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Intrinsic dimensionality of human behavioral activity data

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  • Luana Fragoso
  • Tuhin Paul
  • Flaviu Vadan
  • Kevin G Stanley
  • Scott Bell
  • Nathaniel D Osgood

Abstract

Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two.

Suggested Citation

  • Luana Fragoso & Tuhin Paul & Flaviu Vadan & Kevin G Stanley & Scott Bell & Nathaniel D Osgood, 2019. "Intrinsic dimensionality of human behavioral activity data," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.
  • Handle: RePEc:plo:pone00:0218966
    DOI: 10.1371/journal.pone.0218966
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Sevilla-Casas, Elías, 1993. "Human mobility and malaria risk in the Naya river basin of Colombia," Social Science & Medicine, Elsevier, vol. 37(9), pages 1155-1167, November.
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    1. Jinli Hu & Jueying Wu & Yangyang Sun & Xinyu Zhao & Guang Hu, 2023. "Spatiotemporal Influence of Urban Park Landscape Features on Visitor Behavior," Sustainability, MDPI, vol. 15(6), pages 1-13, March.

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