupling among the three systems exhibits clear convergence characteristics. (3) The coordinated development of coupling demonstrates significant regional disparities, with intra-regional differences being the primary source. Over time, the Yellow River Basin has contributed the most to overall disparities. (4) The coordinated development of the coupling among the three systems shows significant spatial autocorrelation, with increasing autocorrelation intensity. Notably, the lower reaches of the Yangtze River Economic Belt and the middle and lower reaches of the Yellow River Basin are high-value agglomeration areas for coupling coordinated development.

Homicidal violence in urban environments constitutes a critical challenge in cities marked by socioeconomic inequality and territorial segregation. This study examines the geographical distribution of intentional homicides in Guayaquil during 2023 using Geographic Information Systems (GIS) and spatial analysis techniques, specifically the Kernel Density method and Global Moran's Index. The hypothesis posits that homicides tend to concentrate in areas with greater levels of economic and social inequality, evidencing the influence of structural factors on the distribution of urban violence. The results confirm this hypothesis, revealing a marked concentration of homicides in the northern and southern zones of the city, inhabited by low and lower-middle income populations. Guayaquil registered a rate of 89.87 homicides per 100,000 inhabitants, considered epidemic according to the United Nations threshold, with an 884.3% increase compared to 2014. Significant positive spatial autocorrelation (Moran's Index ≈ 0.45, p < 0.05) confirms that homicides cluster persistently in certain urban sectors. The analysis revealed the high prevalence of firearm use and a majority impact on individuals aged between 25 and 30 years. These findings demonstrate that the spatial pattern of violence is not random but rather the result of structural inequalities and territorial segregation, supporting theories of social exclusion, anomie, and the production of urban space. The study highlights the urgent need for comprehensive public policies that articulate urban planning, social inclusion, and citizen security, whilst demonstrating the utility of geospatial analysis for guiding territorialized interventions.

<h2>ABSTRACT</h2><h3>Background</h3> Pulmonary Tuberculosis (PTB) remains a major cause of morbidity in India despite progress in TB elimination, accounting for roughly 25% of global TB cases. Ongoing spatial and demographic disparities hinder further reduction. The study aims to assess PTB syndemic profiles, spatial distribution, and persistent hotspots in a high-burden Indian district from 2019 to 2023 using geospatial analytics to inform precision public health policies. <h3>Methods</h3> A retrospective cross-section analysis of 10,201 PTB cases in Mysuru district used ArcGIS and Google Earth Pro to examine point density by age, gender, HIV status, and diabetes. Spatial autocorrelation (Moran's I, Getis-Ord Gi*) identified hotspot clusters, while chi-square tests evaluated demographics and comorbidity trends. <h3>Results</h3> Between 2019 and 2023, PTB cases declined by 7.7% (from 2,245 to 2,029). Cases among individuals aged 0-18 and 19-44 fell by 22% and 22.3%, respectively. Both male and female cases dropped by about 9.5%, while diabetes cases rose by 10% and non-diabetes cases fell by 6.5%. HIV-positive cases declined by 52.6%. A Moran's Index of 0.381799, z-score of 31.45, and p-value <0.001 indicate strong, statistically significant spatial clustering. <h3>Conclusion</h3> Despite the overall decline in disease burden, persistent urban PTB clusters continue to affect the elderly and individuals with diabetes. While TB-HIV comorbidity has significantly decreased, the enduring Diabetes-TB overlap highlights the need for integrated, geospatially targeted interventions and continuous GIS-based surveillance to address high-risk clusters and advance TB elimination in urban areas.

This paper establishes a high-quality development (HQD) evaluation index system for the emergency industry to analyze its spatio-temporal evolution. The entropy weighting method is applied to comprehensively assess the development level of China’s emergency industry during the observation period. Results indicate that the overall HQD index follows an upward trajectory, although it remains relatively low (rising from 0.211 to 0.255). The 2020 regional analysis highlights a hierarchical pattern in industrial development: coastal regions (0.321) &gt; inland regions (0.246) &gt; border regions (0.153). The overall Gini coefficient has remained stable at 0.22. Specifically, coastal areas (0.19) and inland areas (0.16) exhibit notable disparities, whereas border regions show minimal variation (0.10). The Gini coefficient between coastal and Border regions increased from 0.31 to 0.36, whereas that between coastal and inland areas declined after 2011, falling from 0.29 to 0.23. Regional variation, accounting for 59% of the total, emerges as the primary source of inequality. The Moran index identifies a significant spatial autocorrelation, indicating clear spatial self-correlation in the development levels of China’s emergency industry. Kernel density analysis further illustrates that the national development level has steadily improved. However, the industry as a whole does not exhibit σ-convergence. The novelty of this study lies in constructing an HQD index system for China’s emergency industry and employing multiple analytical methods to examine its spatiotemporal evolution.

This study analyses intra-urban disparities in the ownership, use and perception of long-lasting insecticide-treated mosquito nets (LLINs) in the fight against malaria in two neighbourhoods with different typologies in the city of Korhogo: Soba and Natio-kobadara. Using geolocated household surveys and spatial analyses (Moran's Index and LISA), we identified areas of vulnerability where effective protection remains insufficient despite good reported coverage. The results reveal a discrepancy between ownership, habitual use and actual use, and show that underprotected households are mainly located near the low-lying areas of the neighbourhoods. These observations highlight the need for a territorialized control strategy integrating behavioural communication and geographic targeting.

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