The Hidden Challenges of Spatial Interpolation
Spatial interpolation has long been treated as a convenient solution for filling gaps in incomplete datasets. While the technique provides valuable estimations, it also introduces uncertainty that many practitioners underestimate.
What Makes Interpolation Misleading
Most interpolation methods assume that values change smoothly across space. However, real-world conditions rarely follow these idealized patterns. Sudden environmental transitions, human-built infrastructure, and administrative boundaries often disrupt the continuity that interpolation models expect.
Another common issue arises when analysts select a method simply because it is available in their software. Inverse Distance Weighting, Kriging, and spline-based models each operate under different assumptions. Using the wrong model can produce distortions that are difficult to detect unless the analyst examines residuals and validation points.
Why It Matters
Poorly executed interpolation affects more than visualization. It can alter risk assessments, environmental studies, population estimates, and suitability models. A slight shift in surface values may change the outcome of a decision-making process, especially when thresholds or classifications are involved.
Improving Reliability
Analysts can enhance accuracy by validating models with ground-truth points, testing multiple methods, documenting assumptions, and avoiding overinterpretation of fine-grained surfaces. Understanding the data—its scale, variability, and collection method—is often more important than the choice of algorithm.
Interpolation remains a powerful tool, but only when used with care. Treating it as an analytical shortcut can easily undermine the spatial insights it aims to produce.
