Ordinary Kriging Method Research Articles

Risk Evaluation of Chemical Clogging of Irrigation Emitters via Geostatistics and Multivariate Analysis in the Northern Region of Minas Gerais, Brazil

In this study, we analyzed the hydrogeochemistry of 350 underground wells in the northern region of the state of Minas Gerais, Brazil, for water-chemical parameters that may contribute to the chemical clogging of emitters in drip irrigation systems. Risk class maps were generated for each parameter, and the area was classified based on the water characteristics, considering the degree of water-use restriction in micro-irrigation (i.e., no, moderate, and severe restriction). Inverse distance-weighted, random forest, and ordinary kriging methods were used as interpolation methods. Moreover, a multivariate analysis was conducted to analyze the results. Pearson’s correlation coefficient showed a strong and significant correlation between pH and carbonates, hardness, total dissolved solids (TDS), and electrical conductivity (EC) and between TDS and EC. Principal component analysis revealed that most of the variations in the water quality of the wells could be explained by water–rock interactions with the consequent dissolution of minerals. The principal components were natural sources of ionic salt groups, dissolution of minerals rich in alkaline cations, chemical weathering of iron–magnesium minerals, and increased pH with the conversion of bicarbonates into carbonates. In the parameter cluster analysis, three possible mechanisms that contribute to emitter clogging in the study area were identified: precipitation of calcium and magnesium salts; oxidation of iron and manganese ions forming oxides and insoluble hydroxides; an increase in pH, which converts bicarbonates into carbonates. Clustering analysis revealed the wells that are susceptible to clogging with the exact cause.

Some methods of estimating the spatial pattern point data with application ( comparative study)

The idea of this paper is to estimate spatial data points pattern by Inverse distance weighting, Teard surface analysis of first order and Ordinary kriging method, we calculate the mean absolute error MAE and root mean square error RMSE of these methods as standard to determine the best method to be adopted in the forecasting process, we have been using real data in this paper in the field of salinity dissolved in water wells in the province of kirkuk government , which has been obtained from the groundwater body in the Ministry of water Resources 2014 , and that all the calculations have been processed using matlab program with respect to another to methods

Recognition of potential outliers in soil datasets from the perspective of geographical context for improving farm-level soil mapping accuracies

Soil datasets with outliers lead to inaccurate farm-level digital soil mapping (DSM) results. Existing methods identify potential outliers in soil datasets based on expert experience or simple statistics that neglect the geographical characteristics of soil. In this paper, a novel potential outlier recognition method was developed from the perspective of geographical context. First, spatial search distance was automatically determined by the spatial distance among soil samples. Second, similarities of adjacent soil samples and the local spatial variation level were comprehensively considered to calculate outlier scores. Finally, a frequency histogram of outlier scores was generated to determine a suitable threshold for recognizing potential abnormal samples. To validate the proposed method, it was compared to Lambda and Box-Plot methods, and the ordinary kriging method was used to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus and available potassium, in an agricultural region. Then, a synthetic study using artificially contaminated DEM data was also conducted. The comparative experiment shows that the proposed method is better able to recognize potential outliers by mining the local spatial structure, as indicated by lower mean absolute error (MAE) and root mean square error (RMSE) values. It can be concluded that consideration of local spatial autocorrelation and heterogeneity is helpful in recognizing potential outliers.

Spatial Distribution and Temporal Trends of Dietary Niacin Intake in Chinese Residents ≥ 5 Years of Age between 1991 and 2018.

Limited knowledge exists on trends in niacin consumption and the prevalence of inadequate intakes in China. Understanding trends and the spatial distribution of the prevalence of inadequate niacin intake is crucial to identifying high-risk areas and sub-populations. The dietary intakes of niacin between 1991 and 2018 were analyzed using the China Health and Nutrition Survey (CHNS) data. The estimated average requirement cut point was applied to estimate inadequacy. The geographic information system's ordinary kriging method was used to estimate the spatial distribution of the prevalence of inadequate niacin intakes. However, between 1991 and 2018, the prevalence of inadequate niacin intake increased from 13.00% to 28.40% in females and from 17.75% to 29.46% in males. Additionally, the geographically significant clusters of high and low prevalence were identified and remained stable over almost three decades. The high prevalence of insufficient niacin intake was more pronounced in Henan and Shandong over 27 years. Further, effective and tailored nutrition interventions are required to address inadequate niacin intake in China.

MONITORING OF LAND SURFACE DISPLACEMENT BASED ON SBAS-INSAR TIME-SERIES AND GIS TECHNIQUES: A CASE STUDY OVER THE SHIRAZ METROPOLIS, IRAN

Abstract. Nowadays, subsidence as one of the natural disasters, threaten urban environments. Over the past three decades, the small baseline subset (SBAS) InSAR method due to its capability to monitor medium to large-scale deformations, has emerged as an important tool for estimating surface displacements. In densely vegetated environments, however, the SBAS technique is not effective due to the decorrelation effect. Since the SBAS technique can estimate millions of points for subsidence, these data can be used as input to the Kriging method to estimate the unknown points. Therefore, in this study, we identified the subsidence disaster-stricken-urban areas in the Shiraz metropolis over a 4-year period (between 2017 and 2021) based on the fusion of the SBAS technique and geospatial information science (GIS) using Sentinel-1A time-series images. In the GIS part of this research, the ordinary kriging method was used for interpolation of the SBAS data. Also, the kernel estimation density (KED) was used for the preparation of the raster layers of a population and the built-up density. Based on 33 radar images in ascending and descending orbit, a total of 95 interferograms have been computed. In this research, temporal baselines of less than 100 meters and multi-look window dimensions of 200 meters were used. What is more, the amount of coherence was 0.8 in estimating displacements. Based on the results, the highest displacements occurred in districts 2, 4, 7, and 11. The maximum value of the displacement (30 mm per year) was seen in the southeast of the Shiraz metropolis, in district 7. Moreover, the surface displacement was mainly observed in the lands of the Shiraz international airport and some of the agricultural lands located in district 2. The reasons for the displacements in districts 4 and 11 are related to the high density of the building coverages in these regions. Also, the increased population density was another reason for the subsidence in district 4. It can be concluded that the suggested framework is efficient in determining time-series surface movements in extensive urban and non-urban areas.

A Novel Regional-Scale Assessment of Soil Metal Pollution in Arid Agroecosystems

This work is a novel trial to integrate geostatistics with fuzzy logic under the geographic information system (GIS) environment to model soil pollution. Soil samples from seventy-one soil profiles in the northern Nile Delta, Egypt, and were analyzed for total concentrations of Cd, Co, Cu, Pb, Ni, and Zn. Metal distribution maps were generated using ordinary kriging methods. They were normalized by linear and non-linear fuzzy membership functions (FMFs) and overlain by fuzzy operators (And, OR, Sum, Product, and Gamma). The final maps were validated using the area under the curve (AUC) of the receiver operating characteristic (ROC). The best-fitted semivariogram models were Gaussian for Cd, Pb, and Ni, circular for Co and Zn, and exponential for Cu. The ROC and AUC analysis revealed that the non-linear FMFs were more effective than the linear functions for modeling soil pollution. Overall, the highest AUC value (0.866; very good accuracy) resulted from applying the fuzzy Sum overly to the non-linearly normalized layers, implying the superiority of this model for decision-making in the studied area. Accordingly, 92% of the investigated soils were severely polluted. Our study would increase insight into soil metal pollution on a regional scale, especially in arid regions.

Air pollution and mortality in patients with chronic obstructive pulmonary disease: a cohort study in South Korea.

Evidence on whether long-term exposure to air pollution increases the mortality risk in patients with chronic obstructive pulmonary disease (COPD) is limited. We aimed to investigate the associations of long-term exposure to particulate matter with diameter <10 µm (PM10) and nitrogen dioxide (NO2) with overall and disease-specific mortality in COPD patients. We conducted a nationwide retrospective cohort study of 121,423 adults ⩾40 years diagnosed with COPD during 1 January to 31 December 2009. Exposure to PM10 and NO2 was estimated for residential location using the ordinary kriging method. We estimated the risk of overall mortality associated with 1-, 3-, and 5-years average concentrations of PM10 and NO2 using Cox proportional hazards models and disease-specific mortality using the Fine and Gray method adjusted for age, sex, income, body mass index, smoking, comorbidities, and exacerbation history. The adjusted hazard ratios (HRs) for overall mortality associated with a 10 µg/m3 increase in 1-year PM10 and NO2 exposures were 1.004 [95% confidence interval (CI) = 0.985, 1.023] and 0.993 (95% CI = 0.984, 1.002), respectively. The results were similar for 3- and 5-year exposures. For a 10-µg/m3 increase in 1-year PM10 and NO2 exposures, the adjusted HRs for chronic lower airway disease mortality were 1.068 (95% CI = 1.024, 1.113) and 1.029 (95% CI = 1.009, 1.050), respectively. In stratified analyses, exposures to PM10 and NO2 were associated with overall mortality in patients who were underweight and had a history of severe exacerbation. In this large population-based study of patients with COPD, long-term PM10 and NO2 exposures were not associated with overall mortality but were associated with chronic lower airway disease mortality. PM10 and NO2 exposures were both associated with an increased risk of overall mortality, and with overall mortality in underweight individuals and those with a history of severe exacerbation.

Accuracy assessment of spatial interpolations methods using ArcGIS

The Inverse Distance Weight (IDW), Spline, and Ordinary Kriging (OK) methods are widely used for interpolating spatial data. Many GIS software options are available, particularly for spatial interpolation. This study aims to assess the accuracy of interpolation approaches based on Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data in ArcGIS 10.8. The research used monthly precipitation data extracted from 147 rain gauge station locations in North Sumatra Province. Additionally, eight stations from BMKG's network were excluded from the interpolation process and instead used as validation data. Validation was conducted by calculating error statistics between the predicted and actual data at these excluded points. The predicted data was generated by interpolating values from the 147 rain gauge stations, excluding the eight points corresponding to the validation data locations. The interpolation was done using ArcGIS 10.8 with the IDW, Spline, and Kriging toolbox. A comparison was performed by assessing error parameters and Pearson correlation coefficients. According to the accuracy assessment, both IDW and OK show comparable performance; both are better than spline interpolation. In addition, the box plot chart demonstrated that the spline technique had a tendency to generate outlier values along the perimeters of the study area.

Геоинформационное картографирование эколого-магнитного состояния почв г. Краснокамска

The article is devoted to geoinformation mapping of the ecological and magnetic state of soils in the city of Krasnokamsk. The study included point determinations of the volumetric magnetic susceptibility of soils and the concentration of heavy metals, followed by mathematical-cartographic modeling of the results obtained. Mapping the magnetic susceptibility of soils makes it possible to form areas of the ecological and magnetic state of soils, along which samples are taken for the determination of heavy metals. Thus, the sample size of soil samples for heavy metal studies is reduced without a significant decrease in the representativeness of the final results, which determines the relevance of the studies performed. The purpose of the research is geoinformation mapping of the ecological and magnetic state of soils in the central part of the city of Krasnokamsk. The boundaries of research from the south are limited by the industrial zone of the city, which is mainly represented by pulp and paper industry enterprises. In the eastern part of the survey area, Zakamskaya CHPP-5 and a machine-building plant are located. The soil cover of Krasnokamsk is represented by urbanozems and technogenic surface formations. The total number of magnetic susceptibility measurement points was 77, and heavy metals were determined in 10 samples. During processing spatial data, the methods of mathematical-cartographic modeling, spatial overlay and binary classification were used. The interpolation was performed by the geostatistical method ordinary kriging using the Spatial Analyst tool of the ArcGIS 10.8 program. The creation of the Web GIS “Geoinformation system of magnetic susceptibility and heavy metals in the soils of Krasnokamsk” was performed on the basis of QGIS open geotechnologies using the qgis2web tool, the VS Code code editor. The Web GIS is hosted on the free GitHub platform. Based on the results of mapping, soil contamination with nickel and copper was established. The total area of pollution was 0.55 km2.

Spatial variability study of rainfal lin Cartagena de Indias, Colombia

Precipitation is a component of the hydrological cycle, knowing its spatial distribution is vital for the management of hydrographic basins, the territory and the development of fundamental activities for society. That is why the present study shows the spatial variability of rainfall in Cartagena de Indias city with a network of rain gauges, made up of nine pieces of equipment, separated from each other by 0.9–27 km. After a year of recording (2019), using historical series of data, it was found that the maximum rainfall occurs in the trimester between September and November, with interpolated maps made by the Ordinary Kriging (OK) method it was found that the maximum rainfall is focused on the north, centre and west of the territory, instead, the maximum intensities are presented in the centre and west, the minimums for both variables are presented to the east and south. The 70 and 90% of the rain events have a duration of less than 30 min and 1 h, respectively. Three-parameter exponential function was fitted to the paired correlation distances, and presented correlations lower than 0.8, 0.5 and 0.2 from distances of 1, 3 and 7 km, respectively, in 30 min rain integration. It was also found that with a pluviometric network conformed by at least six pieces of equipment and separated by a 5 km distance from each other in the urban area, a correlation of 0.5 and compliance with the WMO recommendations would be obtained.

Post Monsoon Season Spatial Distribution of Groundwater Depth over the Low-Lands of Sunsari District of Eastern Nepal

The knowledge and an accurate representation of the spatial distribution of groundwater depth of an area are very important to realize sustainable use of groundwater resource, protection of ecosystem and for the development of adaptation policies in the changing climate. The present study has, thus, developed a groundwater depth distribution map of southern low-lands of the Sunsari District at the horizontal grid resolution of 3 km × 3 km. The groundwater depths were surveyed at predefined 82 numbers of geo-referenced grid points scattered over the region during the post monsoon season of the year 2019. Referencing the sampled depths, spatial distribution of the groundwater depth over the area has been predicted using the Ordinary Kriging Method. Three widely used Semivariogram modules such as Spherical, Exponential, and Gaussian were implemented. The Gaussian model of Semivariogram outperformed the other methods with R2 of 0.336, RMSE of 2.591, and MAE of 1.946. The predicted spatial distribution of groundwater depth reasonably well correlated with the observed distribution. The spatial distribution of groundwater level suggests that 12.36% the total area, particularly, over the western, southwestern and northern parts hold groundwater at much shallower depth (&lt; 5 m). Over 51.59 % of the total area in the southeastern and northeastern parts, the groundwater level remained below 8 m from the ground and the rest of the total area holds the groundwater deeper than 8 m.

Estimation of Ordinary Kriging Method with Jackknife Technique on Rainfall Data in Malang Raya

Geostatistics is a science that focuses on spatial data. In geostatistics, there is an estimation method to handle variables whose values ​​vary with the change in location or place, which are called regionalized variables. The estimation method used to handle regionalized variables is called the kriging method. In the ordinary kriging method it is necessary to take into account the semivariogram. Rain is a process of falling water from the clouds to the earth. Rain is measured through rainfall. The purpose of this study was to determine estimation of the ordinary kriging method on normally distributed data and abnormally distributed data, and determine the best semivariogram. The data used is monthly rainfall data in Malang Raya for the period January 2016 to December 2016. From the monthly rainfall dataset, the data are normally distributed in January, February, March, April, May, June, August, September, October, November and December 2016, while the data are not normally distributed in July. Ordinary kriging with Jackknife method can be used to analyze data with normal distribution and data with abnormal distribution.

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Numerous occupant behavior (OB) models that simulate occupancy, activity and action at home have been developed to improve the accuracy and quality of energy demand estimations. Previous studies have revealed that the consideration of inter-occupant diversity improves the performance of OB models. However, existing models ignore spatial variation in OBs or partially consider it using a simple method without evaluating whether it is sufficient. Moreover, the modeling method to reproduce the spatial variation is missing. This study aims to develop a modeling method that can effectively reproduce spatial variation in OBs using American time use data. The global Moran’s index test confirmed that spatial variations exist in OBs; however, they differ by time of day and activities for studied population. Subsequently, two spatial variation representations were generated using the ordinary kriging and spatial autoregressive methods. Finally, three spatial logistic regression models that consider spatial variations were developed and evaluated. The developed models generated smaller errors and higher inter-occupant diversity than the conventional logistic regression models at the state level. The established method is applicable to any country and region. Using higher spatial resolution and richer time use datasets may further improve OB models to model region-specific characteristics of building energy demand.

Výsledky druhej fázy geologického prieskumu hlbokomorských polymetalických konkrécií v prieskumnom území Spoločnej organizácie Interoceanmetal

The exploration rights of the Interoceanmetal Joint Organization for exploration of polymetallic nodules (PMN) are granted from 29 March 2001 to an area located within the Clarion-Clipperton Zone (CCZ) in the eastern central Pacifi c Ocean. Exploration area covers 75,000 km2 and consists of two sectors (B1 and B2). The B2 sector comprises four exploration blocks (H11, H22, H33 and H44). The most prospective area, selected for detailed research, is marked as H22_NE exploitable block and delineated within the H22 exploration block. The article presents results of geological survey, based mostly on the data collected during the second phase of exploration in the licence area (2016–2021, extension of the contract). Results are based on IOM’s expeditions and relevant analytical work. During the IOM-2018 expedition high resolution bathymetric survey of H11, H22, H33 and H44 exploration blocks was carried out. The IOM-2019 expedition provided a new set of the data obtained using the distance methods (side-scan sonar, profi ler) and contact methods (box-corer and gravity corer) in H22_NE exploitable block, H33 exploration block and preliminary delineated Preservation Reference Zone. The study was focused on analytical work based on sediment and nodule analyses of samples in H22 exploration block and H22_NE exploitation block. New estimation of mineral resources in B2 sector was caried out using the geostatistical method of ordinary block kriging with Yamamoto correction. The polymetallic nodule resources have been classifi ed within the Inferred, Indicated and Measured Resources categories of the CRIRSCO classification system.

Change Detection of Groundwater Level and Quality in Coastal Aquifers of Malabar Region in Kerala, India

A study was conducted to assess the changes in groundwater level and quality for the period 2005-2020 in the coastal aquifer of the Vatakara-Koyilandy stretch in the Kozhikode district of Kerala. Hydrogeochemical analysis of groundwater quality parameters, irrigation water quality analysis and preparation of spatial variability of groundwater level and salinity were used for the study. The Piper diagram was used to track the change in the hydrochemical parameters of groundwater sources in 2005 and 2020 and identify changes in the chemical facies of the groundwater. The Gibbs diagram was used to identify the source of the dissolved ions present in the groundwater in both years. The United States Salinity Laboratory diagram was used to assess the groundwater's suitability for irrigation. The spatial variation of water table elevation and salinity were compared using the ordinary kriging method of interpolation. An increase in seawater intrusion into the wells and a reduction in irrigation water quality was indicated by the change in the Hydrochemical facies, and the shifting of more groundwater samples in the evaporation dominance zone in the Gibbs plot. The spatial variability of groundwater level indicates a reduction in premonsoon in both years. On the North coast, a 4% increase in the area under groundwater level &lt; 2m. the reduction in the groundwater table on the coast increases the chance of sweater intrusion and thereby increased salinity. Even though the study area receives heavy rainfall during monsoon and recharging of the coastal aquifer, seawater intrusion progressed landward from 2005 to 2020 during the premonsoon and affected the groundwater level and quality over this period.

Spatial variation and pollution risk assessment of heavy metals in industrial soil based on geochemical data and GIS-A case of an iron and steel plant in Beijing，China

ABSTRACT The precise range of contamination determines the cost of treating heavy metals contamination of soils at industrial sites and was a key aspect of soil heavy metals contamination management. The pollution level and spatial correlation of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in one hundred and thirty topsoil samples from a typical contaminated site was analysed. Geochemical analysis showed that Hg pollution was the most serious in the site, followed by Cd, Pb, Zn, Cu and Cr. The contents of As and Ni in the soil did not exceed the background values. The interpolation accuracy, interpolation results and spatial distribution of inverse distance weight method (IDW), local polynomial method (LPI), radial basis function method (RBF) and ordinary kriging method (OK) were compared, respectively. The results show that the range, mean and coefficient of variation of the four interpolation methods were generally lower than that of the sampled data, which was mainly caused by smoothing effect. The smoothing effect of LPI and OK were the most serious, while the smoothing effect of IDW and RBF were not obvious. The optimal interpolation method of Cd, Cr, Hg, Pb and Zn in polluted sites was RBF and the optimal interpolation method for As, Cu and Ni was IDW. The evaluation of polluted area by IDW and RBF interpolation method was ranked as IDW > RBF, which was much larger than that calculated by sampling point. Both interpolation methods could increased the area of polluted area. The probability kriging (PK) method was similar to that based on sampling points, which can greatly improve the calculation accuracy of heavy metals polluted area in industrial contaminated sites. Geochemical data and spatial interpolation results show that phytoremediation techniques was the most suitable for remediation of heavy metal contaminated soils at this site.

3D Quantitative Metallogenic Prediction of Indium-Rich Ore Bodies in the Dulong Sn-Zn Polymetallic Deposit, Yunnan Province, SW China

The southwestern South China Block is one of the most important Sn polymetallic ore districts in the world, of which the Dulong Sn-Zn polymetallic deposit, closely related to Late Cretaceous granitic magmatism, contains 0.4 Mt Sn, 5.0 Mt Zn, 0.2 Mt Pb, and 7 Kt In, and is one of the largest Sn-Zn polymetallic deposits in this region. In this paper, on the basis of a 3D model of ore bodies established by the cut-off grade of the main ore-forming elements, the In grades were estimated by the ordinary Kriging method and the In-rich cells were extracted. The 3D models of strata, faults, granites, and granite porphyries in the mining area were established and assigned the attributes to the cells, which built buffer zones representing the influence space of the geological factors. The weight of evidence and artificial neural network methods were used to quantitatively evaluate the contribution of each geological factor to mineralization. The results show that the Neoproterozoic Xinzhai Formation (Pt3x), fault (F1), and Silurian granites (S3L) have considerable control effects on the occurrence of In-rich ore bodies. The metallogenic predictions according to the spatial coupling relationship of each geological factor in 3D space were carried out, and then the 3D-space-prospecting target areas of In-rich ore bodies were delineated. In addition, the early geological maps and data information of the mining area were comprehensively integrated in 3D space. The feasibility of 3D quantitative metallogenic prediction based on the deposit model was explored by comparing the two methods, and then, the 3D-space prospecting target area was delineated. The ROC curve evaluation shows that the results of two methods have indicative value for prospecting. The modeling results may support its use for future deep prospecting and exploitation of the Dulong and other similar deposits.

Physicochemical properties and salinization characteristics of soils in coastal land reclamation areas: A case study of China-Singapore Tianjin Eco-City

Land salinization is a global environmental problem, and how to manage saline soils and promote healthy ecosystems has become a major challenge. China-Singapore Tianjin Eco-City is located in coastal land reclamation areas, so salinization is severe in this region. In this study, geostatistical methods, the ordinary kriging method, and principal component analysis were used. Vertical sampling was performed over three layers (0–20 cm, 20–40 cm, and 40–60 cm) at 184 locations within the study area to produce a total of 542 soil samples. It was found that areas with soluble salt contents greater than 3000 mg/kg account for over 90% of the study area, and high soluble salt content in surface layer soils is the dominant factor in soil salinization. Na+, Cl−, and SO42- are the primary control factors that determine the coefficient of variation of the soils’ soluble salt content. Total salinity and Na+, Cl−, SO42-, K+, and Mg2+ reflect on the salinization of the soils, while effective phosphorus, available potassium, and soil organic carbon reflect on the state of soil nutrition. Based on our results, we proposed site-specific and scientific soil remediation and greening measures.

Depression onset and its association with community HIV prevalence: A geospatial and panel analyses of nationally representative South African data, 2015–2017

• We compared depression onset overtime among depression-free individuals based on the HIV prevalence of their communities. • Communities with higher HIV prevalence had higher prevalence of depression onset. • Higher community HIV prevalence was associated with depressive consequences for individual residents over time. The scaling up of antiretroviral therapy services over the past decades has led to a remarkable reduction in HIV infections and HIV-related deaths in South Africa (SA). While this is a step in the right direction, it brings a new public health challenge into focus, namely psychological challenges associated with such chronic and often stigmatising condition in SA, home to the largest HIV epidemic. Given the current lack of national-level evidence, we investigated the role of the HIV epidemic on depression onset in SA using nationally representative panel data from the South African National Income Dynamics Study (SA-NIDS). Our incident cohort consisted of 13,020 sampled adult participants who were depression-free in Wave 4 (baseline year of 2015). We then measured the risk of depression onset in Wave 5 (year 2017) based on the level of HIV prevalence in the community where study participants resided at baseline. A High-resolution map of HIV spatial heterogeneity (i.e., community HIV prevalence) was generated using ordinary kriging mapping methods from a separate nationally representative data source that corresponded to the investigation period. Geospatial analyses were conducted to identify the spatial structure of HIV and depression onset, and generalised estimating equations (GEE) regression models were fitted to determine the risk of depression onset over time based on community HIV prevalence. Our geospatial analyses indicated that HIV and depression onset prevalence spatially overlapped in the eastern part of the country, particularly in Gauteng, KwaZulu-Natal, Mpumalanga, and Free State province. The GEE regression analyses indicated that individual residency in a community with high HIV prevalence was significantly associated with a higher risk of depression than a low HIV prevalence community (adjusted odds ratio =1.45, 95% CI=1.12-1.48). For the first time, we identified a geospatial overlap between HIV and depression, with a greater risk of depression onset in high HIV prevalence communities, at a national scale in SA. There is a need for place-based policy interventions that prioritise the availability of and access to mental health services in high HIV prevalent SA communities, in an ageing HIV epidemic.

Prediction of Earth Rotation Parameters with the Use of Rapid Products from IGS, Code and GFZ Data Centres Using Arima and Kriging – A Comparison

Abstract Real-time prediction of Earth Orientation Parameters is necessary for many advanced geodetic and astronomical tasks including positioning and navigation on Earth and in space. Earth Rotation Parameters (ERP) are a subset of EOP, consisting of coordinates of the Earth’s pole (PMx, PMy) and UT1-UTC (or Length of Day – LOD). This paper presents the ultra-short-term (up to 15 days into the future) and short-term (up to 30 days into the future) ERP prediction using geostatistical method of ordinary kriging and autoregressive integrated moving average (ARIMA) model. This contribution uses rapid GNSS products EOP 14 12h from IGS, CODE and GFZ and also IERS final products – IERS EOP 14 C04 12h (IAU2000A). The results indicate that the accuracy of ARIMA prediction for each ERP is better for ultra-short prediction. The maximum differences between methods for first few days of 15-day predictions are around 0.32 mas (PMx), 0.23 mas (PMy) and 0.004 ms (LOD) in favour of ARIMA model. The maximum differences of Mean Absolute Prediction Errors (MAPEs) on the last few days of 30-day predictions are 1.91 mas (PMx), 0.30 mas (PMy) and 0.026 ms (LOD) with advantage to kriging method. For all ERPs the differences of MAPEs for time series from various analysis centres are not significant and vary up to maximum value of around 0.05 mas (PMx), 0.04 mas (PMy) and 0.005 ms (LOD).