Spatial Interpolation (IDW)

You have rainfall readings at a dozen gauges — what about everywhere in between? Inverse-distance weighting fills the gaps into a smooth map.

Spatial Interpolation (IDW)Live

filling the gaps between measurements

Controls

Presets

Power parameter

Given scattered measurements — rainfall gauges, soil samples, air-quality sensors — inverse-distance weighting estimates values everywhere in between. Each sample's influence falls off with distance raised to a power: a low power gives smooth, blended surfaces; a high power makes each sample dominate its neighborhood in sharp bull's-eyes. The workhorse of GIS mapping.

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Data Inspector

Samples8

Power p2.0

Characterbalanced

Governing equation

\overset{z}{^} (x) = \frac{\sum _{i} w _{i} z _{i}}{\sum _{i} w _{i}}, w_{i} = \frac{1}{d _{i}^{2.0}}

Reading this result: At power 2.0 nearby samples win but distant ones still contribute — a balanced surface between smooth and spiky.

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How it works

Inverse-distance weighting estimates a value at any location as a weighted average of nearby samples, with weights falling off as distance to a power. A low power gives smooth, gently blended surfaces; a high power lets each sample dominate its neighborhood, producing bull's-eyes. It is a staple of GIS for mapping rainfall, pollution, elevation, and mineral grades.

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Frequently asked questions

Is this inverse distance weighting interpolation tool really free?▾

Yes. Spatial Interpolation (IDW) runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.

Do I need to install anything?▾

No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.

Can I save or share my simulation?▾

Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.

How accurate are the results?▾

The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.