ArticleInternational journal of health geographics2021
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.
Article in International journal of health geographics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Is the neighbourhood environment associated with indicators of health in children and adolescents? Developing and testing a new proof-of-concept Healthy Environments Index for Children in Taranaki, New Zealand.International journal of health geographics · 2025Article
- Analysis of the spatio-temporal evolutionary characteristics of myopia among students aged 7-18 years in China: based on panel data analysis.Scientific reports · 2024Article
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Authors and funding
7 authors.
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Abstract
backgroundGeographic 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.
methodsWe 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.
resultsA 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.
conclusionsOur 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.
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