An information theory-based approach to assessing spatial patterns in complex systems
Citation:
Eason, T., W. Chuang, S. Sundstrom, AND H. Cabezas. An information theory-based approach to assessing spatial patterns in complex systems. Entropy. MDPI, Basel, Switzerland, 21(2):182, (2019).
Impact/Purpose:
This paper is in response to the growing availability of large geospatial datasets and the need to develop methods that not only provide insight on observable phenomena but possess the ability to capture hidden patterns in complex, multivariate systems. This work presents the novel development and application of Fisher information to assessing geospatial patterns in complex systems. Methods developed produce results that reflect changes in the index which correspond with geospatial patterns and demonstrate the method as a valuable tool for mining spatial data to detect latent characteristics and signals in complex systems. Such work is particularly important for addressing the linked, cross-scale environmental problems we face today where drivers and options for managing coupled human and natural systems are unknown or difficult to identify.
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. Methods developed 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 method 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 could be answered in a spatial context. In this work,we expand the approach to develop a method for capturing spatial dynamics. Results reflect changes in the index that correspond with geographical patterns and demonstrates the utility of the method in uncovering hidden spatial trends in complex systems.
