This PhD studentship, offered full time over three years, is part of the University’s contribution to the ESRC funded 'Civil Society – Civic Stratification and Civil Repair', awarded to the Wales Institute of Social and Economic Research, Data and Methods Research Centre, and is tied in with the work package that looks at inequalities, civic loss and well-being.
It will develop innovative approaches based around the implementation of network-based web-modelling tools that draw on open data sources to examine spatial and temporal trends in spatial accessibility. There are well-recognised limitations in using so-called ‘static’ approaches to examine patterns of accessibility for more detailed geographies.
One key issue relates to the need to factor in temporal changes in the demand for services, in changing network flow capacities (due to traffic patterns and public transport timetables), and in the opening times and temporal capacities of the services themselves.
As new data sources such as social media activity and on-line bus/train timetables become available through web server interoperability tools, the opportunity arises to refine existing approaches to incorporate this information to provide more accurate representations of spatial service availability.
Building upon research already conducted at this research centre, this project will review approaches to understanding temporal variations in supply-demand relationships, and will develop methodologies to allow their incorporation into so-called ‘floating catchment area’ (FCA) accessibility metrics. It will also engage with the latest web-mapping APIs and geospatial web server technologies to develop browser-based tools for computing and visualising temporal FCA scores.
An upper second-class degree, a Master’s degree, or relevant equivalent industrial experience, is required. We particularly encourage applications from candidates that hold a degree at Graduate or Masters level in Computer Science, Geographical Information Science, Data Science, Informatics, or Geography with significant quantitative elements.