Eason Tarsha, Ching-Chuang Wen, Sundstrom Shana, Cabezas Heriberto
U.S. Environmental Protection Agency, National Risk Management Research Laboratory, Research Triangle Park, North Carolina. USA.
National Research Council, U.S. Environmental Protection Agency, 26 W. Martin Luther King Drive, Cincinnati, Ohio 45268, USA.
Entropy (Basel). 2019 Feb;21(2). doi: 10.3390/e21020182. Epub 2019 Feb 15.
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 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. 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.
鉴于环境变化的强度和频率、社会生态系统的相互关联和跨尺度性质以及大数据的激增，能够帮助综合地理区域内复杂系统行为的方法具有巨大价值。费希尔信息评估数据中的秩序，并且已被确立为捕捉系统动态变化（包括状态和状态转变的检测）的强大有效工具。为计算费希尔信息而开发的方法可以处理各种类型的多变量数据，并且无需对系统驱动因素进行先验决策，使其成为一种独特而强大的工具。然而，该方法主要用于评估时间模式。在其唯一应用于空间数据时，费希尔信息成功检测了陆地和水生系统沿样带的状态。尽管选择相邻位置的采样站提供了一种对数据进行排序的自然方式，但这种方法限制了在空间背景下可以回答的问题类型。在此，我们扩展该方法以开发一种更全面捕捉空间动态的方法。结果反映了与地理模式相对应的指数变化，并证明了该方法在揭示复杂系统中隐藏的空间趋势方面的效用。
