Spatial Target Leakage in Location Intelligence: Quantifying Cross-Validation Bias from Spatial Autocorrelation

Location intelligence models predict site-level outcomes using spatially derived features. Standard random cross-validation systematically overestimates their performance because nearby observations share spatially autocorrelated feature values, creating an information leak from test to train. We formalize this phenomenon as spatial target leakage, demonstrate it on a synthetic geomarketing task where random CV reports R² = 0.81 while spatially blocked CV reports R² = 0.43, and provide a minimal Python implementation of grid-based spatial blocking compatible with scikit-learn.