2016 QUARTER 03

A B C D E F G H I K L M N O P R S T U V W
AM-08 - Kernels and density estimation
  • Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential
  • Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted
  • Explain why and how density estimation transforms point data into a field representation
  • Explain why, in some cases, an adaptive bandwidth might be employed
  • Create density maps from point datasets using kernels and density estimation techniques using standard software
  • Differentiate between kernel density estimation and spatial interpolation
AM-37 - Knowledge discovery
  • Explain how spatial data mining techniques can be used for knowledge discovery
  • Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query
  • Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets
AM-29 - Kriging methods
  • Describe the relationship between the semi-variogram and kriging
  • Explain why it is important to have a good model of the semi-variogram in kriging
  • Explain the concept of the kriging variance, and describe some of its shortcomings
  • Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support)
  • Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping
  • Outline the basic kriging equations in their matrix formulation
  • Conduct a spatial interpolation process using kriging from data description to final error map
  • Explain why kriging is more suitable as an interpolation method in some applications than others