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一种用于分析动物运动数据的新多尺度度量方法。

A new multi-scale measure for analysing animal movement data.

机构信息

Department of Mathematics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.

出版信息

J Theor Biol. 2013 Jan 21;317:175-85. doi: 10.1016/j.jtbi.2012.10.007. Epub 2012 Oct 16.

Abstract

We present a new measure for analysing animal movement data, which we term a 'Multi-Scale Straightness Index' (MSSI). The measure is a generalisation of the 'Straightness Index', the ratio of the beeline distance between the start and end of a track to the total distance travelled. In our new measure, the Straightness Index is computed repeatedly for track segments at all possible temporal scales. The MSSI offers advantages over the standard Straightness Index, and other simple measures of track tortuosity (such as Sinuosity and Fractal Dimension), because it provides multiple characterisations of straightness, rather than just a single summary measure. Thus, comparisons can be made among different segments of trajectories and changes in behaviour can be inferred, both over time and at different temporal granularities. The measure also has an important advantage over several recent and increasingly popular methods for detecting behavioural changes in time-series locational data (e.g., state-space models and positional entropy methods), in that it is extremely simple to compute. Here, we demonstrate use of the MSSI on both synthetic and real animal-movement trajectories. We show how behavioural changes can be inferred within individual tracks and how behaviour varies across spatio-temporal scales. Our aim is to present a useful tool for researchers requiring a computationally simple but effective means of analysing the movement patterns of animals.

摘要

我们提出了一种新的分析动物运动数据的度量方法,我们称之为“多尺度直线度指数”(MSSI)。该度量方法是“直线度指数”的推广,即轨迹起点和终点之间的直线距离与总行程的比值。在我们的新度量中,直线度指数在所有可能的时间尺度上对轨迹段进行重复计算。MSSI 提供了比标准直线度指数和其他简单的轨迹曲折度度量(如正弦度和分形维数)的优势,因为它提供了多个直线度特征,而不仅仅是一个单一的总结度量。因此,可以在不同的轨迹段之间进行比较,并可以推断行为的变化,无论是随着时间的推移还是在不同的时间粒度上。该度量方法还具有一个重要的优势,即相对于几种最近越来越流行的用于检测时间序列位置数据中行为变化的方法(例如状态空间模型和位置熵方法),它的计算非常简单。在这里,我们在合成和真实动物运动轨迹上展示了 MSSI 的使用。我们展示了如何在单个轨迹内推断行为变化,以及行为如何在时空尺度上变化。我们的目的是为需要一种计算简单但有效的分析动物运动模式的方法的研究人员提供一个有用的工具。

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