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Table of Contents

  1. Spatial Analysis - defining the problem
    1. Collecting point data
    2. Interpolations are always a raster-based GIS
    3. Nature of a continuous surface model?
    4. Raster surfaces
    5. TIN surfaces
    6. TINs are more efficient at representing three-dimensional surfaces
    7. A Quick Flash Summary of the Nature of Raster and TIN surfaces
  2. Creating raster surfaces from points
    1. Continuous Surfaces are generated from points and from images
    2. What is a spatial interpolation?
    3. Why interpolate?
  3. Deterministic Models
    1. Inverse Distance Weighting (IDW)
    2. More on the Inverse Distance Weighting (IDW)
    3. Natural Neighbourhood Interpolation
    4. How Natural Neighbourhood Interpolation Works?
    5. Variations of Natural Neighbourhood Interpolation
    6. Spline Interpolation
    7. Spline the Regularized Method
    8. Spline the Tension Method
    9. Rectangular Interpolation (not available in ArcGIS extensions)
    10. Rectangular Interpolation How it Works
    11. Trends based on Polynomial Interpolations
    12. The Essence of Polynomial Interpolations
    13. But the Real World has Valleys and Plateaus
    14. Visualizing local polynomial interpolations
    15. Polynomial Analysis: Visualizing radial basis functions
    16. A Quick Flash Summary of Deterministic Models (Interpolations)
  4. Statistical techniques using a semi-variogram for developing continuous surface models (Kriging)
    1. Effectiveness of Kriging
    2. How Kriging Works?
    3. More on Kriging Works?
    4. Kriging Works Similarly to Inverse Distance Weighting
    5. To make a prediction with Kriging, two tasks are necessary:
    6. Generating a Semivariogram
    7. Understanding Semivariance
    8. Semivariance illustrated
    9. Spatial autocorrelation
    10. Understanding a semivariogram-the range, sill, and nugget
    11. The range and sill
    12. The nugget
    13. An Omnidirection Semivariogram
    14. Modifying Directional Parameters
    15. Changing the Variogram Model
    16. Anisotropic Modelling
    17. Other Kriging Techniques
    18. A Quick Flash Summary of Geostatistics - Kriging and the Semivariogram
  5. Developing Triangular Irregular Network (TIN) models for elevation, slope and aspect modelling
    1. The Essentials of a TIN model
    2. A TIN model explained in more detail
    3. Choices when modelling a TIN
  6. Spatial analysis of categorical data using Neigbourhood Analysis (e.g. generation of soil maps)
    1. Voronoi Maps Explained
  7. Which Interpolation methods to use?
    1. Some interpolation techniques can be automatically applied to certain data types.
    2. Application of Interpolation Techniques Illustrated
    3. So lets have a look at some typical point data that you generate and work out which interpolated works best.
    4. Is interpolation processing speed a factor?
    5. Is it necessary to over/undershoot the local Min. and Max. values?
    6. A Quick Flash Summary of the Other Methods of Spatial Analysis

Spatial Analysis - defining the problem

As botanists we collect information that is discrete at a particular sampling point. This discrete data has to be converted in some way into useful map representations. In order to achieve this we need to interpolate or model our data in way that accurately predicts the occurrence of similar features in areas where we have not sampled. In essence from known information we need to extrapolate to unknown areas to produce useful maps.