Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements.ududB. Speckmann and K. Verbeek are supported by the Netherlands Organisation for Scientific Research (NWO) under project nos. 639.023.208 and 639.021.541, respectively. This paper arose from work initiated at Dagstuhl seminar 12512 “Representation, analysis and visualization of moving objects”, December 2012. The authors gratefully acknowledge Schloss Dagstuhl for their support.
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机译:运动数据有多种形式,包括轨迹数据和检查点数据。尽管轨迹提供了有关单个实体运动的详细信息,但最简单形式的检查点数据并不提供身份信息,而只是在检查点计数。但是,检查点数据越来越受到关注,因为由于隐私原因以及其他数据收集的副产品,它很容易获得。在本文中,我们建议使用“土行者的距离”作为一种通用工具,根据不同时间的检查点计数来重建单个运动或流量。我们基于英国伦敦快递人员实际活动的粗粒度数据汇总,分析了建模的可能性并提供了验证模型预测的实验。虽然我们不能期望从高度粒度的检查点数据中重建出精确的个体运动,但评估的确表明该方法可以生成有意义的物体运动估计。 ud udB。 Speckmann和K. Verbeek在项目编号为N的荷兰科学研究组织(NWO)的支持下。 639.023.208和639.021.541。本文源于2012年12月在Dagstuhl研讨会12512“运动对象的表示,分析和可视化”中发起的工作。作者非常感谢Schloss Dagstuhl的支持。
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