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      Improving spatial data in health geographics: a practical approach for testing data to measure children's physical activity and food environments using Google Street View

      Whitehead, Jesse; Smith, Melody; Anderson, Yvonne; Zhang, Yijun; Wu, Stephanie; Maharaj, Shreya; Donnellan, Niamh
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      Improving spatial data in health geographics a practical approach for testing data to measure childrens physical activity an.pdf
      Published version, 1.227Mb
      DOI
       10.1186/s12942-021-00288-8
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      Permanent link to Research Commons version
      https://hdl.handle.net/10289/14981
      Abstract
      Background

      Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand.

      Methods

      We used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets.

      Results

      A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated.

      Conclusions

      Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.
      Date
      2021
      Type
      Journal Article
      Publisher
      BMC
      Rights
      This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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      • Arts and Social Sciences Papers [1423]
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