Infostop：多用户移动数据中的可扩展停靠位置检测
Infostop: Scalable stop-location detection in multi-user mobility data
摘要：
近年来，数据驱动的移动性研究蓬勃发展，为现实世界的挑战提供了解决方案，包括预测流行病和规划交通。这些进步得益于能够分析大规模数字痕迹数据集的计算工具。预处理空间轨迹时的挑战之一是所谓的停止位置检测，这需要将原始时间序列减少到个人静止的目的地序列。Hariharan和Toyama（2004）提出了这个问题的最广泛采用的解决方案，包括过滤掉非平稳测量，然后在平稳点上应用凝聚聚类。然而，这种现有技术的解决方案受到两个限制：（i）由于固有的测量噪声，位于非常接近的频繁访问的地方（诸如相邻的建筑物）可能被合并到唯一的位置，（ii）不能同时分析多个用户的轨迹，因此目的地的定义不能在用户之间共享。在本文中，我们描述的Infostop算法，克服了国家的最先进的解决方案的局限性，利用基于流的网络社区检测算法Infomap。我们测试Infostop人口的$\sim 1000$个人高度重叠的流动性。我们发现，Infostop检测到的位置的大小饱和的用户数量的增加和时间复杂性增长速度比以前的解决方案。我们证明，Infostop可以用来很容易地推断社交会议。最后，我们提供了一个用Python和C++编写的Infostop的开源实现，它有一个简单的API，可用于标记时间顺序坐标序列（GPS或其他）和无序的空间点集。
英文摘要：
Data-driven research in mobility has prospered in recent years, providing solutions to real-world challenges including forecasting epidemics and planning transportation. These advancements were facilitated by computational tools enabling the analysis of large-scale data-sets of digital traces. One of the challenges when pre-processing spatial trajectories is the so-called stop location detection, that entails the reduction of raw time series to sequences of destinations where an individual was stationary. The most widely adopted solution to this problem was proposed by Hariharan and Toyama (2004) and involves filtering out non-stationary measurements, then applying agglomerative clustering on the stationary points. This state-of-the-art solution, however, suffers of two limitations: (i) frequently visited places located very close (such as adjacent buildings) are likely to be merged into a unique location, due to inherent measurement noise, (ii) traces for multiple users can not be analysed simultaneously, thus the definition of destination is not shared across users. In this paper, we describe the Infostop algorithm that overcomes the limitations of the state-of-the-art solution by leveraging the flow-based network community detection algorithm Infomap. We test Infostop for a population of $\sim 1000$ individuals with highly overlapping mobility. We show that the size of locations detected by Infostop saturates for increasing number of users and that time complexity grows slower than for previous solutions. We demonstrate that Infostop can be used to easily infer social meetings. Finally, we provide an open-source implementation of Infostop, written in Python and C++, that has a simple API and can be used both for labeling time-ordered coordinate sequences (GPS or otherwise), and unordered sets of spatial points.
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