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‣The kriging standard error value is 3.6386.‣If it is assumed that the errors are normally distributed, 95 percent predictionintervals can be obtained in the following way:‣Kriging Predictor + 1.96*sqrt(kriging variance)Which means:‣If predictions are made again and again from the same model, in thelong run 95 percent of the time the prediction interval will contain the valueat the prediction location.‣In the example, the prediction interval ranges from:‣ 95.49 to 109.75 (102.62 + 1.96 * 3.64 )114
LABRATORY WORK‣ You will use the mean annualrainfall dataset.‣ The stations are in the easternand north eastern part of Turkey‣ interpolate the rain values at thelocations where values are notknown1. using the default settings of theGeostatistical Analyst.2. By analysing spatialautocorrelations3. Compare the result models‣Exploring your data‣Examining the distribution of your data‣Histogram‣QQ Plots‣Identifying global trends in your data‣Understanding spatial autocorrelation and directional influences115
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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
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Triangulated Irregular Network (TIN
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GIS ANALYSIS FUNCTIONS50
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A Classification of GIS Analysis Fu
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1. Maintenance and analysis of the
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Edge-matching :Maintenance and anal
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Maintenance and analysis of the spa
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Attribute query functions2. Mainten
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3. Integrated analysis of spatial a
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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
Page 78 and 79: Comparison of Vector and Raster Ove
Page 80 and 81: Connectivity (Network) Operations:
Page 82 and 83: Connectivity (Network) Operations:P
Page 84 and 85: Connectivity (Network) Operations:P
Page 86 and 87: Network FunctionsConnectivity (Netw
Page 88 and 89: Connectivity (Network) Operations:N
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 98 and 99: Calculate the empirical semivariogr
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 116 and 117: THANK YOU116
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