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How fast is a cow? Cross-Scale Analysis of Movement Data

机译：母牛多快？运动数据的跨尺度分析

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摘要

Data representing the trajectories of moving point objects are becoming increasingly ubiquitous in GIScience, and are the focus of much methodological research aimed at extracting patterns and meaning describing the underlying phenomena. However, current research within GIScience in this area has largely ignored issues related to scale and granularity - in other words how much are the patterns that we see a function of the size of the looking glass that we apply? In this article we investigate the implications of varying the temporal scale at which three movement parameters, speed, sinuosity and turning angle are derived, and explore the relationship between this temporal scale and uncertainty in the individual data points making up a trajectory. A very rich dataset, representing the movement of 10 cows over some two days every 0.25s is investigated. Our cross-scale analysis shows firstly, that movement parameters for all 10 cows are broadly similar over a range of scales when the data are segmented to remove quasi-static subtrajectories. However, by exploring realistic values of GPS uncertainty using Monte Carlo Simulation, it becomes apparent that fine scale measurement of all movement parameters is masked by uncertainties, and that we can only make meaningful statements about movement when we take these uncertainties into account.
机译：代表运动点对象轨迹的数据在GIS科学中变得越来越普遍，并且是许多旨在提取描述潜在现象的模式和含义的方法论研究的重点。但是，当前在GIS科学领域内的研究在很大程度上忽略了与规模和粒度有关的问题-换句话说，我们看到的图案与所应用的窥镜尺寸之间的函数关系是多少？在本文中，我们研究了变化时标的含义，在该时标上得出三个运动参数，速度，正弦度和转弯角度，并探讨了该时标与构成轨迹的各个数据点的不确定性之间的关系。研究了非常丰富的数据集，该数据集代表每0.25秒的两天中10头奶牛的运动。我们的跨尺度分析表明，首先，当对数据进行分段以去除准静态子轨迹时，所有10头母牛的运动参数在一定范围的尺度上大致相似。但是，通过使用蒙特卡洛模拟探索GPS不确定性的现实值，很明显，所有运动参数的精细测量都被不确定性所掩盖，并且只有考虑到这些不确定性，我们才能对运动做出有意义的陈述。

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