Valid topology is a fundamental requirement for spatial statistical methods that rely on polygonal contiguity. When the polygonal dataset isn’t topologically sound, it might lead to potential inaccuracies in the outcome of the analysis. The aim of this work is to explore the influence of corruption of contiguity weights matrices on the robustness of commonly used spatial algorithms. The discussed methods are Global Moran’s I, a spatial autocorrelation indicator, and two regionalisation techniques: agglomerative clustering and SKATER. As the robustness of these methods to incorrect topology has not been previously explored in any context, this study provides an evaluation of how topological integrity affects the reliability of spatial analytical frameworks.