The climatic reference values for monthly and annual average air temperature and total precipitation in Catalonia – northeast of Spain – are calculated using a combination of statistical methods and geostatistical techniques of interpolation. In order to estimate the uncertainty of the method, the initial dataset is split into two parts that are, respectively, used for estimation and validation...

The variogram model is one of the most relevant parameters in geostatistical estimation and simulation methods. The sample variogram is inferred from available data, which may be subject to spatial bias and proportional effect. Over this sample variogram, a licit variogram model is fit and is carried through the process of estimation and/or simulation of the random function usually without rega...

BACKGROUND The report of the fifth national tuberculosis (TB) epidemiological survey in P. R. China, 2010, roughly showed that pulmonary TB (PTB) prevalence was higher in western China than in central and eastern China. However, accurately estimating the continuous spatial variations of PTB prevalence and clearly understanding factors impacting on spatial variations of PTB prevalence are import...

The present study was carried out to evaluate three interpolation methods including weighted moving average (WMA) with the power of 2 and 3, Kriging and Cokriging methods. Data of 23 wells in Mazandaran province were collected in fall and spring 2006. Seven parameters including electrical conductivity (EC), pH, total dissolved solids (TDS), sodium adsorption ratio (SAR), total hardness (TH), ch...

the estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. in this paper, we investigate the spatial distribution of three air pollutants in tehran’s atmosphere: carbon monoxide (co), nitrogen dioxide (no2), and atmospheric particulate matters less ...

The first step in statistical data analysis is to verify three data features: dependency, stationarity, and distribution. If data are independent, it makes little sense to analyze them geostatisticaly. If data are not stationary, they need to be made so, usually by data detrending and data transformation. Geostatistics works best when input data are Gaussian. If not, data have to be made to be ...

A new methodology has been developed to design optimal monitoring network to estimate the transient pollution plume resulting from active pollution sources in a contaminated aquifer. An optimization algorithm is linked with geostatistical kriging model as well as a numerical simulation model. Simulated annealing is used as the optimization tool. The physical process in the aquifer, i.e., the fl...

Kriging techniques are used to map national public health data routinely reported to the Centers for Disease Control and Prevention (CDC). Although choropleth maps are commonly used to display geographic and temporal disease-related events for political (state and county) jurisdictions, kriged maps may offer opportunities to enhance visual communication of event patterns over time. As a geostat...

Geostatistical models often require a variogram or covariance model for kriging and krigingbased simulation. Next to the initial decision of stationarity, the choice of an appropriate variogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental variograms with a nested combination of proven models such as the spherical, exponential, ...