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

Submitted: 12 April 2016
Accepted: 1 September 2016
Published: 23 November 2016
Abstract Views: 2597
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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.

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Supporting Agencies

NIH/NIEHS
Xiaohui Xu, Department of Epidemiology and Biostatistics, Texas A&M University, College Station, TX

Xiaohui Xu, PhD, MS , Associate Professor
Department of Epidemiology & Biostatistics

How to Cite

Xu, X., Hu, H., Ha, S., & Han, D. (2016). Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset. Geospatial Health, 11(3). https://doi.org/10.4081/gh.2016.482

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