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      Tracking Human Mobility Using WiFi Signals

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          Abstract

          We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking.

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          Understanding individual human mobility patterns

          M. González, A L Barabási, C. Hidalgo (2008)
          Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.
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            Modelling disease outbreaks in realistic urban social networks.

            Stephen Eubank, Hasan Guclu, V. Anil Kumar … (2004)
            Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.
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              Unique in the Crowd: The privacy bounds of human mobility

              Yves-Alexandre de Montjoye, César Hidalgo, Michel Verleysen … (2013)
              We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
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                Author and article information

                Contributors
                Ye Wu: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2015
                Publication date (Electronic): 1 July 2015
                Volume: 10
                Issue: 7
                Electronic Location Identifier: e0130824
                Affiliations
                [1 ]Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
                [2 ]Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States of America
                [3 ]Department of Economics, University of Copenhagen, Copenhagen, Denmark
                [4 ]Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
                Beijing University of Posts and Telecommunications, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SL AS PS. Performed the experiments: PS AS RG. Analyzed the data: PS RG. Wrote the paper: PS AS RG SL.

                Article
                Publisher ID: PONE-D-15-02827
                DOI: 10.1371/journal.pone.0130824
                PMC ID: 4489206
                PubMed ID: 26132115
                SO-VID: 94b8e8ba-3917-4aa8-8709-0f4888f837ca
                Copyright statement: Copyright @ 2015
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                Date received : 20 January 2015
                Date accepted : 26 May 2015
                Page count
                Figures: 3, Tables: 0, Pages: 11
                Funding
                This work was supported by Villum Foundation, http://villumfoundation.dk/C12576AB0041F11B/0/4F7615B6F43A8EA5C1257AEF003D9930?OpenDocument, Young Investigator programme 2012, High Resolution Networks (SL) and University of Copenhagen, http://dsin.ku.dk/news/ucph_funds/, through the UCPH2016 Social Fabric grant (SL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Custom metadata
                Data Availability Data are from Copenhagen Networks study ( http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0095978). Due to privacy consideration regarding subjects in our dataset, including European Union regulations and Danish Data Protection Agency rules, we cannot make our data publicly available. The data contains detailed information on mobility and daily habits of 63 individuals at a high spatio-temporal resolution. We understand and appreciate the need for transparency in research and are ready to make the data available to researchers who meet the criteria for access to confidential data, sign a confidentiality agreement, and agree to work under our supervision in Copenhagen. Please direct your queries to Sune Lehmann, the Principal Investigator of the study, at sljo@ 123456dtu.dk .

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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