KRIGING By Bryan

WHAT IS KRIGING?an advanced geostatistical interpolation tool that generates an estimated surface from a scattered set of points with z-values based on statistical models that include autocorrelation, the statistical relationships among the measured pointsinteractive investigation of the spatial behavior of the phenomenon represented by the z-values should be done before selecting the best estimation method for generating the output surfacegeostatistical techniques not only have the capability of producing a prediction surface but also provide some measure of the certainty or accuracy of the predictions

HOW KRIGING WORKS…Kriging assumes that there is a spatial correlation within the distance and direction of each sample points and this correlation can be used to explain variation in the surfaceThe Kriging tool fits a mathematical function to a specified number of points to determine the output value for each location Kriging is a multistep process and includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and exploring a variance surface. Kriging is most useful when there is a spatially correlated distance or directional bias in the data is known

LET’S BEGIN…Add the Boston and Superfund_Sites shapefile to a blank ArcGIS map and make the Boston shapefile hollowGo to Toolbox Spatial Analyst Tools Interpolation KrigingOrdinary semivariogram: Spherical

PARAMETERSInput Point Features: the input point features containing the z-values to be interpolated into a surface rasterZ value field: the field that holds a height or magnitude value for each point (this can be a numeric field or the Shape field if the input point features contain z-values)Output Surface Raster: the output interpolated surface raster and is always a floating-point rasterSemivariogram properties: 2 models (ordinary and universal)Universal kriging types assume that there is a structural component present and that the local trend varies from one location to another

Ordinary semivariogram have 5 models: Spherical, Circular, Exponential, Gaussian and Linear

Universal semivariogram have 2 models: Liniear Drift and Quadratic DriftI did not work on Cell Size and Search Radius since they were optional

MORE EXAMPLESOrdinary semivariogram: Circular

ALL OTHER ORDINARY SEMIVARIOGRAMS

Exponential Gaussian Linear

UNIVERSAL SEMIVARIOGRAM: LINEAR

UNIVERSAL SEMIVARIOGRAM: QUADRATIC

ORDINARY SEMIVARIOGRAM AT NUMBER OF POINTS = 30

Spherical 30 points

Spherical 12 points

UNIVERSAL SEMIVARIOGRAM AT NUMBER OF POINTS = 30

Linear at 12 points Linear at 30 points