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Spatially thinned bootstrap and random toroidal shift methods for correct estimation of P-values of maps similarity.

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Title: Spatially thinned bootstrap and random toroidal shift methods for correct estimation of P-values of maps similarity.
Authors: Assunção, Renato1 (AUTHOR) rassuncao@esri.com, Butler, Kevin1 (AUTHOR), Krause, Eric1 (AUTHOR), Janikas, Mark1 (AUTHOR), Lee, Ting-Hwan1 (AUTHOR), Asefaw, Hanna1 (AUTHOR)
Source: International Journal of Geographical Information Science. Jul2025, Vol. 39 Issue 7, p1597-1621. 25p.
Subjects: Distribution (Probability theory), Texture mapping, Noise
Abstract: In the context of comparing two categorical maps, addressing the challenges posed by the dependence on marginal frequencies and spatial configuration is a formidable task. Map comparison typically relies on a numerical similarity index S, such as the spatial fuzzy kappa index, which has a probability distribution that varies according to the marginal frequencies of the classes and their spatial configuration in the maps. This variability makes it difficult to mathematically derive and assess the uncertainty associated with any empirical index. In this paper, we introduce two novel methods: the spatially thinned bootstrap and the random toroidal shift. These methods provide a robust framework for comparing two maps using an arbitrary similarity index while accounting for these marginal noise factors. Our approach is characterized by its generality and abstraction, enabling application across different indices of map similarity. To illustrate the effectiveness of our methods, we conduct an extensive study employing the spatial fuzzy kappa index, revealing valuable insights into the behavior and underlying distribution of similarity indices. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Jul2025, Vol. 39 Issue 7, p1597-1621. 25p.
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  Data: In the context of comparing two categorical maps, addressing the challenges posed by the dependence on marginal frequencies and spatial configuration is a formidable task. Map comparison typically relies on a numerical similarity index S, such as the spatial fuzzy kappa index, which has a probability distribution that varies according to the marginal frequencies of the classes and their spatial configuration in the maps. This variability makes it difficult to mathematically derive and assess the uncertainty associated with any empirical index. In this paper, we introduce two novel methods: the spatially thinned bootstrap and the random toroidal shift. These methods provide a robust framework for comparing two maps using an arbitrary similarity index while accounting for these marginal noise factors. Our approach is characterized by its generality and abstraction, enabling application across different indices of map similarity. To illustrate the effectiveness of our methods, we conduct an extensive study employing the spatial fuzzy kappa index, revealing valuable insights into the behavior and underlying distribution of similarity indices. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13658816.2025.2461603
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      – Code: eng
        Text: English
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      – SubjectFull: Texture mapping
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      – SubjectFull: Noise
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              Text: Jul2025
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