Computational study demonstrates a Wasserstein metric framework for measuring function localization in bounded Euclidean domains, providing improved quantification of spatial concentration.
We identify shortcomings in two popular measures of localization of functions: the Lᵖ-Lq L p - L q participation ratio and the mass concentration comparison. We then introduce a novel localization measure for functions on bounded subsets of Rᵈ R d , d=1,2,3, d = 1 , 2 , 3 , ⋯ , based on a Wasserstein metric. For efficient computation, we prove the equality of this measure with a suitable Sobolev norm in dimension one. We demonstrate our approach by numerical experiments in one and two dimensions. Finally, we discuss and mitigate challenges arising from boundary effects.
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Karamehmedović et al. (2026) studied this question.
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