Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this paper is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely k-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through such a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location, and we present the results obtained by applying our proposed approach on a real-world dataset collected over five months.

翻译：有关城市环境中人类的时空流动的信息有各种各样的应用。目前,虽然收集此类数据有多种不同的方法,但缺乏标准化的分析框架。本文件的重点是分析通过被动的无线网络遥感收集的数据,因为这种被动收集的数据可以低成本地广泛覆盖。我们建议采用一种系统的方法,利用不受监督的机器学习方法,即K-手段集群和等级聚居群(HAC)来分析通过这种被动的无线网络嗅觉方法收集的数据。我们研究了数据组合的三个方面,即按时间、人和地点,我们介绍了在五个月中收集的真实世界数据集中应用我们建议的方法取得的结果。