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

More documents

Recommendations

Info

Geostatistical Analyst‣A major challenge facing most GIS modelers is to generate the most accuratepossible surface.‣The deterministic methods (eg. IDW) weights the surrounding measured valuesto derive a prediction for each location.‣the weights are based only on the distance between the measured points‣However, the geostatistical methods that are based on statistical models theweights are based not only on the distance between the measured points andthe prediction location but also on the overall spatial arrangement among themeasured points.‣To use the spatial arrangement in the weights, the spatial autocorrelationmust be quantified.92
Calculate the empirical semivariogram‣Kriging, like most interpolation techniques,is built on the assumption that” things thatare close to one another are more alikethan those farther away (quantified here asspatial autocorrelation).”‣The empirical semivariogram is a meansto explore this relationship.93
Page 1 and 2:
International Training Course on
Page 3 and 4:
What is GIS?A widely accepted defin
Page 5:
What is GIS?‣Where do certain con
Page 8 and 9:
It is one of the most important ele
Page 10 and 11:
Geographic PositionThe Nature of Ge
Page 12 and 13:
The Nature of Geographic DataSpatia
Page 14 and 15:
Organizing Geographic Data ForAnaly
Page 16 and 17:
The Components of GIS‣All of thes
Page 18 and 19:
The Components of GISMethods includ
Page 20 and 21:
The Components of GISSoftwareAll pa
Page 22 and 23:
Data InputData QualityErrors in the
Page 24 and 25:
Positional AccuracyData Qualityexpe
Page 26 and 27:
SPATIALDATAMODELS26
Page 28 and 29:
GIS Spatial Data ModelsRepresentati
Page 30 and 31:
Raster Data ModelIn the raster data
Page 32 and 33:
Raster Data ModelRaster file can be
Page 34 and 35:
1. Traditional Raster Encoding34
Page 36 and 37:
3. Quadtree raster encoding‣provi
Page 38 and 39:
Vector Data ModelIn the vector data
Page 40 and 41:
Vector Data Model‣The data model
Page 42 and 43: Topological ModelTopological relati
Page 44 and 45: Topological ModelOther names for No
Page 46 and 47: Some Basic Topological Relationship
Page 48 and 49: Triangulated Irregular Network (TIN
Page 50 and 51: GIS ANALYSIS FUNCTIONS50
Page 52 and 53: A Classification of GIS Analysis Fu
Page 54 and 55: 1. Maintenance and analysis of the
Page 56 and 57: Edge-matching :Maintenance and anal
Page 58 and 59: Maintenance and analysis of the spa
Page 60 and 61: 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 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
Page 108 and 109: 108
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
Page 116 and 117: THANK YOU116
Page 118 and 119: 118
Page 120 and 121: 120
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?