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

• Main Authors: Jesse Whitehead, Melody Smith, Yvonne Anderson, Yijun Zhang, Stephanie Wu, Shreya Maharaj, Niamh Donnellan
• Format: Article
• Language: English
• Published: BMC 2021-08-01
• Series: International Journal of Health Geographics
• Subjects: Measurement; Neighbourhood environments; Child health; Health behaviours; Health geography; Child-friendly cities
• Online Access: https://doi.org/10.1186/s12942-021-00288-8

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.