Spatial Cluster Analysis Research Articles

Spatiotemporal analysis of mosquito-borne infections and mosquito vectors in mainland Portugal

BackgroundThe incidence of mosquito-borne infections has increased worldwide. Mainland Portugal’s characteristics might favour the (re)emergence of mosquito-borne diseases. This study aimed to characterize the spatial distribution of vectors and notification rates of imported cases of mosquito-borne infections in mainland Portugal and demarcate the areas where these geographies overlap.MethodsWe used data from imported cases of malaria, dengue and Zika from 2009 to 2019, alongside data on the presence of mosquitoes capable of potentially transmitting these diseases at the municipality level (2009–2018). This data was provided by the National Epidemiological Surveillance System and Regional Health Administrations, based on reports from the Vector Surveillance Network. While the mosquitoes in question do not currently transmit these diseases, they have the potential to do so if there is a significant increase in pathogen circulation. A spatial cluster analysis was performed using the univariate Local Moran Index, the Bivariate Moran Local Index and the Mann-Kendall method.ResultsWe found significant spatial variability in both notification rates of imported mosquito-borne infections and the distribution of competent mosquito species. We identified clusters of simultaneous high concentrations of vectors and imported cases of malaria in Condeixa-a-Nova (Coimbra), Cuba (Beja), Santiago do Cacém (Setúbal), Albufeira and São Brás de Alportel (Faro), most located on the Southern coast of Portugal. For dengue, we detected clusters of simultaneous high concentrations of vectors and imported cases in Paredes, in the Northern region, and Faro, on the southern coast. For Zika, no clusters were identified.ConclusionThis study identified areas with high notification rates of imported cases and the presence of competent vectors. Surveillance, control, and awareness efforts are essential, as these areas may present higher risks for local transmission in the future if ecological conditions remain or become suitable, potentially evolving into foci for disease transmission.

Historical Redlining and Community-Reported Housing Quality: A Spatial Analysis.

Historical redlining, a racially discriminatory practice implemented by the USgovernment in the 1930s, has been associated with present-day environmental outcomes. However, there is limited research examining the relationship between historical redlining and contemporary housing quality. The objective of the present study was to investigate the relationship between historical redlining and contemporary housing quality in Atlanta, Georgia. Spatial patterns of housing code violation complaints from 2015 to 2019 were examined using point-pattern and spatial cluster analyses. We used Bayesian hierarchical models, accounting for spatial autocorrelation, to estimate associations between historical redlining and housing complaints, after adjusting for contemporary neighborhood characteristics, such as poverty, median structure age, vacant and renter-occupied properties, and residential racial segregation. A total of 48,626 housing code violation complaints were reported during the study period, including 6531 complaints deemed "hazardous." Historical redlining was a statistically significant predictor of housing complaints. We observed a 167% increased risk (IRR = 2.67, 95% confidence interval = 1.49, 4.77) of housing complaints for historically redlined neighborhoods compared to neighborhoods historically graded as "best" or "still desirable," after adjusting for neighborhood characteristics. Redlined neighborhoods also had an increased risk of "hazardous" housing complaints (IRR = 1.94, 95% confidence interval = 1.11, 3.40), after adjusting for contemporary neighborhood characteristics. Historically redlined neighborhoods exhibited disproportionately higher rates of housing code violation complaints. Spatial analysis of housing code violation complaints can provide insights into housing quality and inform interventions targeted at addressing the environmental legacy of structural racism.

Regional disparities of full pentavalent vaccine uptake and the determinants in Ethiopia: Mapping and spatial analysis using the EDHS data.

The full pentavalent (DPT-HepB-Hib) vaccination is the main strategy to prevent five communicable diseases in early childhood, especially in countries with huge communicable disease burdens like Ethiopia. Exploring spatial distributions and determinants of full pentavalent vaccination status in minor ecological areas in Ethiopia is crucial for creating targeted immunization campaigns and monitoring the advancement of accomplishing sustainable development goals. This study aimed to investigate the spatial disparities and determinants of full pentavalent vaccination among 12-23-month-old children in Ethiopia. The data on pentavalent vaccine uptake was found in the Ethiopian Health and Demographic Survey (EDHS, 2019). A two-stage cluster sampling method was applied to collect the EDHS data. The enumeration area was the primary sample unit while the household served as the secondary sampling unit. The geographical variations of full pentavalent vaccine uptake were explored using Quantum Geographic Information System (QGIS) software. The significant predictors of full pentavalent vaccination were identified using a simple logistic regression model through R version 4.1 software. The national full pentavalent vaccine uptake was 59.2%. The spatial distribution of full pentavalent vaccine uptake was not uniform in Ethiopia. Spatial cluster analysis revealed that most of low coverage regions for full pentavalent vaccine uptake were Afar, Somali, and Harari. The regions with the highest and lowest rates of vaccine uptake were Tigray and Harari region, respectively. Maternal age of 35-49 years (AOR = 3.42; 95% CI: 1.99, 5.87), and 25-34 years (AOR = 1.55; 95% CI: 1.17, 2.19), primary education attended (AOR = 1.51; 95%CI: 1.07, 2.11), richness wealth index (AOR = 1.96; 95% CI: 1.40, 2.75), birth order of 1-3 (AOR = 1.88; 95% CI: 1.19, 2.96), and delivery in the health facility (AOR = 3.41: 95% CI: 2.52, 4.61) were the determinants of full pentavalent vaccine uptake in Ethiopia. Ethiopia's full pentavalent vaccine uptake was far lower than the global target. Older maternal age, maternal education, wealth index, birth order, and giving birth in a health facility were the determinants of full pentavalent vaccine uptake. Special attention should be given to Afar, Somali, and Harari regions, to strengthen the vaccine uptake. Moreover, improved socioeconomic status and getting maternal health services during delivery are necessary to enhance vaccine uptake.

Spatial clustering of seasonal sea ice of Hudson Bay, Canada, 1971–2018

ABSTRACT The spatial variation in seasonal sea ice in Hudson Bay is examined using spatial clustering analyses. For the period 1971 to 2018, a time series of sea-ice breakup and freeze-up dates and ice-free season length at thirty-six grid locations is generated from sea-ice charts derived from satellites and other data. These data are analyzed spatially using three different clustering techniques. Overall, the three methods revealed a northeast/southwest axis in sea-ice behavior consistent with a well-documented cyclonic current flow and wind regime in Hudson Bay. The methods did differ in assigning grid locations to clusters with the greatest consistency for breakup behavior and in the northern region of the Bay across all three metrics. The greatest variability occurred with the central sea-ice platform likely the response to only subtle variations among these locations, leading to varying clusters. Missing data especially with freeze-up and ice-free season played a role in the clustering.

Spatial associations of Hansen's disease and schistosomiasis in endemic regions of Minas Gerais, Brazil.

Brazil has the second highest case count of Hansen's disease (leprosy, HD), but factors contributing to transmission in highly endemic areas of the country remain unclear. Recent studies have shown associations of helminth infection and leprosy, supporting a biological plausibility for increased leprosy transmission in areas with helminths. However, spatial analyses of the overlap of these infections are limited. Therefore, we aimed to spatially analyze these two diseases in a co-endemic area of Minas Gerais, Brazil, in order to identify potential epidemiologic associations. An ecological study using public health surveillance records and census data was conducted to investigate whether the occurrence of HD -and specifically multibacillary (MB) disease- was associated with the presence of schistosomiasis in a community of 41 municipalities in eastern Minas Gerais, Brazil from 2011 to 2015. Multivariate logistic regression and spatial cluster analyses using geographic information systems (GIS) were performed. The average annual incidence of HD in the study area was 35.3 per 100,000 while Schistosoma mansoni average annual incidence was 26 per 100,000. Both HD and schistosomiasis were spatially distributed showing significant clustering across the study area. Schistosomiasis was present in 10.4% of the tracts with HD and thirteen high-high clusters of local bivariate autocorrelation for HD and schistosomiasis cases were identified. A multivariate non-spatial analysis found that census tracts with MB disease were more likely to have schistosomiasis when adjusted for population density, household density, and household income (aOR = 1.7, 95% CI 1.0, 2.7). This remained significant when accounting for spatial correlation (aOR = 1.1, 95% CI (1.0, 1.2)). We found clustering of both HD and schistosomiasis in this area with some statistically significant overlap of multibacillary HD with S. mansoni infection. Not only did we provide an effective approach to study the epidemiology of two endemic neglected tropical diseases with geographic spatial analyses, we highlight the need for further clinical and translational studies to study the potential epidemiologic associations uncovered.

Population shifts during the reconstruction period in areas marked as evacuation zones after the Fukushima Daiichi nuclear power plant accident: a mobile spatial statistics data-based time-series clustering analysis.

An accurate understanding of the population is essential for the development of medical care and social resources. However, the development of transportation networks has increased temporal and spatial fluctuations in the population, making it difficult to accurately forecast medical care demand, especially during disaster recovery. This study examined the population movement in areas affected by the Fukushima Daiichi nuclear power plant accident using demographic data. The target area includes two cities, seven towns, and three villages that are in the evacuation zone. Using a population estimation that reflects changes in population by time of day, which was obtained from a mobile phone company (NTT DOCOMO), we applied clustering analysis to examine the population dynamics over a 4-year period. From 2019 to 2022, the population increased in eight areas and decreased in four areas. The population was further classified into five groups, identifying the unique characteristics and fluctuations of each group. Different regions had different percentages of groups reflecting the characteristics of their populations. The differences among the regions and demographic transition showed the potential to understand the challenges of recovery and to use the data to inform healthcare planning and social policies. This method, which utilizes estimated population data, is also applicable to the study of medical resources and social policies in the event of future disasters and may be useful in analyzing regional characteristics in detail.

Equity Assessment of Groundwater Vulnerability and Risk in Drinking Water Supplies in Arid Regions

Groundwater is a vital drinking water source, especially in arid regions, sustaining both urban and rural populations. Its quality is influenced by natural (hydrogeological) and human-driven (demographic, policy) factors, which may pose significant public health risks, especially for communities relying on unregulated water supplies. This study addresses critical gaps by examining groundwater vulnerability and contamination disparities, emphasizing their implications for public health and equitable resource management. It analyzes the impact of socio-hydrogeological factors on arsenic and nitrate levels in groundwater-supplied systems in Arizona, U.S. Methods include spatial analysis, ANOVA, multivariate regression, and cluster analysis. Significant disparities in arsenic and nitrate contamination, including exceedances of regulatory limits, were observed across supply types, aquifer characteristics, jurisdictional oversights, and groundwater management areas. Domestic wells and community water systems showed distinct contamination risks. Groundwater vulnerability was influenced by geological differences (karst vs. alluvial aquifers) and regulatory oversight, with Tribal and State systems facing unique challenges and resource needs. Socioeconomic disparities were evident, with minority communities, institutional facilities, rural areas, and specific housing types disproportionately exposed to higher contaminant levels. These findings unveil the intersection of race, socioeconomic status, and public health risks, offering an adaptable framework for addressing similar groundwater challenges in arid and semi-arid regions globally. This study is innovative in its focus on policy distinctions between private and regulated wells, karst and alluvial aquifers, and State and Tribal jurisdictions. It emphasizes the need for targeted vulnerability assessments and remediation strategies that integrate geological, hydrological, and regulatory factors to address risk disparities in vulnerable communities. These environmental inequities underscore the urgent need for stronger regulations and strategic resource allocation to support marginalized communities. The study recommends enhancing monitoring protocols, prioritizing resource distribution, and implementing targeted policy interventions to ensure equitable and sustainable access to safe drinking water in arid regions.

Analysis of Spatial Clustering of Seismic Events in the Northwest Pacific Ocean

The spatial clustering of epicenters of seismic events in the Northwest Pacific Ocean is analyzed using the Discrete Perfect Sets (DPS) topological filtering algorithm. The results of the analysis are presented in this paper. Based on the data shallow earthquakes recorded from 1963 to 2022 by the seismic network of the Kamchatka Branch of the Geophysical Survey of the Russian Academy of Sciences, stable areas of epicenter clustering are identified. These areas are associated with the Northern and Southern segments of the Kuril-Kamchatka seismofocal zone and do not depend on the time of recording of the earthquakes. The characteristic clustering radius for the Kuril-Kamchatka seismofocal zone is determined and is found to be 42–44 km. The analysis has confirmed the unique pattern of seismicity in the territory of the Commander segment of the Aleutian arc, which differs from that observed in the Northern and Southern segments.

Spatial patterns of Mental Health Act apprehensions in Toronto, Canada, 2014-2022.

This research examines the temporal rates and spatial patterns of police-involved Mental Health Act (MHA) apprehensions in Toronto, Canada, from 2014 to 2022. Building from the work of Toronto Police Services (TPS), the goal of this research is to deepen our understanding of MHA apprehensions and provide insights which may be used to guide local mental health reform efforts. Using data collected by TPS, an exploratory, descriptive analysis of spatial patterns of MHA apprehension events between 2014 and 2022 at the neighbourhood level was conducted. To identify high-rate clusters, we used a discrete Poisson model in SaTScan, with a circular moving window. Primary and secondary clusters with a relative risk of 1.5 or greater are reported. From 2014 to 2022, 93,932 MHA apprehensions occurred in Toronto. Apprehension rates were found to vary substantially between neighbourhoods, with approximately 91% of Toronto's 140 neighbourhoods experiencing a net positive increase in rates during the study period after adjusting for population growth. Repeated spatial cluster analysis for each year revealed a range of 4 to 7 clusters annually, with the downtown core consistently emerging as an area of elevated risk (RR range 1.58-1.99). Rising MHA apprehensions within Toronto highlight the pressing need to confront the city's intensifying mental health needs. These findings offer valuable insights into the patterns and nature of police-involved MHA apprehensions, outline reproducible analysis methods that can be used by others, and support targeted evaluation, expansion, and implementation of downstream initiatives to improve mental health responses.

Analysis of the spatio-temporal evolutionary characteristics of myopia among students aged 7-18 years in China: based on panel data analysis.

Myopia is a major important public health concern and has huge burden on health system across the world. This study aimed to explore the spatial and temporal analysis of the prevalence of myopia among students aged 7-18 years to identify the potential clusters and the trends of myopia in China. Spatial autocorrelation analysis, temporal and spatial scan analysis and beta convergence test were performed using ArcGIS10.0 and Stata 15.0. The present study indicated that the prevalence of myopia among students aged 7-18 years in China increased every five years from 1995 to 2019, and there was a certain spatial clustering. The phased spatiotemporal scanning analysis indicated that the gathering area of myopia in students aged 7-18 years in China first shifted from northwest to southeast, and the gradually shifted to Hebei, Shanxi and other northern China regions with Tianjin as the core, and finally spread to the whole of East China, where high-risk areas regarding myopia continue to exist. At the same time, the difference in the level of myopia in each region will gradually shrink over time and will converge. There is a great deal of spatial variations in the pattern of the prevalence of myopia among children and adolescents in China. The spatial cluster analysis provides new evidence for policy-makers to design tailored interventions to reduce the prevalence of myopia and allocate health resource to unmet need areas.

A Fuzzy Spatial Multiple Criteria Analysis Methodology for Solid Waste Landfill Siting

The process of siting municipal solid waste landfills in Greece faces significant challenges due to land resource limitations, the country’s mountainous and water-permeable terrain, and strong public opposition. This study introduces a novel methodology for optimizing landfill sites on Lemnos Island in the North Aegean Sea using a Fuzzy Spatial Multiple Criteria Analysis (FSMCA) approach. By combining fuzzy sets theory, Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), spatial autocorrelation, spatial clustering and sensitivity analysis, this methodology addresses the uncertainties and complexities inherent in landfill siting. The decision problem is structured hierarchically into five levels to manage multiple criteria effectively. Criteria weights are determined using AHP, with discrete criteria graded according to Greek and EU guidelines, and continuous criteria evaluated through fuzzy sets theory. The region’s suitability is assessed using multiple criteria analysis, revealing that 9.7% of Lemnos Island is appropriate for landfill placement. Sensitivity analysis confirms the robustness of the methodology to changes in criteria weights. The case study demonstrates the practical application and benefits of FSMCA in a real-world scenario, underscoring its potential to improve sustainable waste management practices and inform policy making.

Spatial genetic characterization of the red fox (Vulpes vulpes) in the area between the Alps and the Central Dinaric Mountains.

Red fox, Vulpes vulpes, is a globally distributed species characterized by its high adaptability to diverse habitats and a broad range of food resources. This remarkable adaptability has allowed the red fox to thrive in various environments, from urban areas to remote wilderness. In this study, we used a set of microsatellite markers for the comparative genetic analysis of red fox populations from two countries. We included populations from the Eastern Alps and the northern Dinaric Mountains in Slovenia, as well as the Central Dinaric Mountains in Bosnia and Herzegovina. We successfully isolated DNA and genotyped 118 red fox samples. Our analyses, which included Bayesian clustering techniques, revealed a weak genetic differentiation among the studied populations. However, it is noteworthy that statistically significant differences in estimates of genetic differentiation were only apparent when comparing the populations between the two countries. Further spatial genetic clustering analyses provided additional insights, unveiling a differentiation into four genetic clusters. These clusters comprised two distinct groups in Bosnia and Herzegovina and two in Slovenia. This pattern of differentiation suggests that isolation by distance is a key factor influencing the genetic structure of the red fox in this studied region. Additionally, our findings highlighted that populations from the Alps and northern Dinaric Mountains exhibit higher genetic diversity and observed heterozygosity compared to their counterparts in the Central Dinaric Mountains. The genetic diversity is also notable when compared to other European red fox populations. Studying genetic diversity is crucial for the resilience and adaptability of populations, ensuring their survival amid environmental changes and human-induced pressures.

Analysis of Tuberculosis Epidemiological Distribution Characteristics in Fujian Province, China, 2005-2021: Spatial-Temporal Analysis Study.

Tuberculosis (TB) is a chronic infectious disease that harms human health for a long time. TB epidemiological distribution analysis can help governments to control TB in high TB incidence areas. The distribution trend of TB cases varies in different regions. The unbalanced temporal and spatial trends of pulmonary TB (PTB) risk at a fine level in Fujian Province remain unclear. The purpose was to analyze different distribution characteristics, explore the prevalence of TB in this region, and provide a scientific basis for further guidance of TB control work in Fujian Province, China. Prefectural-level and county-level notified PTB case data were collected in Fujian Province. A joinpoint regression model was constructed to analyze the unbalanced temporal patterns of PTB notification rates from 2005 to 2021 at prefecture-level city scales. The spatial clustering analysis and spatial autocorrelation analysis were performed to assess the inequality of the locations of PTB cases. Demographical characteristics were explored by the method of descriptive analysis. TB cases reported in Fujian showed an overall downward trend from 2005 to 2021 (in 2005: n=32,728 and in 2021: n=15,155). TB case numbers showed obvious seasonal changes. The majority of TB cases were middle-aged and older adult male patients (45 years and older; n=150,201, 42.6%). Most of the TB cases were farmers (n=166,186, 47.1%), followed by houseworkers and the unemployed (n=48,828, 13.8%) and workers (n=34,482, 9.8%). Etiologically positive TB cases continue to be the main source of TB cases (n=159,702, 45.3%). Spatially, the reported TB cases were mainly distributed in cities in southeastern Fujian, especially at the county level. TB case numbers showed 2 spatial groups; cases within each group shared similar case characteristics. In terms of geographical distribution, TB showed obvious spatial correlation, and local areas showed high aggregation. The TB incidence trend decreased annually in Fujian Province. TB cases distributed commonly in the male population, middle-aged and older people, and farmers. Etiologically positive cases are still the main source of Mycobacterium tuberculosis infection. TB incidence is higher in the cities with a developed economy and large population in the southeast. TB control should be strengthened in these populations and areas, such as via early screening of cases and management of confirmed cases.

Spatial Cluster Analysis of the Social Determinants of Health and Fatal Crashes Involving US Geriatric and Non-Geriatric Road Users

Spatial Cluster Analysis of the Social Determinants of Health and Fatal Crashes Involving US Geriatric and Non-Geriatric Road Users

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

High-Speed Fluctuation Analysis of Silver-Nanoparticle SERS in Solutions.

We analyzed the fluctuation of surface-enhanced Raman spectra with a temporal resolution of 25 ms using a conventional electron-multiplying charge-coupled device camera experimental setup. The signal-to-noise ratio of the spectra was improved using density-based spatial cluster analysis with noise. Silver nanoparticles (AgNPs) with different sizes were dispersed as surface-enhanced Raman spectroscopy platforms in violet aqueous solutions. The movement of AgNPs and the fluctuation of the spectra were characterized. The fluctuation (signal ON and OFF) was evaluated on the basis of the time intervals between ON and OFF timing. The behavior of each AgNP solution was explained by a two-dimensional random walk model, which means that the phenomenon was mainly governed by the Brownian motion of the AgNPs in the solution. The fluctuation was also compared among three different Raman modes, one of which showed anomalous behavior.

Maternal mortality in Brazil: an analysis of temporal trends and spatial clustering.

This article aims to analyze spatial and temporal patterns of maternal mortality in Brazil during the period 2010-2020 and identify related socioeconomic indicators. We conducted an ecological study of the maternal mortality ratio (MMR) in Brazil's municipalities using secondary data. Temporal analysis was performed using the joinpoint method. Bayesian statistics, spatial autocorrelation, the Getis Ord Gi* technique and the scan statistic were used to identify spatial clusters, and multiple non-spatial and spatial regression models were used to assess the association between factors and the MMR. There was an increase in the MMR in 2020 and an increase in deaths in the North and Southeast. Clusters were found in Amazonas, Tocantins, Piauí, Maranhão, Bahia and Mato Grosso do Sul. The following indicators were negatively associated with the MMR: cesarean section rate, Municipal Human Development Index, and per capita household income of people who are vulnerable to poverty. The MMR was stable up to 2019, followed by a sharp rise in 2020 coinciding with the onset of the Covid-19 pandemic in the country. It is essential that efforts to reduce maternal mortality in Brazil extend beyond the promotion of improvements in antenatal, childbirth and postpartum care to address the social determinants of the problem.

Uranium and Radium in Groundwater and Incidence of Colorectal Cancer in Georgia Counties, USA: An Ecologic Study.

Colorectal cancer (CRC) is the third most commonly occurring cancer in the United States, with higher incidence rates among Black populations. Groundwater concentrations of natural radionuclides uranium and radium have seldom been investigated in relation to CRC despite their known carcinogenicity. We investigate spatial patterns of CRC by race, and in relation to groundwater concentrations of uranium and radium, testing the hypothesis that uranium and radium in groundwater might differentially contribute to incident CRC in Black and White populations in counties of Georgia, USA. Black populations showed a higher incidence of CRC than White populations; the median incident rate difference was 9.23 cases per 100,000 (95% CI: 2.14, 19.40). Spatial cluster analysis showed high incidence clusters of CRC in similar regions for Black and White populations. Linear regression indicated there are, on average, 1-2 additional cases of colorectal cancer in counties with higher levels of radium in their groundwater, irrespective of race. Uranium was not associated with CRC. This ecologic study suggests that radium in groundwater may be linked with increased incidence of CRC, although it did not explain higher CRC incidence rates in Black populations. Further studies are needed to verify this association given the inherent limitations in the ecologic study design and the crude exposure assessment.

A Geographically Weighted Regression–Compute Unified Device Architecture Approach to Explore the Spatial Agglomeration and Heterogeneity in Arable Land Consumption in Southwest China

Arable land loss has become a critical issue in China because of rapid urbanization, industrial expansion, and unsustainable agricultural practices. While previous studies have explored the factors contributing to this loss, they often fall short in addressing the challenges of spatial heterogeneity and large-scale dataset analysis. This research introduces an innovative approach to geographically weighted regression (GWR) for assessing arable land loss in China, effectively addressing these challenges. Focusing on Chongqing, Guizhou, and Yunnan Provinces over the past two decades, it examines spatial autocorrelation with R-squared values exceeding 0.6 and residuals. Eight factors, including environmental elements (rain, evaporation, slope, digital elevation model) and human activities (distance to city, distance to roads, population, GDP), were analyzed. By visualizing and analyzing R² spatial patterns, the results reveal a clear spatial agglomeration distribution, primarily in urban areas with industries, highly urbanized cities, and flat terrains near rivers, influenced by GDP, population, rain, and slope. The novelty of this study is that it significantly enhances GWR computational capabilities for handling extensive datasets by utilizing Compute Unified Device Architecture (CUDA) on a high-performance GPU cloud server. Simultaneously, it conducts comprehensive analyses of the GWR model’s local results through visualization and spatial autocorrelation tools, enhancing the interpretability of the GWR model. Through spatial clustering analysis of local results, this study enables targeted exploration of factors influencing arable land changes in various temporal and spatial dimensions while also evaluating the reliability of the model results.

Clay mineral composition and transport pattern of surface sediments in the Ganges Submarine Delta

The Ganges Submarine Delta is a key land-sea transitional area in the northeastern Indian Ocean. An understanding of sediment distribution, provenance, and transportation in this area is of great importance for understanding its sedimentary environment and the sediment “source-sink” system of the northeastern Indian Ocean. This study aimed to identify the provenances and transport patterns of fine-grained sediments in the Ganges Submarine Delta through analysis of the grain size and clay mineral content of 84 surface sediment samples. Sediment illite, chlorite, kaolinite, and smectite contents were ∼66%, 18%, 11%, and 5%, respectively. Sediment illite content decreased from north to south; smectite and chlorite decreased and increased from northwest to southeast, respectively; kaolinite content was highest in the south. Spatial cluster analysis of the four clay minerals grouped the study area into two provinces: province I in the north is characterized by a clay mineral assemblage similar to that of the Ganges-Brahmaputra rivers; province II in the south is characterized by deep water and changes to clay mineral characteristics due to the mixing of material originating from the Indian Peninsula. This study used the illite/(smectite + chlorite + kaolinite) and kaolinite/illite ratios to discuss sediment transport paths in the two provinces. Himalayan material is widely distributed throughout the study area, and mainly transported from the estuary to the southwestern area by the tides, plume, and monsoons; sediments of the Mahanadi river are transported from southwest to northeast by the southwest monsoon, thereby affecting the sediment composition of province II.