Professional summary
David is interested in quantifying and mapping the provision and benefits of Ecosystem Services (ES), particularly those associated with green and blue spaces in an urban setting. A critical part of achieving this is developing our understanding of the interplay between pressures (e.g. noise, air pollution, Urban Heat Island effect) and contextual factors (e.g. land use / land cover (LULC), demographics, poverty and health indicators) in determining who benefits, where, when and by how much. An improved understanding of these relationships will allow us to support sustainable and equitable development through accurate ES assessment and scenario modelling.
David uses a variety of tools to obtain, process and analyse a range of data-types, from remotely sensed multispectral satellite images, to census and socio-economic indicator data. He enjoys developing new methods and modelling techniques, as well as Shiny apps, in R statistical programming language, but also uses other tools and platforms, such as Google Earth Engine and Python in his workflows.
Web tools and apps
Dunescapes data visualisation platform
Pollution Removal by Vegetation (in progress of creating a new version)
Dunescapes data visualisation platform
Pollution Removal by Vegetation (in progress of creating a new version)