2018 QUARTER 02

A B C D E F G H I K L M N O P R S T U V W
AM-16 - Interpolation methods
  • Identify the spatial concepts that are assumed in different interpolation algorithms
  • Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting, and kriging
  • Differentiate between trend surface analysis and deterministic spatial interpolation
  • Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem
  • Design an algorithm that interpolates irregular point elevation data onto a regular grid
  • Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting
  • Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package
  • Describe how surfaces can be interpolated using splines
  • Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations
  • Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)
  • Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)
AM-17 - Intervisibility
  • Define “intervisibility”
  • Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by DEMs
  • Perform siting analyses using specified visibility, slope, and other surface related constraints
  • Explain the sources and impact of errors that affect intervisibility analyses

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