Can’t see the wood for the trees? An assessment of street view- and satellite-derived greenness measures in relation to mental health
Autor: | Helbich, M, Poppe, R, Oberski, D, Zeylmans Van Emmichoven, M, Schram, R, Urban Accessibility and Social Inclusion, Sub Social and Affective Computing, Methodology and statistics for the behavioural and social sciences, Leerstoel Klugkist, Landscape functioning, Geocomputation and Hydrology, Landdegradatie en aardobservatie, ICT |
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Přispěvatelé: | Urban Accessibility and Social Inclusion, Sub Social and Affective Computing, Methodology and statistics for the behavioural and social sciences, Leerstoel Klugkist, Landscape functioning, Geocomputation and Hydrology, Landdegradatie en aardobservatie, ICT |
Rok vydání: | 2021 |
Předmět: |
Monitoring
Context (language use) Anxiety 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Normalized Difference Vegetation Index Correlation 03 medical and health sciences 0302 clinical medicine 11. Sustainability medicine 030212 general & internal medicine Urban living 0105 earth and related environmental sciences Nature and Landscape Conservation Green space Policy and Law Ecology Depression Deep learning 15. Life on land Mental health Uncertain geographic context Management Urban Studies Geography Street view imagery Computer vision Metric (unit) medicine.symptom Cartography |
Zdroj: | Landscape and Urban Planning Landscape and Urban Planning, 214, 1. Elsevier |
ISSN: | 0169-2046 |
DOI: | 10.1016/j.landurbplan.2021.104181 |
Popis: | Greenness in the urban living environment is inconsistently associated with mental health. Satellite-derived measures of greenness may inadequately characterize how people encounter greenness visually on site, but systematic comparisons are lacking. We aimed 1) to compare associations between remotely sensed and street view (SV) greenness, and 2) to examine whether these greenness metrics are differently associated with mental health outcomes. We used cross-sectional depressive and anxiety symptoms data on adults in Amsterdam, the Netherlands. We employed a convolutional neural network to segment greenness in SV panoramas. Greenness was measured top-down by normalized difference vegetation indices (NDVI) from 1 m resolution orthophotos (OP) and 30 m resolution Landsat-8 (LS) imagery per postal code, and 100 and 300 m concentric and street-network buffers at the home address. Correlation analyses assessed associations across greenness measures. Covariate-adjusted regressions (e.g., noise, air pollution, deprivation) were conducted to assess associations between each greenness metric and mental health outcomes. Correlations between greenness metrics were significantly positive and moderately high. SV greenness was less sensitive across scales and residential contexts than OP and LS greenness. There was no statistically significant evidence that people with less urban residential greenness had higher depression or anxiety scores than those exposed to higher levels. Nor did different greenness measures, scales, or residential context definitions alter our null associations. This suggests that even though SV and remotely sensed measures capture different aspects of greenness, these differences across exposure metrics did not translate into an association with mental health outcomes. |
Databáze: | OpenAIRE |
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