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

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 bir...

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Autores principales: Xu, Xiaohui, Hu, Hui, Ha, Sandie, Han, Daikwon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5800510/
https://www.ncbi.nlm.nih.gov/pubmed/27903063
http://dx.doi.org/10.4081/gh.2016.482
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author Xu, Xiaohui
Hu, Hui
Ha, Sandie
Han, Daikwon
author_facet Xu, Xiaohui
Hu, Hui
Ha, Sandie
Han, Daikwon
author_sort Xu, Xiaohui
collection PubMed
description 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 smartphone-assisted 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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spelling pubmed-58005102018-02-06 Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset Xu, Xiaohui Hu, Hui Ha, Sandie Han, Daikwon Geospat Health Article 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 smartphone-assisted 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. 2016-11-23 /pmc/articles/PMC5800510/ /pubmed/27903063 http://dx.doi.org/10.4081/gh.2016.482 Text en http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (CC BY-NC 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Article
Xu, Xiaohui
Hu, Hui
Ha, Sandie
Han, Daikwon
Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
title Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
title_full Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
title_fullStr Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
title_full_unstemmed Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
title_short Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
title_sort smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5800510/
https://www.ncbi.nlm.nih.gov/pubmed/27903063
http://dx.doi.org/10.4081/gh.2016.482
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