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Pipeline to identify dominant features in spatial data

Flury, Roman; Furrer, Reinhard (2022). Pipeline to identify dominant features in spatial data. Journal of Computational Mathematics and Data Science, 5:100063.

Abstract

Dominant-feature identification decomposes spatial data into several additive components to make different features apparent on each component. It recognizes their dominant features credibly and assesses feature attributes. This paper describes the pipeline to apply this method to regular and irregular lattice data as well as geostatistical data. These implementations are all openly available and templates for each case are provided in an associated git repository. As geostatistical data is typically large, we propose several efficient approximations suitable for such data. Emphasizing the use of these approximations in the context of dominant-feature identification, we apply them to data from a climate model describing the monthly mean diurnal range for the period between the years 2081 and 2100.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Institute for Computational Science
Dewey Decimal Classification:510 Mathematics
Uncontrolled Keywords:Multi-scale process Multiresolution decomposition Geostatistical data Climate models
Language:English
Date:1 December 2022
Deposited On:28 Jan 2023 17:17
Last Modified:23 Sep 2024 03:35
Publisher:Elsevier
ISSN:2772-4158
OA Status:Gold
Publisher DOI:https://doi.org/10.1016/j.jcmds.2022.100063
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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