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)
List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe
Outline how higher order derivatives of height can be interpreted
Explain how slope and aspect can be represented as the vector field given by the first derivative of height
Explain why the properties of spatial continuity are characteristic of spatial surfaces
Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each
Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model
Outline a number of different methods for calculating slope from a DEM
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)
List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe
Outline how higher order derivatives of height can be interpreted
Explain how slope and aspect can be represented as the vector field given by the first derivative of height
Explain why the properties of spatial continuity are characteristic of spatial surfaces
Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each
Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model
Outline a number of different methods for calculating slope from a DEM
AM-17 - Intervisibility