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Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset

Xiaohui Xu, Hui Hu, Sandie Ha, Daikwon Han
  • Xiaohui Xu
    Department of Epidemiology and Biostatistics, Texas A&M University, College Station, TX, United States | xiaohui.xu@sph.tamhsc.edu
  • Hui Hu
    Department of Epidemiology, University of Florida, Gainesville, FL, United States
  • Sandie Ha
    Department of Epidemiology, University of Florida, Gainesville, FL, United States
  • Daikwon Han
    Department of Epidemiology and Biostatistics, Texas A&M University, College Station, TX, United States

Abstract

It is well known that the conventional, automated geocoding method based on self-reported residential addresses has many issues. We developed a smartphone-assisted aerial image-based method, which uses the Google Maps application programming interface as a spatial data collection tool during the birth registration process. In this pilot study, we have tested whether the smartphone-assisted method provides more accurate geographic information than the automated geocoding method in the scenario when both methods can get the address geocodes. We randomly selected 100 well-geocoded addresses among women who gave birth in Alachua county, Florida in 2012. We compared geocodes generated from three geocoding methods: i) the smartphone-assisted aerial image-based method; ii) the conventional, automated geocoding method; and iii) the global positioning system (GPS). We used the GPS data as the reference method. The automated geocoding method yielded positional errors larger than 100 m among 29.3% of addresses, while all addresses geocoded by the smartphoneassisted method had errors less than 100 m. The positional errors of the automated geocoding method were greater for apartment/condominiums compared with other dwellings and also for rural addresses compared with urban ones. We conclude that the smartphone-assisted method is a promising method for perspective spatial data collection by improving positional accuracy.

Keywords

Positional accuracy; Geocode; Vital statistics; Bias; Environmental epidemiology

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Submitted: 2016-04-12 21:46:51
Published: 2016-11-23 16:53:09
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Copyright (c) 2016 Xiaohui Xu, Hui Hu, Sandie U Ha, Daikwon Han

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