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