Spaceprint: a Mobility-based Fingerprinting Scheme for Spaces

Abstract

In this paper, we address the problem of how automated situational awareness in a specific location can be achieved by characterizing the fingerprint of recurrent situations from ubiquitously generated mobility data. Without semantic input about the time and space (location) where situations take place, this turns out to be a fundamental challenging problem. Uncertainties in data also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for finding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the actual identity of a space can be discovered from the extracted situation patterns.