IoT 18(10): e1

Research Article

Studies in Small Scale Data: Three Case Studies on Describing Individuals’ Spatial Behaviour in Cities

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  • @ARTICLE{10.4108/eai.15-1-2018.153563,
        author={Lynnette Widder and Jessie Braden and Joy Ko and Kyle Steinfeld},
        title={Studies in Small Scale Data: Three Case Studies on Describing Individuals’ Spatial Behaviour in Cities},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={3},
        number={10},
        publisher={EAI},
        journal_a={IOT},
        year={2018},
        month={1},
        keywords={Resource Flows, Transportation, Human Factors, Visualization, Design Thinking, Apps, GIS, GPS.},
        doi={10.4108/eai.15-1-2018.153563}
    }
    
  • Lynnette Widder
    Jessie Braden
    Joy Ko
    Kyle Steinfeld
    Year: 2018
    Studies in Small Scale Data: Three Case Studies on Describing Individuals’ Spatial Behaviour in Cities
    IOT
    EAI
    DOI: 10.4108/eai.15-1-2018.153563
Lynnette Widder1,*, Jessie Braden2, Joy Ko3, Kyle Steinfeld4
  • 1: Lecturer, Columbia University Masters of Sustainability Management, New York NY 10027 USA
  • 2: Director, Pratt Institute Spatial Analysis/Visualization Initiative, Brooklyn, NY 11205 USA
  • 3: Adjunct Professor, Rhode Island School of Design Department of Architecture, Providence, RI 02903 USA
  • 4: Assistant Professor, University of California, Berkeley, Berkeley, CA 94720 USA
*Contact email: lw268@columbia.edu

Abstract

Big Data has been effectively mined to understand behavioural patterns in cities and to map large-scale trends predicated upon the repeated actions of many aggregated individuals. While acknowledging the vital role that this work has played in harnessing the Urban Internet of Things as a means to ensure efficient and sustainable urban systems, our work seeks to recover a scale of behavioural research associated with earlier, empirical studies on urban networks. UrbanIOT data expands the depth and precision of intimate behavioural analysis; small-scale analysis lends insight into important anomalies not explained by large-scale trends. The three case studies at stake here combined empirical journaling with data from mobile devices, tracking both automatically and through user reporting. Each produced diverse information and visualizations for describing the interaction of individual citizens, resources and urban systems. These are: a description of behaviours relative to food stores and shopping habits in New York City, US; a description of the correlation between mobility and food waste likelihood in Providence, RI, US; and a study of mobility patterns and personal choices in Copenhagen, DK.