Table 3.
Summary of findings.
| Study * | Measure of Quality | Tool(s) Used to Assess Green Space Quality ** | Outcome | Outcome Assessment Tool ** | Direction of Effect ≠ |
|---|---|---|---|---|---|
| Environment/land cover type (n = 22) | |||||
| Marselle, 2015 (n = 127) [22] |
Environment types: natural and semi-natural places, green corridor, urban green space, farmland, urban public spaces, coastal, mixture | Self-reported | Positive & negative affect | PANAS scale | (o) |
| Stas, 2021 (n = 189) [79] |
Vegetation species and cover types: trees vs. grass | GIS analysis | Severe tree pollen allergy event | Self-reported | (+) |
| Astell-Burt, 2020 (n = 109,688) [46] |
Vegetation cover types: trees vs. grass | GIS analysis | Dementia: first medication prescription, first hospitalisation and deaths | Medical records | (+) |
| Astell-Burt, 2019 (n = 46,786) [45] |
Vegetation cover types: trees, grass vs. low-lying vegetation | GIS analysis | Psychological stress; depression/anxiety; general health | K10-PDS; self-reported | (+) |
| Richardson, 2018 (n = 46,093) [41] |
Natural space types: parks, woods, open waters | GIS analysis | Live births | Medical records | (+) |
| Astell-Burt, 2020 (n = 45,644) [48] |
Vegetation cover types: trees vs. grass | GIS analysis | Memory complaints; self-rated memory | Semantic differential scale | (o) |
| Astell-Burt, 2021 (n = 45,644) |
Vegetation cover types: trees vs. open grass | GIS analysis | CVD mortality, CVD events, AMI | Medical records | (o) |
| Gernes, 2019 (n = 478) [28] |
Land cover diversity | GIS analysis | Outdoor allergen sensitisation; allergic rhinitis | Skin prick tests; clinically diagnosed | (–) |
| Donovan, 2018 (n = 39,108) [56] |
Vegetation cover types | GIS analysis | Childhood asthma | Medical records | (+)(–) |
| Parmes, 2020 (n = 8063) [35] |
Forest types: deciduous, coniferous vs. mixed | GIS analysis | Wheezing, asthma, allergic rhinitis, eczema | Parental reported | (–) |
| Jarvis, 2020 (n = 1,960,575) [64] |
Land cover types: coniferous, deciduous, shrub, grass-herbs, water, buildings, paved surfaces | GIS analysis | General health, mental health, common mental disorders | Semantic differential scale | (+) |
| Nishigaki, 2020 (n = 126,878) [26] |
Vegetation cover types: trees vs. grass | GIS analysis | Depression | SGD | (+) |
| Wyles, 2019 (n = 4515) [86] |
Environment types: coastal, rural green vs urban green | Self-reported | Restorativeness | Semantic differential scale | (+) |
| Reid, 2017 (n = 1387) [74] |
Vegetation cover types: trees vs. grass | GIS analysis | Perceived health | Semantic differential scale | (+) |
| Jiang, 2020 (n = 212) [65] |
Vegetation cover types: trees vs. low-lying vegetation | GIS analysis | General health; stress level | SF-12; PSS | (+)(–) |
| Egorov, 2020 (n = 186) [59] |
Vegetation cover types: trees vs. grass | GIS analysis | Allostatic load | Clinically measured | (+) |
| Wheeler, 2015 (n = 31,672 LSOAs) [83] |
Land cover diversity and environment types | SDI; GIS analysis | Health status | Semantic differential scale | (+) |
| Aerts, 2020 (n = 1872 census tracts) [34] |
Land cover types: gardens, forests vs. grassland | GIS analysis | Respiratory diseases | Medication sales | (+) |
| Dennis, 2020 (n = 1673 LSOAs) [53] |
Land cover diversity; vegetation cover types (ground, canopy vs. field-level) | SDI; GIS analysis | Chronic morbidity prevalence | CIDR | (+) |
| Sander, 2017 (n = 546 census blocks) [76] |
Land cover types: water, forest, canopy, impervious surfaces, and grass | GIS analysis | BMI | Self-measured height & weight | (+) |
| Wu, 2017 (n = 543 districts) [33] |
Vegetation cover types: forest, grassland, average tree canopy and near-road tree canopy |
GIS analysis (50 m and 100 m buffers) | Autism | Medical records | (+) |
| Wu, 2018 (n = 187 census tracts) [85] |
Land cover types: water, open land, developed land, barren land, forest, shrub land, grassland, agriculture and wetland | GIS analysis (50 m and 100 m buffers) | Sudden unexpected deaths | Medical records | (+) |
| Natural features (n = 15) | |||||
| Marselle, 2015 (n = 127) [22] |
Perceived naturalness; bird, butterfly, plants and trees biodiversity | Semantic differential scale, manual counting of species | Positive and negative affect | PANAS scale | (–) |
| Astell-Burt, 2020 (n = 46,786) [47] |
Tree coverage | GIS analysis | Diabetes, hypertension and cardiovascular diseases | Medical records | (+) |
| Wyles, 2019 (n = 4515) [86] |
Protected/designated area status | Assigned by national agency | Restorativeness | Semantic differential scale | (+) |
| Leng, 2020 (n = 4155) [70] |
Presence of evergreen trees | Environmental audits | Obesity, hypertension, diabetes, dyslipidaemia, stroke risk | Clinically measured BMI, blood pressure, blood glucose and lipid tests, stroke risk score card | (+) |
| Camargo, 2017 (n = 1392) [51] |
Conditions of trees | Semantic differential scale | Quality of life | EUROHIS-QOL | (+) |
| Zhang, 2019 (n = 909) [24] |
Tree density | POST | Quality of life | WHOQOL-BREF | (+) |
| Carter, 2014 (n = 440) [52] |
Retention of green space and bushland | Semantic differential scale | Physical function | SF-36v2 | (o) |
| Tan, 2019 (n = 326) [23] |
Tree density | Environmental audits | Physical functioning, physical role, bodily pain and emotional role | SF-12v2 | (o) |
| Pazhouhanfar, 2018 (n = 250) [73] |
Tree and greening, flowers, sun, water, fresh air, and bird voice | Semantic differential scale | Mood ratings (relaxed/happy/excited/calmed) | Semantic differential scale | (+) |
| Zhu, 2020 (n = 240) [89] |
Sky index, soft/hard surface ratio, vertical vegetation coverage | Grid pixel calculation | Restorative effect | PRS | (+)(–) |
| Wood, 2018 (n = 128) [44] |
Ecological study richness score: plant diversity, bird diversity, bee/butterfly diversity, number of habitats | Environmental audits; SDI | Restorative effect | Modified ART | (+) |
| Honold, 2016 (n = 32) [62] |
Diversity of vegetation: façade, design, building shapes, vanishing points, angles | Semantic differential scale | Stress level | Hair cortisol level (immunoassay) | (o) |
| Wheeler, 2015 (n = 31,672 LSOAs) [83] |
Bird species richness, freshwater quality indicator, density of protected area density | Bird occurrence atlas, routine surface water testing | Health status | Semantic differential scale | (+) |
| Mears, 2020 (n = 345 LSOAs) [42] |
Bird biodiversity | Citizen science programme data | Poor general health | Semantic differential scale | (o) |
| Lai, 2019 (n = 174 zip codes) [69] |
Pollen allergenicity of trees | Street tree census | Asthma prevalence | Medical records | (–) |
| Infrastructure & amenities (n = 14) | |||||
| Droomers, 2015 (n = 48,132) [57] |
Green intervention projects: reclaming vacant land, added recreational areas, paths and tracks, improved drainage, landscaping, maintenance | Construction and installation of new amenities | Health status | Semantic differential scale | (o) |
| Dobbinson, 2020 (n = 1670) [55] |
Refurbishments to existing amenities: playground eqiupment, quality walking paths, shade and shade-sail | Construction and installation of new amenities | Positive and negative affect | PANAS scale | (o) |
| Wood, 2017 (n = 492) [84] |
Park functions | POSDAT | Mental wellbeing | WEMWBS | (+) |
| McCarthy, 2017 (n = 13,469) [31] |
Playground quality: useability, cleanliness and maintenance, distinct areas for different age groups, colourful eqiupment, shade cover, benches, fence, separation from roads | Environmental audits | BMI | Clinically measured | (o) |
| Rundle, 2013 (n = 13,102) [75] |
Number of recreational facilities | Environmental audits | BMI | Clinically measured | (o) |
| Bojorquez, 2018 (n = 2345) [40] b |
Park quality score: bathrooms, lighting, playground, etc. (9 items in total) | Environmental audits | Depressive symptoms | CES-D | (o) |
| Camargo, 2017 (n = 1392) [51] |
Walking paths conditions | Semantic differential scale | Quality of life | EUROHIS-QOL | (+) |
| Zhang, 2019 (n = 909) [24] |
Amenities: children’s play equipment, seating facilities, dog litter bags, water sources for dogs, drinking fountains, parking facilities, public transport, variety of permitted activities | POST | Quality of life | WHOQOL-BREF | (o) |
| Bai, 2013 (n = 893) [50] |
Availability of facilities of interest | Semantic differential scale | BMI | Self-measured height and weight | (o) |
| Pope, 2018 (n = 578) [43] |
Maintenance | Dichotomous survey question | Psychological distress | GHQ-12 | (o) |
| Tan, 2019 (n = 326) [23] |
Number of facilities and seats | Environmental audits | Physical functioning, physical role, bodily pain and emotional role | SF-12v2 | (o) |
| Sugiyama, 2009 (n = 271) [25] |
Quality of access paths | Semantic differential scale | Health statusQuality of life | No. of days with poor physical/mental healthSWLS | (o) |
| Mears, 2020 (n = 345 LSOAs) [32] |
Play facilities: playgrounds, games area, skate or bike parks | Environmental audits | BMI | Clinically measured | (o) |
| Ngom, 2016 (n = N/A) [71] |
Green space functions | GIS databases | Coronary heart disease, cerebrovascular disease, heart failure, diabetes, hypertension | Medical records | (+) |
| Size (n = 11) | |||||
| Wood, 2017 (n = 492) [84] |
Park size | GIS analysis (1.6 km buffer) | Mental wellbeing | WEMWBS | (+) |
| Stark, 2014 (n = 44,282) [78] |
Park size | GIS analysis (805 m buffer) | BMI | Self-measured height and weight | (+) |
| Rundle, 2013 (n = 13,102) [75] |
Park size | GIS analysis (805 m buffer) | BMI | Clinically measured | (+) |
| Zhang, 2019 (n = 909) [24] |
Park area | GIS analysis (400 m and 800 m buffers) | Quality of life | WHOQOL-BREF | (o) |
| Tan, 2019 (n = 326) [23] |
Area | Environmental audits | Physical functioning, physical role, bodily pain and emotional role | SF-12v2 | (+) |
| Kim, 2016 (n = 92) [30] |
Size of tree canopy | GIS analysis (805 m buffer) | Quality of life | PedsQL | (+) |
| Kim, 2014 (n = 61) [29] |
Size of tree canopy | GIS analysis (805 m buffer) | BMI | Clinically measured | (+) |
| Dennis, 2020 (n = 1673 LSOAs) [53] |
Mean patch size | GIS databases | Chronic morbidity prevalence | CIDR | (+) |
| Wang, 2019 (n = 369 census tracts) [82] |
Patch area | GIS analysis (805 m buffer) | All-cause, cardiovascular, chronic respiratory and neoplasm mortality | Medical records | (+) |
| Mears, 2020 (n = 345 LSOAs) [32] |
Garden size | GIS analysis (300 m buffer) | Obesity rate | Clinically measured BMI | (o) |
| Mears, 2020 (n = 345 LSOAs) [42] |
Garden size | GIS analysis (300 m buffer) | Poor general health Depression and severe mental illnesses |
Semantic differential scale Medical records |
(+) |
| Shape, pattern & connectivity (n = 8) | |||||
| Kim, 2016 (n = 92) [30] |
Pattern of green space patches: fragmentation, shape irregularity, isolation from other patches | GIS analysis (805 m buffer) | Quality of life | PedsQL | (+) |
| Kim, 2014 (n = 61) [29] |
Connectedness | GIS analysis (805 m buffer) | BMI | Clinically measured | (+) |
| Kim, 2021 (n = 2301 census tracts) [67] |
Size & dispersion of tree canopy patches | GIS analysis | Asthma emergency visits | Medical records | (+) |
| Sander, 2017 (n = 546 census blocks) [76] |
Contiguity | GIS analysis | BMI | Self-measured height and weight | (+)(–) |
| Wang, 2019 (n = 369 census tracts) [82] |
Pattern of green space patches: fragmentation, connectedness, aggregation, shape irregularity | GIS analysis (805 m buffer) | All-cause, cardiovascular, chronic respiratory and neoplasm mortality | Medical records | (+) |
| Tsai, 2016 (n = 52 MSAs) [80] |
Pattern of green space patches: aggregation, contrast between patch types | GIS analysis | BMI | Self-reported height and weight | (+)(–) |
| Jaafari, 2020 (n = 87 hexagons) [63] |
Pattern of green space patches: patch density, connectedness, shape irregularity | GIS analysis | Mortality of respiratory cancer diseases and respiratory diseases | Medical records | (+) |
| Shen, 2017 (n = 48 districts) [77] |
Pattern of green space patches: fragmentation, aggregation, between-patch distances | GIS analysis | Respiratory mortality | Medical records | (+) |
| Safety (n = 6) | |||||
| Orstad, 2020 (n = 3652) [72] |
Perceived park crime | Dichotomous survey question | Mental health | Number of days with stress, depression, and emotion problems | (+) |
| Camargo, 2017 (n = 1392) [51] |
Perceived safety of the way home | Semantic differential scale | Quality of life | EUROHIS-QOL 8-items | (+) |
| Bai, 2013 (n = 893) [50] |
Safety | Semantic differential scale | BMI | Self-reported | (o) |
| Pope, 2018 (n = 578) [43] |
Safety | Dichotomous survey question | Psychological distress | GHQ-12 | (o) |
| Tan, 2019 (n = 326) [23] |
Perceived safety: reduced visibility, prospect of crime, presence of security guards, fear of falling, unwell feelings | Survey questionnaire (details unspecified) | Physical functioning, physical role, bodily pain and emotional role | SF-12v2 | (+) |
| Sugiyama, 2009 (n = 271) [25] |
Safety: night-time safety, safety along surrounding paths, lack of crime | Semantic differential scale | Health status Quality of life |
No. of days with poor physical/mental health SWLS |
(+) |
| Cleanliness and absence of incivilities (n = 5) | |||||
| Stark, 2014 (n = 44,282) [78] |
Cleanliness score | Parks Inspection Program audit tool | BMI | Self-measured height and weight | (+) |
| Rundle, 2013 (n = 13,102) [75] |
Weeds, litter, glass, graffiti score and overall cleanliness score | Parks Inspection Program audit tool | BMI | Clinically measured | (o) |
| Zhang, 2019 (n = 909) [24] |
Aesthetics: watered grass, no graffiti, no vandalism | POST | Quality of life | WHOQOL-BREF | (o) |
| Bai, 2013 (n = 893) [50] |
Cleanliness | Semantic differential scale | BMI | Self-measured height and weight | (o) |
| Mears, 2020 (n = 345 LSOAs) [42] |
Cleanliness | Environmental audits | Depression | Medical records | (+) |
| Peacefulness (n = 3) | |||||
| Herranz-Pascual, 2019 (n = 137) [61] |
Soundscape characteristics | Semantic differential scale | Depression | Semantic differential scale | (+) |
| Sugiyama, 2009 (n = 271) [25] |
NuisanceL dogs and dog foulings, presence of young people | Semantic differential scale | Health status Quality of life |
No. of days with poor physical/mental health SWLS |
(o) |
| Pazhouhanfar, 2018 (n = 250) [73] |
Private environment | Semantic differential scale | Mood ratings (relaxed/happy/excited/calmed) | Semantic differential scale | (o) |
| Perceived quality/Satisfaction with quality (n = 7) | |||||
| Putra, 2020 (n = 4969) [36] |
Perceived quality by parents | Semantic differential scale | Prosocial behaviour | SDQ | (+) |
| Feng, 2018 (n = 3897) [39] |
Perceived quality | Dichotomous survey question | Psychological distress; serious mental illnesses | K6-PDS | (+) |
| Feng, 2019 (n = 3843) [38] |
Perceived quality | Semantic differential scale | BMI | Self-measured height and weight | (+) |
| McEachan, 2018 (n = 805) [37] |
Satisfaction with green space by parents | Semantic differential scale | Total difficulties, internalising difficulties, externalising difficulties and prosocial behaviours | SDQ | (+) |
| Bai, 2013 (n = 893) [50] |
Attractiveness | Semantic differential scale | BMI | Self-measured height and weight | (o) |
| Pazhouhanfar, 2018 (n = 250) [73] |
Attractiveness | Semantic differential scale | Mood ratings (relaxed/happy/excited/calmed) | Semantic differential scale | (o) |
| Jonker, 2014 (n = N/A) |
Satisfaction with quality | Semantic differential scale | Life expectancy and healthy life expectancy | National life table data | (+) |
| Combination of features (n = 13) | |||||
| Zhang, 2017 (n = 223) [87] |
Perceived quality: recreational facilities, amenities, natural features, absent of civilities, accessibility, maintenance | Semantic differential scale | Neighbourhood satisfaction | Semantic differential scale | (+) |
| Francis, 2012 (n = 911) [60] |
Objective quality score: walking paths, shade, water features, irrigated lawn, birdlife, lighting, sporting facilities, playgrounds, type of surrounding roads, presence of nearby water Subjective quality score: atmosphere, comfort, safety, attractiveness and maintenance, variety of things to do, presence of adequate seating, public art, other people |
POST (objective)Semantic differential scale (subjective) | Psychological distress | K6-PDS | (+) |
| Bird, 2016 (n = 380) [27] |
Park typology: team sports features, pool-oriented features, perceived safety, cycling-oriented features, play area features, walking-oriented, aesthetically pleasing, incivilities, infrequent park installations, schoolyard features | Author-developed typology, with principal component analysis | % truncal fat | X-ray absorptiometry | (+) |
| Kruize, 2020 (n = 3947) [68] |
Objective quality score: general characteristics, facilities, traffic safety, infrastructure, sidewalk amenities, incivilities Satisfaction with green space: quality, amount, maintenance, safety |
Environmental audits/Semantic differential scale | Mental wellbeing | MHI-5 | (+) |
| Vries, 2013 (n = 1641) [81] |
Composite score: variation, maintenance, orderly arrangement, absence of litter, general impression | Semantic differential scale | Perceived general health; health complaints and mental health | SF-36; acute health-related complaint checklist; MHI-5 | (+) |
| Dillen, 2012 (n = 1553) [54] |
Green area quality: accessibility, maintenance, variation, naturalness, colourfulness, clear arrangement, shelter, absence of litter, safety, general impression | Environmental audits | Perceived general health; health complaints and mental health | SF-36; acute health-related complaint checklist; MHI-5 | (+) |
| Carter, 2014 (n = 440) [52] |
Useability: in good conditions, well-equipped, including spaces to relax and socialise | Semantic differential scale | General health and vitality | SF-36v2 | (+) |
| Dzhambov, 2018 (n = 399) [58] |
Perceived quality: safety, maintenance, aesthetic, suitability for sport and social interactions, biodiversity | Semantic differential scale | Mental health | GHQ-12 | (+) |
| Tan, 2019 (n = 326) [23] |
Aesthetics: colour, shape, diversity and seasonal variation of plants, maintenance, proportions of soft surfaces | Survey questionnaire (details unspecified) | Physical functioning | SF-12v2 | (o) |
| Sugiyama, 2009 (n = 271) [25] |
Pleasantness: adequacy for children to play, adequacy for adults to chat, variety of activities to engage in, quality of trees and plants, facilities (toilet, shelter) | Semantic differential scale | Health status Quality of life |
No. of days with poor physical/mental health SWLS |
(+) |
| Zhang, 2019 (n = 250) [88] |
Visual sensation: Variety of plants, richness of plants’ colour, plant light and shadow mottle, nice road texture, rich terrain, wide view, ornamental water Auditory sensation: natural sound, sweet background music, happy people sounds (singing or playing instruments), quiet background, no traffic noise Tactile sensation: road material is comfortable and the foot feels good, strong hydrophilic, seat is comfortable for sitting, comfortable grass for flat lay |
Semantic differential scale | Restorative effect | Semantic differential scale | (+) |
| Mears, 2020 (n = 345 LSOAs) [32] |
Quality * Size ≥2 ha * Predominantly natural feeling * Good or better quality ratings from council assessment, based on: signage; provision of facilities; maintenance of paths; safety; planting and plant management; and cleanliness |
Environmental audits | BMI | Clinically measured | (+) |
| Mears, 2020 (n = 345 LSOAs) [42] |
Quality * Size ≥2 ha * Predominantly natural feeling * Good or better quality ratings from council assessment, based on: signage; provision of facilities; maintenance of paths; safety; planting and plant management; and cleanliness |
Environmental audits | Poor general health | Semantic differential scale | (o) |
Notes: Within each quality domain, studies were arranged by study design, and then by sample size. A full version of this table is available as Supplementary File S4. * Abbreviation: DA: dissemination areas; LSOA: lower layer Super output areas; MSA: metropolitan statistical areas ** AMI: acute myocardial infarction; ART: attention restoration theory; BMI: body mass index; CES-D: Center for Epidemiologic Studies-Depression; CIDR: comparative illness and disability ratio; CVD: cardiovascular diseases; EUROHIS-QOL-8: EUROHIS 8-item quality of life questionnaire; GDS: geriatric depression scale; GHQ-12: 12-item general health questionnaire; GIS: Geographic Information System; K10-PDS: Kessler ten-item psychological distress scale; K6-PDS: Kessler six-item psychological distress scale; MHI-5: 5-item mental health inventory; PANAS: positive and negative affect schedule; PedsQL: paediatric quality of life inventory; POST: Public Open Space Tool; POSDAT: Public Open Space Desktop Auditing Tool; PRS: perceived restorativeness scale; PSS: perceived stress scale; SDI: Shannon’s diversity index; SDQ: strengths and difficulties questionnaire; SF-8: eight-item short form survey; SF-12: 12-item short form survey; SF-12v2: short form 12 item (version 2); SF-36: 36-item short form survey; SF-36v2: short form 36 item (version 2); SWLS: satisfaction with life scale; WEMWBS: Warwick Edinburgh mental well-being scale; WHOQOL-BREF: World Health Organization quality-of-life scale. ≠ (+) Some evidence of protective associations; (–) some evidence of risk associations; (o) no significant associations observed.