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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6688651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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