Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/58529
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Aplinkos inžinerija / Environmental engineering (T004)
Author(s): Smith, Graham;Cirach, Marta;Swart, Wim;Dėdelė, Audrius;Gidlow, Christopher;Kempen, Elise van;Kruize, Hanneke;Gražulevičienė, Regina;Nieuwenhuijsen, Mark J
Title: Characterisation of the natural environment: quantitative indicators across Europe
Is part of: International journal of health geographics. London : BioMed central Ltd., 2017, Vol. 16, iss. 1
Extent: p. 1-15
Date: 2017
Note: PubMed ID: 28446187
Keywords: Physical activity;Health;Social interaction;Nature Parks;Urban planning;Stress
Abstract: Background: The World Health Organization recognises the importance of natural environments for human health. Evidence for natural environment-health associations comes largely from single countries or regions, with varied approaches to measuring natural environment exposure. We present a standardised approach to measuring neighbourhood natural environment exposure in cities in different regions of Europe. Methods: The Positive Health Effects of the Natural Outdoor environment in TYPical populations of different regions in Europe (PHENOTYPE) study aimed to explore the mechanisms linking natural environment exposure and health in four European cities (Barcelona, Spain; Doetinchem, the Netherlands; Kaunas, Lithuania; and Stoke-on-Trent, UK). Common GIS protocols were used to develop a hierarchy of natural environment measures, from simple measures (e.g., NDVI, Urban Atlas) using Europe-wide data sources, to detailed measures derived from local data that were specific to mechanisms thought to underpin natural environment-health associations (physical activity, social interaction, stress reduction/restoration). Indicators were created around residential addresses for a range of straight line and network buffers (100 m–1 km). Results: For simple indicators derived from Europe-wide data, we observed differences between cities, which varied with different indicators (e.g., Kaunas and Doetinchem had equal highest mean NDVI within 100 m buffer, but mean distance to nearest natural environment in Kaunas was more twice that in Doetinchem). Mean distance to nearest natural environment for all cities suggested that most participants lived close to some kind of natural environments (64 ± 58–363 ± 281 m; mean 180 ± 204 m). The detailed classification highlighted marked between-city differences in terms of prominent types of natural environment. Indicators specific to mechanisms derived from this classification also captured more variation than the simple indicators
Internet: https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-017-0090-z
Affiliation(s): Aplinkotyros katedra
Gamtos mokslų fakultetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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