Reliable Topology for Dynamic Data: Mathematical Foundations and Applications

Abstract

Across many scientific domains, practitioners rely on coarse, discretized summaries to track the evolving structure of complex systems under noise, measurement error, and changing system size. Understanding when such summaries are reliable -- and when apparent robustness is illusory -- remains a fundamental challenge. Topological data analysis (TDA) provides a case study: Crocker diagrams track the number of topological features across spatial scale and time, and because they are computationally efficient and easy to interpret, they have been widely used for exploratory analysis, bifurcation detection, model selection, and parameter inference. Despite their popularity, Crocker diagrams have lacked rigorous stability guarantees ensuring robustness to small data distortions. We develop a conditional stability theory for Crocker diagrams constructed from evolving point clouds. Our main results include deterministic conditions guaranteeing exact invariance when pairwise distances are well separated from the diagram's discretization thresholds, together with bounds on how much the diagrams can change when these conditions fail. We also establish probabilistic stability guarantees under Gaussian noise and bounds on topological change caused by adding or removing points, scaling linearly with the number of modified points. We illustrate these results using two complementary examples: an analytically tractable breathing polygon model that reveals how stability thresholds depend on geometry, and a feasibility analysis of epithelial cell imaging data showing when bounded-change guarantees provide the appropriate robustness framework. Together, these results reveal a two-tier stability structure for coarse, discretized topological summaries: exact invariance under verifiable geometric separation conditions, and geometry-controlled bounded change otherwise.