2016 QUARTER 02

A B C D E F G H I K L M N O P R S T U V W
AM5-2 - 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
AM10-3 - 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
AM8-5 - Kriging variants
  • Compare and contrast co-kriging, log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging, and universal kriging
  • Interpret the results of universal kriging
  • Apply universal kriging to appropriate data sets