  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.

Machine Learning;Applications