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An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems

Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in da...

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Autores principales: Eason, Tarsha, Chuang, Wen-Ching, Sundstrom, Shana, Cabezas, Heriberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688651/
https://www.ncbi.nlm.nih.gov/pubmed/31402835
http://dx.doi.org/10.3390/e21020182
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author Eason, Tarsha
Chuang, Wen-Ching
Sundstrom, Shana
Cabezas, Heriberto
author_facet Eason, Tarsha
Chuang, Wen-Ching
Sundstrom, Shana
Cabezas, Heriberto
author_sort Eason, Tarsha
collection PubMed
description Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. The methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. The results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.
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spelling pubmed-66886512020-02-01 An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems Eason, Tarsha Chuang, Wen-Ching Sundstrom, Shana Cabezas, Heriberto Entropy (Basel) Article Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. The methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. The results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems. MDPI 2019-02-15 /pmc/articles/PMC6688651/ /pubmed/31402835 http://dx.doi.org/10.3390/e21020182 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Eason, Tarsha
Chuang, Wen-Ching
Sundstrom, Shana
Cabezas, Heriberto
An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems
title An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems
title_full An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems
title_fullStr An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems
title_full_unstemmed An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems
title_short An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems
title_sort information theory-based approach to assessing spatial patterns in complex systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688651/
https://www.ncbi.nlm.nih.gov/pubmed/31402835
http://dx.doi.org/10.3390/e21020182
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