Generates generalized covariate field (GCF) variables from
spatial covariates observed at projected coordinates, and selects a
stable subset of them for geospatial prediction. For each input
covariate the method builds spatial-pattern features (local indicator
of spatial association, local Geary's c, log local variance, rank
quantile entropy, geocomplexity, log scale variance, local variogram
exponent, and signed z-score and median absolute deviation outlier
strengths over a series of buffer radii) and neighbourhood-distribution
features (buffer-wise quantiles of the covariate values surrounding
each location), reduces the buffer and quantile sweeps to a compact set
of interpretable functional summaries, and selects variables by random
forest importance combined with spatial-block stability resampling and
group voting. The GCF method is positioned as prediction-oriented
feature construction: its output feeds any downstream regression
learner. Methods are described in Song (2026)