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

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Make a prediction‣the weights decrease with distance but are more refined than a straight distanceweighting‣since they account for the spatial arrangement of the data.112
Kriging variance‣One of the strengths of using a statistical approach is that it is possible to alsocalculate a statistical measure of uncertainty for the prediction.‣To do so, multiply each entry in the ג vector times each entry in the g vector andadd them together to obtain what is known as the predicted kriging variance.‣The square root of the kriging variance is called the kriging standard error.113
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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
Page 62 and 63: 3. Integrated analysis of spatial a
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 114 and 115: ‣The kriging standard error value
Page 116 and 117: THANK YOU116
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