Tips for Correcting Standard Error in Kriging Prediction

August 23, 2020 by Leslie Edison


Over the past week, some of our readers have received a standard error in kriging prediction error. This problem occurs for several reasons. We'll cover them below. Conventional kriging provides a standard error map that shows the uncertainty of the predicted values. Right-click a geostatistical level in the ArcMap table of contents created using normal kriging and select Change Output to Predicted Standard Error.



Atlas of cancer deaths in USA < / place> (1950-1994) represents the distribution in the table. von Krebs in by district. His education and his usefulness includes various assumptions. This implies, in particular, cancer equality Countrywide risks and the universality of the reported cancer risk Representation of the residents of the area. This hypothesis would be it is reasonable if the district is sufficiently homogeneous in two characteristics core population and exposure risks. Even so, this hypothesis may for many western states United States . These conditions are usually divided into just a few counties, each covering large geographic areas with irregularities distributed populations. Therefore, it is unlikely that the circular plane Statistics correctly display the range of actual experience per classout of the county in an approved way.

Overall, the goal of these three years is The research project aims to study the geographic variations in the relationship between Cancer and arsenic risk in United States . Arsenic exposure may be one of the reasons the development in some respects of cancer of the bladder, lungs, kidneys and skin. Meanwhile, arsenic concentrations vary between geographic concentrations. Location. Font family state divided into several counties. All The county includes large geographic areas with uneven distribution. Population size. To represent the range of actual experience in the county, we have more research needs to be done on geographic variation using Information systems software (GIS).

kriging prediction standard error

This report shows that Study of geographic variation usingGIS analysis software , Arizona . Modeling arsenic in groundwater Concentrations were the main goal of this research project.

A Geographic Information System (GIS) is A type of mapping software that connects data from real objects on the screen Menu. It offers data creation, data visualization, analysis and List four main uses. In reality, it is used to connect multiple sources geo-referenced health statistics. Overview of various The interactions between health, environment and behavior can be obtained by identifying Cancer incidence cluster followed by comparison with cluster locations in the mapped distribution of arsenic. Geostatistical Analyst in ArcMap version 8.1 from ESRI Inc usesI was involved in geostatistical analysis. reported below.

Interpolation methods are basic. classified as deterministic and stochastic. Deterministic interpolation is used techniques for creating new surfaces from points measured according to either the degree of similarity or the degree of softness. It can be divided in two subgroups: global and local. Geostatistical interpolation is applied Techniques for using the statistical properties of measured points.

Accept these values ​​where they appear. close to each other are more similar than those further away, on the contrary Weighted distance (IDW) uses the measured values ​​surrounding the forecast. Location to predict the value of an unmeasured location. IDW has more Predicted readings have a greater impact on predicted value than those who depart from it.

In this experiment, it was IDW is performed on 170 points of the training sample with a strength of 2 and Neighborhood method. There are 15 neighbors and at least 10 of them through the elliptical (quadrant) search box . Example this method is illustrated below:

Regular kriging the model assumes Z (s) = μ + ε (s), where μ is unknown The constant and ε (s) are random numbers bania. This allows local Influences due to the values ​​of the close proximity. It generates predictions, quantiles, Standard probability or error maps using continuous data points in the figure Space. Because of the unknown mean, only a few assumptions can be made for this. conventional kriging, which makes this method especially flexible.

In this analysis Conventional kriging was carried out at 170 locations. training set using a spherical semivariogram model, automatic delay of 4364.1 and 12 delays. We also used the neighborhood method, including 5 neighbors and at least 2 using the form type . Semivariogram equation this is

where the nugget effect is the sum of measurement errors and small irregularities (variations on a microscopic scale). Because each component can be Zero, the nugget effect can be entirely one or the other.

Conceptual framework For local polynomial interpolation, many smaller overlap planes must be used. then use the center of each level as the forecast for each location in field of study. As a result, the surface becomes more flexible and more flexible over time. exactly. This interpolation matches many polynomials from overlapping quarters. Local polynomial interpolation is sensitive to Remoteness from the microdistrict.

In experiment Local polynomial interpolation was performed at 170 points of the training sample. weight 125 644.96 and degree 1. This also takes a quarter The method includes 165 (at least 10). An example area is in next picture

Global polynomial The interpolation method adjusts the plane between the sample points according to The general trend. Airplane is a special case of the mathematical family Formulas are called polynomials. The purpose of interpolation is to minimize errors. We can measure the error by subtracting kEach measured point from its predicted Evaluate the plan, square it, and fold it. This amount is called Least squares. This process is a first-order theoretical framework. Global polynomial interpolation. Global polynomial interpolation is perfect Area determined by a mathematical function (polynomial) for the input Examples of points. The area of ​​the global polynomial is gradually changed and fixed







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