geostatistical modelling
Spatially explicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach
... In geostatistical modelling, the standard assumption is that there is a stationary spatial dependence in the data, which implies that the spatial correlation is a function of the distance between points and ...
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Application of Geostatistical Modelling to Study the Exploration Adequacy of Uniaxial Compressive Strength of Intact Rock alongthe Behesht-Abad Tunnel Route
... of geostatistical modelling in estimation of geotechnical ...geostatistical modelling. In this study, the ability of geostatistical modelling to obtain the pros and cons of ...
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Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS)
... when modelling spatially-correlated malaria survey data may lead to imprecise estimates of the risk, the significance of the risk factors and of the prediction ...Similarly, modelling spatial correlation in ...
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Bayesian geostatistical modelling of soil transmitted helminth survey data in the People’s Republic of China
... Bayesian geostatistical model fit is MCMC ...the geostatistical model using the stochastic partial differential equations (SPDE)/INLA [19,25] approach, readily implemented in the INLA R-package (available ...
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Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co endemicity
... based geostatistical analysis to predict for each pixel in the map the risk of having CFA stratified according to age as well as producing a map of the associated predic- tion ...the modelling approach ...
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Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data
... MIS data were analysed in order to identify environmen- tal/climatic, demographic, and socioeconomic and con- trol intervention factors associated with malaria risk and produce a contemporary risk map of malaria among ...
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Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection
... Giardina, F. , Gosoniu, L. , Konate, L. , Diouf, M. B. , Perry, R. , Gaye, O. , Faye, O. & Vounatsou, P. (2012). Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical ...
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Geostatistical modelling of the association between malaria and child growth in Africa
... One of the main challenges in modelling the asso- ciation between malaria and HAZ is the need to take account of confounding effects. Among these, socio- economic status has been shown to be one of the most ...
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Geostatistical methods and applications in global health
... a geostatistical framework that allows for joint modelling of data from multiple diagnostics by considering two main classes of inferential problems: (1) to predict prevalence for a gold-standard diagnostic ...
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Modelling the distribution and transmission intensity of lymphatic filariasis in sub Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling
... Lymphatic filariasis (LF) is a mosquito-borne disease caused by the filarial worms, Wuchereria bancrofti, Bru- gia malayi and B. timori. Since the launch of the Global Programme to Eliminate Lymphatic Filariasis in 2000 ...
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Geostatistical inference in the presence of geomasking:A composite likelihood approach
... of geostatistical models and apply this to analyse remote sensing data on total column ozone, where positional error is caused by allocation of each measured value to the centre of the nearest ...Gaussian ...
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Asymptotics of Maximum Composite Likelihood Estimation for Geostatistical Data
... a geostatistical setting has lim- ited results on the statistical performance of such estimators relative to maximum likelihood estimation, particularly from a theoretical ...
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Contribution of geostatistical analysis for the assessment of RMR and geomechanical parameters
... Geotechnical engineering practitioners are always looking out for tools which can improve the design and help understand and reduce the large uncertainty and variations in rock masses. In the literature, only a few ...
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Geostatistical Analyses of Field Spatial Variability of Cotton Yield
... In geostatistical analyses, autocorrelation has often been used to describe the spatial variabilities of soil properties and plant characteristics and the degree of dependencies among neighboring observations in a ...
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Indices of precipitation extremes in Southern Portugal – a geostatistical approach
... The objective of the present work is to evaluate the spatial distribution of extreme precipitation events in Southern Por- tugal, using a geostatistical approach to assess the relation- ships between spatial and ...
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A geostatistical approach to multisensor rain field reconstruction and downscaling
... known geostatistical technique; it is based on the assumption that the expected value of the regionalised variable is a known constant, although this is unrealistic for many ...1996). Geostatistical ...
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Spatial Distribution of Lead in Calcareous Soils and Rice Seeds of Khuzestan, Iran
... caused by low-level exposure to Pb have been extensively documented. Such KHDOWKHIIHFWVLQFOXGHQHXURORJLFDOLPSDLUPHQWDQGGH¿FLWVLQWKHIXQFWLRQLQJRI the central nervous system (Needleman 1983; Needleman et al.1RWDEO\ the Pb ...
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A Comparative Evaluation of Normal Polygon Geotechnical Deterministic Analysis (NPGDA) and GEOStatistical INterpolation Techniques (Kriging) (GEOSTAINT-K): A Case Study from Kota Kinabalu Area, Sabah, Malaysia
... regional modelling and prediction of shallow landslides using a transient rainfall infiltration model in combination with slope stability calculation (Transient Rainfall Infiltration and Grid-based Regional ...
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Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure
... and Ribeiro Jr., 2007). We simulate data on the unit square [0, 1] 2 evaluate the integral in (3) by numerical quadrature over a 64 × 64 prediction grid, and approximate the expectation of thew integral by a Monte Carlo ...
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Application of Geostatistical Methods to Estimate Groundwater Level Fluctuations
... Kriging is a technique of making optimal, unbiased estimates of regionalized variables at unsampled locations using the structural properties of the semivariogram and the initial set of data values (David, 1977; Gundogdu ...
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