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A spatial analysis of proximate greenspace and mental wellbeing in London
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Houlden, Victoria, Porto de Albuquerque, João , Weich, Scott and Jarvis, Stephen A. (2019) A spatial analysis of proximate greenspace and mental wellbeing in London. Applied Geography, 109 . 102036. doi:10.1016/j.apgeog.2019.102036 ISSN 0143-6228.
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WRAP-spatial-analysis-proximate-greenspace-mental-wellbeing-London-Jarvis-2019.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1648Kb) | Preview |
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WRAP-spatial-analysis-proximate-greenspace-mental-wellbeing-London-Houlden-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1144Kb) | Preview |
Official URL: http://doi.org/10.1016/j.apgeog.2019.102036
Abstract
While local-area greenspace is associated with reduced symptoms of mental distress and greater life satisfaction, most previous research has measured the amount of local-area greenspace within administrative boundaries, and found mixed results for associations between greenspace and multidimensional mental wellbeing. The study was designed to examine whether the amount of greenspace within a radius of individuals’ homes was associated with mental wellbeing, testing the government guideline that greenspace should be available within 300 m of homes.
Individual and Household-level data were drawn from the Annual Population Survey at postcode level (APS, Pooled Dataset 2012–2015), which includes 3 mental wellbeing measures, covering aspects of life satisfaction, sense of worth, and happiness, as well as a range of socio-demographic variables. Greenspace data were obtained Greenspace Information for Greater London Group (GiGL), and was used to calculated the amount of greenspace within a 300 m radius of individuals. Linear regression models revealed positive and statistically significant associations between the amount of greenspace and indicators of life satisfaction and worth. Moran's I, an indicator of spatial autocorrelation, revealed statistically significant clustering of the residuals of these models, so Geographically Weighted Regression (GWR) models were calculated, in order to adjust for underlying spatial processes within the data and investigate the geographic variation in the association between local greenspace and mental wellbeing. The global GWR model revealed that an increase in 1 ha of greenspace within 300 m of residents was associated with a statistically significant 0.803 increase in life satisfaction, 0.740 and 0.521 for worth and happiness, respectively. This therefore provides some support for the inclusion of greenspace within 300 m of homes. Local GWR coefficients revealed slight variation in the strength of these associations across the study space. Therefore, further analyses are required to investigate whether the walking (network distance), absolute size, or type of each greenspace are able to explain this spatial variation.
| Item Type: | Journal Article | ||||||||
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| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Social Sciences > Centre for Interdisciplinary Methodologies |
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| Library of Congress Subject Headings (LCSH): | Greenbelts -- Health aspects -- London -- Great Britain, Public spaces -- Social aspects -- London -- Great Britain, Recreation areas, Open spaces -- Health aspects -- London -- Great Britain, Well-being -- Great Britain | ||||||||
| Journal or Publication Title: | Applied Geography | ||||||||
| Publisher: | Elsevier Inc. | ||||||||
| ISSN: | 0143-6228 | ||||||||
| Official Date: | August 2019 | ||||||||
| Dates: |
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| Volume: | 109 | ||||||||
| Article Number: | 102036 | ||||||||
| DOI: | 10.1016/j.apgeog.2019.102036 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 26 June 2019 | ||||||||
| Date of first compliant Open Access: | 2 September 2019 | ||||||||
| RIOXX Funder/Project Grant: |
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