Greenspaces provide incredible valuable benefits to people. For example, there are over 62,000 urban greenspaces in Great Britain, estimated to provide just over £130 billion to those living nearby. These benefits can be broken down to a variety of services: e.g. food production (£114 million per year), carbon sequestration (£33 million), air filtration (£211 million), cooling (£166 million), noise mitigation (£14 million), improved physical health (£4.4 billion) and other cultural services (£2.1 billion). However, in order to receive these benefits, people need to be able to access these greenspaces within the landscape. On average, in Great Britain there are 1.4 access points per hectare of functional greenspace, with the average urban property having 4.6 hectares of green space within a 200-meter radius. One may think that distance to greenspace is the main contributor to its usage. However, the evidence for this is unclear. In fact, how people access nature at a landscape scale is currently not known. For example, Schipperijn et al. (2010) found that for Denmark distance is not a limiting factor which suggest that other variables are at play such as length of the routine, mode of transportation used daily, working hours, etc. Here, we addresses this knowledge gap, combining theories from human mobility and behavioural ecology to understand the “movement ecology of people”. We will use smartphone data to quantify the equitability of greenspace access in the UK, and investigate how this changes during shocks (e.g. covid-19). We will also investigate the optimal configuration of greenspaces within a landscape: Is a single large green space used more than several small spaces? Or do corridors of green space (which are known to be beneficial for biodiversity) overlap with people’s routines more?
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as Environmental Science, Geography, Forestry, Biology, Botany, Natural Sciences, Data Science, or related disciplines. Experience of working with big data, spatial modelling (e.g. GIS) and writing computer code (e.g. R statistics) would be advantageous.
For further details please contact Prof Simon Willcock
To apply for this project follow this link