Wind David Kofoed, Sapiezynski Piotr, Furman Magdalena Anna, Lehmann Sune
DTU Compute, Technical University of Denmark, Copenhagen, Denmark.
PLoS One. 2016 Feb 22;11(2):e0149105. doi: 10.1371/journal.pone.0149105. eCollection 2016.
Human mobility patterns are inherently complex. In terms of understanding these patterns, the process of converting raw data into series of stop-locations and transitions is an important first step which greatly reduces the volume of data, thus simplifying the subsequent analyses. Previous research into the mobility of individuals has focused on inferring 'stop locations' (places of stationarity) from GPS or CDR data, or on detection of state (static/active). In this paper we bridge the gap between the two approaches: we introduce methods for detecting both mobility state and stop-locations. In addition, our methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select the most important routers and one which uses a density-based clustering algorithm to detect router fingerprints. We validate our results using participants' GPS data as well as ground truth data collected during a two month period.
人类移动模式本质上是复杂的。在理解这些模式方面，将原始数据转换为一系列停留位置和转移的过程是重要的第一步，这极大地减少了数据量，从而简化了后续分析。先前对个体移动性的研究主要集中在从GPS或CDR数据中推断“停留位置”（静止地点），或检测状态（静态/活动）。在本文中，我们弥合了这两种方法之间的差距：我们介绍了检测移动状态和停留位置的方法。此外，我们的方法完全基于WiFi数据。我们研究了智能手机每两分钟收集一次的两个月WiFi数据，并以标记时间间隔的形式推断停留位置。为此，我们研究了两种算法，这两种算法都能扩展到大型数据集：一种是选择最重要路由器的贪心方法，另一种是使用基于密度的聚类算法来检测路由器指纹。我们使用参与者的GPS数据以及在两个月期间收集的地面真值数据来验证我们的结果。
