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Accounting for space and uncertainty in real‐time location system‐derived contact networks

机译:在实时位置系统派生联系网络中占空间和不确定性

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Point data obtained from real‐time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity‐based definitions of interanimal “contact,” however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining “contact” as polygon intersections. We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of “contact” to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks. Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions. By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network‐model realism and researchers' ability to draw inferences from RTLS data.
机译:从实时定位系统(RTLS)获得的点数据可以被处理成动物联系网络,描述跟踪个体之间的交互的实例。然而,基于接近的中间“联系人”的定义可能不足以用于描述流行病学和社会学相关的相互作用,涉及与跟踪装置相对远的身体部位或其他物理空间。通过使用多边形,而不是点,可以克服这种弱点来表示跟踪的个人并将“联系人”定义为多边形交叉口。我们提出了从RTLS点数据中派生多边形的新程序,同时保持与个人重定位事件相关的距离和方向。我们展示了使用两个联系网络创建示例的网络建模方法的多功能性,其中我们使用该过程来创建(a)中间物理联系网络和(b)视觉联系网络。此外,在创建网络时,我们建立另一个过程来调整“联系人”的定义,以解释RTLS位置准确性,确保所有真正的联系人可能捕获并在我们的网络中表示。使用本文描述的方法和我们已经开发的相关R包,称为联系人,研究人员可以从RTLS点导出多边形。此外,我们表明这些多边形对联系网络创建具有高度通用性,可用于回答各种流行病学,道德学和社会学研究问题。通过引入这些方法并提供通过联系R包轻松应用程序的方法,我们希望大大提高网络模型现实主义和研究人员借鉴RTLS数据的推论能力。

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