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Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA

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  • Merrill, Nathaniel
  • Atkinson, Sarina F.
  • Mulvaney, Kate K.
  • Mazzotta, Marisa J.
  • Bousquin, Justin

Abstract

We introduce and validate the use of commercially-available datasets of human mobility based on cell phone locational data to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to over 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. The high-resolution information in both space and time provided by cell phone location derived data creates opportunities for developing next-generation models of human interactions with the natural environment. However, the opaque and rapidly developing methods for processing locational information by the data providers required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation.

Suggested Citation

  • Merrill, Nathaniel & Atkinson, Sarina F. & Mulvaney, Kate K. & Mazzotta, Marisa J. & Bousquin, Justin, 2019. "Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA," SocArXiv 3nx2v_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:3nx2v_v1
    DOI: 10.31219/osf.io/3nx2v_v1
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    References listed on IDEAS

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    1. Van Berkel, Derek B. & Tabrizian, Payam & Dorning, Monica A. & Smart, Lindsey & Newcomb, Doug & Mehaffey, Megan & Neale, Anne & Meentemeyer, Ross K., 2018. "Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR," Ecosystem Services, Elsevier, vol. 31(PC), pages 326-335.
    2. Mingxiao Li & Song Gao & Feng Lu & Huan Tong & Hengcai Zhang, 2019. "Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
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