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Does nature make us happier? A spatial error model of greenspace types and mental wellbeing
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Houlden, Victoria, de Albuquerque, João Porto , Weich, Scott and Jarvis, Stephen A. (2021) Does nature make us happier? A spatial error model of greenspace types and mental wellbeing. Environment and Planning B: Urban Analytics and City Science, 48 (4). pp. 655-670. doi:10.1177/2399808319887395 ISSN 2399-8083.
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Official URL: http://dx.doi.org/10.1177/2399808319887395
Abstract
Exposure to nature is thought to benefit mental health and wellbeing. However, many studies consider greenspace as a single entity, which overlooks the potential significance of the various forms of greenspace, and natural greenspaces in particular. This study was designed to investigate the association between different types of greenspace and mental wellbeing. Drawing wellbeing and socioeconomic data from the Annual Population Survey (2012–2015), and shapefiles from the Greenspace Information for Greater London group, the amount of greenspace accessible within a 300 m walk of individual’s postcodes was calculated, and categorised according to type. Spatial Error Models were used to account for spatial patterns in the data. Natural greenspace was significantly associated with improved life satisfaction (B = 0.028, p < 0.001) and happiness (B = 0.023, p = 0.019) scores. The spatial autoregressive parameter (λ) was small but significant (p < 0.001), implying slight second-order spatial variation in the model. These results imply that natural areas may be more important for hedonic mental wellbeing than other greenspaces. Future research is needed on exploring causal relationships between exposure to greenspace and mental wellbeing outcomes
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Arts > School for Cross-faculty Studies Faculty of Arts > School for Cross-faculty Studies > Global Sustainable Development |
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| Journal or Publication Title: | Environment and Planning B: Urban Analytics and City Science | ||||||||
| Publisher: | Sage | ||||||||
| ISSN: | 2399-8083 | ||||||||
| Official Date: | 1 May 2021 | ||||||||
| Dates: |
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| Volume: | 48 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 655-670 | ||||||||
| DOI: | 10.1177/2399808319887395 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | Author(s), Article Title, Journal Title (Journal Volume Number and Issue Number) pp. xx-xx. Copyright © [year] (Copyright Holder). Reprinted by permission of SAGE Publications. Additionally, please provide a link to the appropriate DOI for the published version of the Contribution on the SAGE Journals website (http://journals.sagepub.com). | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
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