You are currently viewing an archived version of Topic Interpolation methods.
If updates or revisions have been published you can find them at Interpolation methods.

Learning Objectives:

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)

You are currently viewing an archived version of Topic Interpolation methods. If updates or revisions have been published you can find them at Interpolation methods.

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