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Identification of dominant features in spatial data

Flury, Roman; Gerber, Florian; Schmid, Bernhard; Furrer, Reinhard (2021). Identification of dominant features in spatial data. Spatial Statistics, 41:100483.

Abstract

Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales or resolutions. To identify dominant features, we propose a sequential application of multiresolution decomposition and variogram function estimation. Multiresolution decomposition separates data into additive components, and in this way enables the recognition of their dominant features. A dedicated multiresolution decomposition method is developed for arbitrary gridded spatial data, where the underlying model includes a precision and spatial-weight matrix to capture spatial correlation. The data are separated into their components by smoothing on different scales, such that larger scales have longer spatial correlation ranges. Moreover, our model can handle missing values, which is often useful in applications. Variogram function estimation can be used to describe properties in spatial data. Such functions are therefore estimated for each component to determine its effective range, which assesses the width-extent of the dominant feature. Finally, Bayesian analysis enables the inference of identified dominant features and to judge whether these are credibly different. The efficient implementation of the method relies mainly on a sparse-matrix data structure and algorithms. By applying the method to simulated data we demonstrate its applicability and theoretical soundness. In disciplines that use spatial data, this method can lead to new insights, as we exemplify by identifying the dominant features in a forest dataset. In that application, the width-extents of the dominant features have an ecological interpretation, namely the species interaction range, and their estimates support the derivation of ecosystem properties such as biodiversity indices.

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
07 Faculty of Science > Institute of Geography
08 Research Priority Programs > Global Change and Biodiversity
Dewey Decimal Classification:340 Law
610 Medicine & health
510 Mathematics
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Physical Sciences > Computers in Earth Sciences
Physical Sciences > Management, Monitoring, Policy and Law
Language:English
Date:1 March 2021
Deposited On:30 Mar 2021 08:31
Last Modified:25 Oct 2024 01:38
Publisher:Elsevier
ISSN:2211-6753
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.spasta.2020.100483
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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