Spatial Interpolation in ArcGIS Pro | IDW, Spline, and Kriging
Spatial interpolation is a powerful GIS technique used to estimate unknown values across a surface using a set of known data points. In simple terms, it helps us “fill in the gaps” to create continuous maps from scattered data — like mapping rainfall, temperature, or pollution levels between observation stations.
In this tutorial, you’ll learn step-by-step how to apply and compare three of the most common interpolation methods in ArcGIS Pro:
IDW (Inverse Distance Weighted) – estimates values based on distance weighting.
Spline – smooths surfaces to fit the known data points.
Kriging – a geostatistical method that considers both distance and spatial trends.
You’ll also see how:
📌 Changing the IDW Power parameter affects interpolation results.
📌 Subsetting data impacts accuracy and map patterns.
📌 To use the Raster Calculator to compare differences between interpolation results.
📌 Interpolation methods perform when data points are unevenly distributed or limited.
By the end of this video, you’ll understand how different interpolation techniques produce different spatial patterns — and which method works best depending on your data and research goal.
🎯 Topics Covered:
Introduction to Spatial Interpolation
IDW, Spline, and Kriging Methods Explained
Visualizing Differences Between Methods
Using Definition Queries & Raster Calculator in ArcGIS Pro
Comparing Complete vs. Subset Datasets
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