by Zaky Fouad
On January 13th 2023, I asked chatGPT what are greenspaces? It answered:
“Greenspaces are areas of natural or semi-natural land, such as parks, gardens, and other open spaces, that provide a range of benefits for people and the environment. These benefits can include improved physical and mental health, increased biodiversity, and reduced air and noise pollution”.
I repeated the question five months later (July-3rd):
“Greenspaces, also known as green spaces or green areas, refer to open, undeveloped land or areas that are predominantly covered with vegetation, such as parks, gardens, forests, meadows, and other natural or landscaped areas. Greenspaces are often characterized by their abundance of plant life, including trees, shrubs, grass, and flowers. Greenspaces serve several important functions within urban and rural environments. They provide recreational spaces for activities like walking, jogging, picnicking, and playing sports. These areas also contribute to the overall well-being and quality of life for residents by offering opportunities for relaxation, social interaction, and connection with nature.”
Like human minds (or at least mine), AI does not define greenspaces without referring to uses and health benefits. Nevertheless, this intuitive view (of the positive impact) does not match the inconsistencies in research findings which either confirms, rejects, or shows no positive association. Hereby, we become sceptical and ask a wicked question: Did previous studies accurately quantify the society’s exposure to greenspace, before testing the association to health outcomes?
For that question, we embark on a journey to explore different measures of greenspace exposure and their association with health-related outcomes. We developed two datasets. Firstly, the health-related outcomes comprise frequency of visits to greenspace, level of physical activity using GPAQ, connectedness to nature score, and wellbeing (ICECAP-A score), self-reported through an online survey for residents of the West of England (figure 1) during the 2020 lockdown. The second dataset encompasses the measures of greenspace exposure, calculated using GIS-software courtesy of one key survey data-entry: the residents’ postcodes.
Figure 1: Area of study; Source: Generated by the author using Office for National Statistics (ONS). Open Geography Portal [GeoPackage], Open Government Licence v.3.0. 2022. Available online here; accessed on 13 March 2023
Regarding greenspace measures, let’s imagine you want to know the amount of greenery around your house. So, you buy a drone, fly it high, take an image and calculate percentage of greenery in it. This is similar to how we evaluate the degree of greenness, using satellite images to calculate Normalised-Difference-Vegetation-Index (NDVI), although it is more complicated since NDVI does not capture greenness but the difference between red (absorbed by greenery) and infra-red (reflected by greenery). In your experiment, flying the drone higher (or lower) leads to a wider (or smaller) image catchment area. In our analyses, we do not fly satellites higher or lower; instead, we define how far we look, i.e., a catchment radius for NDVI terms (r200m, r300m, r500m-walking-route, r1000m and r2000m) from the postcodes (figure 2). Those radii are suggested by Natural England’s Accessible Greenspace Standards (AGS) to represent greenspaces closeness to homes: doorstep-greenspace=200m, local-greenspace=300m, neighbourhood-greenspace=1000m and wider-neighbourhood-greenspace=2000m.
Figure 2: Catchments Radius-300m from postcodes, each gets NDVI-value from background map. Source: Generated by the author using Copernicus Sentinel 2 Colour Infrared (Bands 843) [TIFF Geospatial Data], Scale 1:20,000, Tiles: Sr,ss,st,su,sv,sw,sx,sy,sz. Updated: 11 February 2020, to Be Added, Using: EDINA Pilot Digimap Service. Available online here; accessed on 24 February 2023.
Let’s say you would rather define your greenspace exposure as how close you live to greenspaces. For that, you get a map, draw a straight-line from home to the nearest greenspace entrance and measure it. This is the ‘Euclidean distance to the nearest greenspace’. You could improve your measurement by drawing another line from home, along the streets you walk reaching the greenspace, because your real-life journey does not cut through buildings but follows the streets. This is the ‘Network distance to the nearest greenspace’. In both calculations, your measurements are towards the greenspace entrance, not its centre nor boundary, which accurately represents your distance to access greenspaces while excluding the journey travelled inside the greenspace that is relative to the greenspace size (figure 3). You could expand your dataset to calculate distances to specific greenspace sizes, suggested by Natural England’s AGS: distance to the nearest 0.5ha doorstep-greenspace, 2ha local-greenspace, 10ha neighbourhood-greenspace or 20ha wider-neighbourhood-greenspace.
Figure 3: How Euclidean vs Network distances are calculated. Source: Created by the author.
Findings on the exposure measures:
- NDVI increases as catchment increases; greenery increases when capturing wider areas.
- Average network distance to greenspaces (333m) > average Euclidean distance (190m); walking along streets > the virtual straight-line.
- Average distances to greenspaces > AGS standards for doorstep-greenspaces (275m>200m) and local-greenspaces (374m>300m).
Findings on participants health explain the impact of lockdown on behaviours and wellbeing:
- 38% increase in visits to greenspaces during lockdown than before.
- 9% increase in physical activity during lockdown.
- 16% decrease in wellbeing (ICECAP-A score) during lockdown.
Findings on the relationship between greenspace exposure measures vs health outcomes:
- Residents are more aware of the greenery within their immediate context, which impacts their connectedness to nature.
- Residents visited greenspaces more frequently and did more physical activity when living within a greener context or closer to greenspaces.
- Health-related outcomes relate to exposure measures of the immediate context (NDVI 200m and 300m) and to proximity to the nearest or small greenspaces (0.5ha); but not to the wider context (NDVI 1000m and 2000m) nor proximity to larger greenspaces (10ha/20ha).
In the end, I see different measures of greenspace exposure as different types of shoes. You could try to wear heels to cycle, derbies to ski, or sandals to a muddy hike. You might avoid injuries, but surely you would experience a bad day. All shoes cover feet (fully or partially) but serve different purposes. Similarly, all measures quantify greenspace exposure but capture unique perceptions of the relationship between society and greenspaces at different scales.
This blog is based on the paper ‘Measures of Greenspace Exposure and Their Association to Health-Related Outcomes for the Periods before and during the 2020 Lockdown: A Cross-Sectional Study in the West of England‘ by Danielle Sinnett, Isabelle Bray, Rachael McClatchey, Rebecca Reece and myself.