Evaluating a posteriori geometric hypotheses in spatial data: constrained logarithmic curve patterns in a summit landscape
Geometric patterns identified in spatial data after inspection are difficult to evaluate statistically. When hypotheses are formulated a posteriori, conventional tests can overestimate significance because exploratory choices are not accounted for. This problem is pronounced in small-N spatial point sets, where model flexibility and feature selection strongly influence outcomes.A constrained evaluation framework is applied to assess a posteriori geometric hypotheses in spatial data.