Interpolation
A spatial analysis technique for estimating unknown values at specific locations based on known values from surrounding points, foundational for creating continuous surfaces such as elevation or temperature (inferred from standard GIS usage).
What does Interpolation mean?
The practice of guessing unknown values at certain locations based on known values from nearby points is known as interpolation in GIS. It is frequently used to convert a collection of discrete sample points into continuous surface data (such as elevation, temperature, or rainfall).
Important Features:
Generates raster (gridded) data from dispersed points
Believes that values progressively shift over time.
Typical interpolation techniques consist of:
Inverse Distance Weighting, or IDW
Kriging
Spline
Inherent Neighbour
When full data coverage is unavailable, interpolation aids in the creation of maps for a variety of geographic investigations, including weather forecasting, environmental research, and terrain modelling.
Related Keywords
Interpolation techniques, such as nearest neighbor, linear, and spline interpolation, are used to estimate unknown values between known data points.
A mathematical technique called cubic spline interpolation is used to produce a smooth curve that traverses a collection of data points. It ensures continuity in the first and second derivatives by fitting piecewise cubic polynomials between each pair of locations, in contrast to linear interpolation. Because it produces a smooth, realistic-looking curve that faithfully represents intricate information, it is frequently utilized in data analysis, computer graphics, and engineering.
A numerical technique for estimating a function's values from a collection of known data points is polynomial interpolation. It creates a smooth curve that roughly represents the underlying function by building a single polynomial that goes through each of the specified points. Lagrange and Newton interpolation are common methods used for data analysis, curve fitting, and value prediction in science, engineering, and computer graphics.
Inverse Distance Weighting (IDW) interpolation uses nearby known points to estimate values at unsampled places, giving closer points greater weight. It is frequently used in GIS to map continuous data, such as rainfall or elevation.