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GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research
BACKGROUND: Inaccurately modelled environmental exposures may have important implications for evidence-based policy targeting health promoting or hazardous facilities. Travel routes modelled using GIS generally use shortest network distances or Euclidean buffers to represent journeys with correspond...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147039/ https://www.ncbi.nlm.nih.gov/pubmed/32276644 http://dx.doi.org/10.1186/s12942-020-00208-2 |
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author | Mizen, Amy Fry, Richard Rodgers, Sarah |
author_facet | Mizen, Amy Fry, Richard Rodgers, Sarah |
author_sort | Mizen, Amy |
collection | PubMed |
description | BACKGROUND: Inaccurately modelled environmental exposures may have important implications for evidence-based policy targeting health promoting or hazardous facilities. Travel routes modelled using GIS generally use shortest network distances or Euclidean buffers to represent journeys with corresponding built-environment exposures calculated along these routes. These methods, however, are an unreliable proxy for calculating child built-environment exposures as child route choice is more complex than shortest network routes. METHODS: We hypothesised that a GIS model informed by characteristics of the built-environment known to influence child route choice could be developed to more accurately model exposures. Using GPS-derived walking commutes to and from school we used logistic regression models to highlight built-environment features important in child route choice (e.g. road type, traffic light count). We then recalculated walking commute routes using a weighted network to incorporate built-environment features. Multilevel regression analyses were used to validate exposure predictions to the retail food environment along the different routing methods. RESULTS: Children chose routes with more traffic lights and residential roads compared to the modelled shortest network routes. Compared to standard shortest network routes, the GPS-informed weighted network enabled GIS-based walking commutes to be derived with more than three times greater accuracy (38%) for the route to school and more than 12 times greater accuracy (92%) for the route home. CONCLUSIONS: This research advocates using weighted GIS networks to accurately reflect child walking journeys to school. The improved accuracy in route modelling has in turn improved estimates of children’s exposures to potentially hazardous features in the environment. Further research is needed to explore if the built-environment features are important internationally. Route and corresponding exposure estimates can be scaled to the population level which will contribute to a better understanding of built-environment exposures on child health and contribute to mobility-based child health policy. |
format | Online Article Text |
id | pubmed-7147039 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71470392020-04-18 GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research Mizen, Amy Fry, Richard Rodgers, Sarah Int J Health Geogr Research BACKGROUND: Inaccurately modelled environmental exposures may have important implications for evidence-based policy targeting health promoting or hazardous facilities. Travel routes modelled using GIS generally use shortest network distances or Euclidean buffers to represent journeys with corresponding built-environment exposures calculated along these routes. These methods, however, are an unreliable proxy for calculating child built-environment exposures as child route choice is more complex than shortest network routes. METHODS: We hypothesised that a GIS model informed by characteristics of the built-environment known to influence child route choice could be developed to more accurately model exposures. Using GPS-derived walking commutes to and from school we used logistic regression models to highlight built-environment features important in child route choice (e.g. road type, traffic light count). We then recalculated walking commute routes using a weighted network to incorporate built-environment features. Multilevel regression analyses were used to validate exposure predictions to the retail food environment along the different routing methods. RESULTS: Children chose routes with more traffic lights and residential roads compared to the modelled shortest network routes. Compared to standard shortest network routes, the GPS-informed weighted network enabled GIS-based walking commutes to be derived with more than three times greater accuracy (38%) for the route to school and more than 12 times greater accuracy (92%) for the route home. CONCLUSIONS: This research advocates using weighted GIS networks to accurately reflect child walking journeys to school. The improved accuracy in route modelling has in turn improved estimates of children’s exposures to potentially hazardous features in the environment. Further research is needed to explore if the built-environment features are important internationally. Route and corresponding exposure estimates can be scaled to the population level which will contribute to a better understanding of built-environment exposures on child health and contribute to mobility-based child health policy. BioMed Central 2020-04-10 /pmc/articles/PMC7147039/ /pubmed/32276644 http://dx.doi.org/10.1186/s12942-020-00208-2 Text en © The Author(s) 2020 Open AccessThis 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. |
spellingShingle | Research Mizen, Amy Fry, Richard Rodgers, Sarah GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research |
title | GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research |
title_full | GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research |
title_fullStr | GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research |
title_full_unstemmed | GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research |
title_short | GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research |
title_sort | gis-modelled built-environment exposures reflecting daily mobility for applications in child health research |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147039/ https://www.ncbi.nlm.nih.gov/pubmed/32276644 http://dx.doi.org/10.1186/s12942-020-00208-2 |
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