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Modern approaches to field data collection and mapping: Digital methods, crowdsourcing, and the future of statistical analyses

机译:现代数据收集和映射的现代方法:数字方法,众包和统计分析的未来

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Modern use of mobile devices for field geology has facilitated new approaches to, and methodologies for, field data collection. Here, we highlight current, state-of-the-art methods, including digital-compass measurements and field data collection with mobile devices, which facilitate crowdsourcing by novice geologists. Crowd-sourced collection of field data is advocated as a means of assembling big datasets for the construction of detailed geologic maps. However, expert control of field data is necessary to address inconsistencies in crowd-sourced novice datasets. Digital compasses on mobile devices can facilitate collection of field data by less experienced geologists. However, concerns exist regarding instrument-related data quality. We incorporate discussions of statistical methods that are relevant to evaluating the precision and accuracy of digital compasses as compared with analogue compasses. All compass platforms tested (Brunton Pocket Transits, iPhones, iPads, and Android-based phones) exhibited inconstancies in precision. However, the least reliable were Android-based devices. We argue that redundancy in measurements, coupled with assessing instrument drift through time, is necessary for all types of compasses. Statistical evaluation of compass measurements and other field data is arguably an important component of future mapping and data collection methods, as we adapt to the opportunities and challenges of assembling massive field datasets.
机译:现代使用用于现场地质的移动设备已经促进了新方法,以及现场数据收集的方法。在这里,我们突出了当前的最先进的方法,包括具有移动设备的数字式罗盘测量和现场数据收集,这促进了新手地质学家的众所周境。众所周知的现场数据集合作为组装大型数据集的手段,以建造详细的地质地图。但是,必须对现场数据进行专家控制,以解决人群源新手数据集中的不一致。移动设备上的数字指南可以通过不太经验丰富的地质学家促进现场数据的集合。但是,有关仪器相关数据质量的担忧。我们纳入了与评估数字罗盘的精度和准确性相关的统计方法的讨论,与模拟指南针相比。所有测试的罗盘平台(Brunton Pocket Transits,iPhone,iPad和基于Android的手机)在精确度上表现出Inconstancies。但是,最不可靠的是基于Android的设备。我们争辩说,在各种类型的指南针都有必要,再测量冗余,加上评估仪器漂移。罗盘测量和其他现场数据的统计评估可以说是未来映射和数据收集方法的重要组成部分,因为我们适应组装大规模场数据集的机会和挑战。

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