- 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)
This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.
Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.