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What is Spatial Interpolation and How Missing Data Points Get Estimated
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What is Spatial Interpolation and How Missing Data Points Get Estimated
01 Nov 2025Trishunya Team
What is Spatial Interpolation and How Missing Data Points Get Estimated
No survey ever measures every single point on the ground, that would be physically impossible. Instead, a set of known measured points is used to mathematically estimate values everywhere in between. Spatial interpolation is this estimation process, and it is the invisible mathematics behind every contour line and continuous elevation surface you see on a survey drawing.
99%+
Of any DEM surface is interpolated, not directly measured
4+
Common interpolation methods available
Zero
Single method universally best for every scenario
1
Core assumption: nearby points are more similar
Why Interpolation is Necessary
The fundamental idea behind every interpolation method
Nearby locations tend to have similar values, elevation, temperature, or any other measured attribute. This assumption, called spatial autocorrelation, is what makes interpolation possible and reasonably reliable, even though it is technically an educated estimate rather than a direct measurement.
Common Interpolation Methods
Inverse Distance Weighting estimates unknown values by weighting nearby known points more heavily than distant ones. Simple and fast, works reasonably well for smoothly varying surfaces.
Kriging is a statistical method that accounts for spatial correlation patterns in the data itself, often producing more accurate results but requiring more computational effort and statistical understanding to apply correctly.
Spline interpolation fits a smooth mathematical curve through known points, useful for creating visually smooth contour surfaces, though it can occasionally overshoot values between sparse points.
Where Interpolation Quality Matters Most
Sparse Point Density
Areas with fewer measured points rely more heavily on interpolation assumptions, increasing potential estimation error.
Complex Terrain Features
Sharp elevation changes, cliffs, or sudden drops are harder for interpolation to represent accurately without dense nearby measurements.
A well-planned point collection grid, denser in complex areas, gives interpolation algorithms the input data they need for reliable results.
Moderate, ground gaps under thick vegetation may need filling
Drone photogrammetry and LiDAR naturally reduce reliance on heavy interpolation by capturing dense actual data points, which is one reason these methods often produce more reliable terrain models than sparse ground survey grids for complex sites. Learn more about our drone survey services.
