Machine learning models for spatial prediction tasks face unique evaluation challenges that extend beyond temporal leakage. Real estate applications, urban planning, and logistics optimization all work with geographic data where nearby locations behave more similarly than distant ones. This spatial autocorrelation violates independence assumptions and can make models appear stronger than they truly are.

The authors identify six spatial traps that undermine model credibility in geographic contexts. These include the Proximity and Persistence Trap where models exploit spatial memory rather than genuine generalization, Coverage Illusion where dense areas skew performance metrics, and Boundary Illusion where administrative divisions distort results. Geographic bias can encode systemic inequalities while appearing neutral, and the Hedonic Oversimplification reduces complex market dynamics to simple property features.

Even with AutoML and code agents automating workflows, human judgment remains essential for designing proper validation strategies. Random train-test splits fail because they allow spatially correlated observations on both sides. The authors demonstrate this using the London House Price Prediction dataset, showing how temporal-spatial holdouts reveal more realistic performance than random splits.

These spatial evaluation pitfalls highlight why domain expertise matters more than ever in machine learning. Automated tools can fit models, but understanding geographic structure and its implications requires human insight. Practitioners must build meaningful baselines that capture spatial autocorrelation and design validation schemes that test true generalization across unfamiliar geographies.