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

Spatio-temporal Human Exposure Mapping Using Mixed Methods

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.The student will be supported in data collection using participatory methods by leveraging PCTWIN “People-Centred Tsunami Early Warning along the Indian Coastlines”, a 4-year grant funded by NERC UKRI and the Indian Ministry of Earth Sciences (MoES). PCTWIN’s activities focus on two communities on the eastern and western Indian Coasts.

The resulting exposure maps will be used to improve the operational capacities of the Indian Tsunami Early Warning Centre (ITEWC) and will provide a blueprint for mapping local mobility patterns based on participatory mixed methods. They ultimately contribute to the move towards impact-based forecasting of natural hazards.

The student will be supervised by Prof Fatemeh Jalayer (Geophysical Hazard Risks) with secondary supervisors Dr Saman Ghaffarian (Geospatial Data Science) and Dr Sarah Dryhurst (Risk Communication and Perception).

Download the PhD specification

Duties and responsibilities of RDR PhD Students

In addition to carrying out the research for their PhD thesis, RDR PhD students should aim to publish research in peer-reviewed journals. 
In addition, they need to contribute to the intellectual life of the RDR and actively engage with the RDR Doctoral Research Centre and UCL as communities of scholars. PhD students are encouraged to contribute to teaching and public engagement events. Full-time PhD students with RDR as their primary location should centre their research in the RDR Doctoral Research Centre, and be based in an RDR Research Room.

RDR PhD students are expected to regularly attend RDR seminar series with internal and external speakers and to engage in the informal life of RDR by joining events including:

• The annual RDR Student Forum in January to present their research.
• The annual RDR Spring Academy typically on the first Monday and Tuesday of the third term.
• The one-day RDR Annual Conference and the Humanitarian Summit in June, presenting a poster at the RDR Annual Conference.
• RDR public events during first and second terms.
• RDR students have access to a wide range of training opportunities. 

How to apply

Informal enquiries, if you have something to ask, may be addressed to Prof Fatemeh Jalayer (f.jalayer@ucl.ac.uk)

Candidates should apply for the Research Degree: Risk and Disaster Reduction (RRDRDRSING01) by completing the online form here. For the first line, state explicitly “PhD in Risk and Disaster Reduction – Human Exposure Modelling (supervisor Fatemeh Jalayer)”. Please include a one-page cover letter outlining your suitability for the role and why you want the studentship. Do also ensure you fill in your qualifications and referee details. Please make sure you send the filled application form also to RDR-pgr@ucl.ac.uk

In Section 2 of the application, please list the department (15) as “Department of Risk and Disaster Reduction” and the Research subject area (17) as “Risk and Disaster Reduction”. In addition to submitting the full application according to instructions on the form, please submit section 1 to 5 and your cover letter to RDR-pgr@ucl.ac.uk under subject line “PhD studentship application – Human Exposure Modelling”.

Key requirements

The principal requirements for admission to the MPhil/PhDs in the RDR are a 1st class or high upper 2nd class bachelor’s degree or a master’s degree with merit or distinction in relevant disciplines. Excellent English oral and written communication skills are required. Candidates should have excellent knowledge and experience of computational social science research methods, and an enthusiasm for fieldwork. Experience with Python and geospatial data science would be an advantage. Candidates should be able to travel nationally and internationally for research.

Important details

• Funding amount: £21,237 per annum for 3 years, for students eligible for UK rates
• Hours: Full time
• Closes: 15th December 2024
• Start: 1st February 2025 (latest possible start date before May 1st 2025)
• Supervisor: Prof Fatemeh Jalayer
• Second supervisor(s): Dr Saman Ghaffarian and Dr Sarah Dryhurst

Apply now

Download the PhD specification

Image: The Great Tohoku Earthquake and Tsunami Damage Misawa, Hachinohe, and Noda, by GT Bird Photography