3. Integrated analysis of spatial and attribute data - RTC, Regional ...

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Calculate the empirical semivariogramThe ordinary kriging model is:‣where s =(X,Y) location; one of the sample locations is s = (1,5),and‣Z(s) is the value at that location; for example, Z(1,5) = 100.‣The model is based on a constant mean m for the data (no trend) and randomerrors e(s) with spatial dependenceThe predictor is formed as a weighted sum of the data:where‣Z(si) is the measured value at the ith location, for example, Z(1,5) = 100;‣ג i is an unknown weight for the measured value at the ith location; ‣s 0is the prediction location, for example, (1,4); and N = 5 for the fivemeasured values98
Calculating the empirical semivariogram‣In a semivariogram, half the differencesquared between the pairs of locations (the y-axis) is plotted relative to the distance thatseparates them (the x-axis).‣The distance between two locations is calculated by using the Euclidean distance:100-105=5 2 =25Theempiricalsemivariance=0.5 timesthe difference squared:0.5 * average[(value atlocation i - value atlocation j) 2 ].99
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International Training Course on
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What is GIS?A widely accepted defin
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What is GIS?‣Where do certain con
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It is one of the most important ele
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Geographic PositionThe Nature of Ge
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The Nature of Geographic DataSpatia
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Organizing Geographic Data ForAnaly
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The Components of GIS‣All of thes
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The Components of GISMethods includ
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The Components of GISSoftwareAll pa
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Data InputData QualityErrors in the
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Positional AccuracyData Qualityexpe
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SPATIALDATAMODELS26
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GIS Spatial Data ModelsRepresentati
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Raster Data ModelIn the raster data
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Raster Data ModelRaster file can be
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1. Traditional Raster Encoding34
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3. Quadtree raster encoding‣provi
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Vector Data ModelIn the vector data
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Vector Data Model‣The data model
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Topological ModelTopological relati
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Topological ModelOther names for No
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Some Basic Topological Relationship
Page 48 and 49: Triangulated Irregular Network (TIN
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Page 52 and 53: A Classification of GIS Analysis Fu
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Page 60 and 61: Attribute query functions2. Mainten
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Page 72 and 73: Logical Operations on Raster: LOGIC
Page 74 and 75: Overlay Analysis‣One of the major
Page 76 and 77: Vector overlayThe process of subdiv
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Page 90 and 91: The principles of geostatistical an
Page 92 and 93: Geostatistical Analyst‣A major ch
Page 94 and 95: Calculate the empirical semivariogr
Page 96 and 97: Modeling a semivariogram‣spatial
Page 100 and 101: Binning the empirical semivariogram
Page 102 and 103: Calculating the empirical semivario
Page 104 and 105: Fitting a model‣Now you can plot
Page 106 and 107: Different types of semivariogram mo
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Page 110 and 111: Creation of g vector for the unmeas
Page 112 and 113: Make a prediction‣the weights dec
Page 114 and 115: ‣The kriging standard error value
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