PhD Studentship - Spatio-temporal Human Exposure Mapping Using Mixed Methods

Overview

Description

Early warning systems for natural hazards are shifting their paradigm towards impact-based forecasting. Within such a framework, capturing exposure “on-the-fly” is a crucial step towards accurate prediction of the potential social impacts. Human exposure datasets provide invaluable information about the spatial concentration of populations.

As such, these maps form an essential cornerstone for prediction of social impact of natural hazards. Gridded population maps are open datasets providing global coverage of population density. However, they are often based on census data reporting the location of the population at domicile. In this sense, they can be viewed as “night-time” exposure maps.

This calls for high-resolution temporal downscaling of human exposure datasets across different times of the day, week, and seasons.

This PhD project aims to use participatory mixed methods (e.g. crowd-sourced geospatial data science, interviews, focus groups, surveys) for data collection about local mobility patterns in a community. These mobility patterns will then be analysed and processed through advanced and conventional data science methods to develop temporally down-scaled digital gridded maps for human exposure.

Applicable programmes

Benefits

The funding amount is £21,237 per annum for 3 years, for students eligible for UK rates.