Mining the Thin Air - For Understanding of Urban Society

Ron Bekkerman, Adi Zmirli, Scott Kirkpatrick

    Research output: Contribution to journalArticlepeer-review

    Abstract

    We explore the potential of crowd-sourced information on human mobility and activities in an urban population drawn from a significant fraction of smartphones in the Los Angeles basin during February-May 2015. The raw dataset was collected by WeFi, a smartphone app provider. The dataset is noisy, irregular, and lean; however, it is large scale (over a billion events), cheap to collect, and arguably unbiased. We employ the state-of-the-art Big Data techniques to turn this structurally thin dataset into semantically rich insights on commuting, overworking, recreational traveling, shopping, and fast food consumption of the Greater LA population. For example, we reveal that Greater LA residents commute substantially longer than what is reported in the US census data. Also, we show that younger individuals dine at McDonald's significantly more than the older population does. Our results have implications for public health, inequality, urban traffic, and other research areas in social sciences. The large number of phones participating in our "crowd" makes it possible to obtain those results without the risk of compromising individual privacy.

    Original languageEnglish
    Pages (from-to)262-275
    Number of pages14
    JournalBig Data
    Volume7
    Issue number4
    DOIs
    StatePublished - Dec 2019

    Bibliographical note

    Publisher Copyright:
    © Copyright 2019, Mary Ann Liebert, Inc., publishers 2019.

    Keywords

    • data science
    • human mobility
    • public health

    ASJC Scopus subject areas

    • Information Systems and Management
    • Information Systems
    • Computer Science Applications

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