Start Date: October 2017
There is increasingly cross-country-time-series data available on various economic, social, demographic, environmental, political and administrative indicators, provided by the UN (http://data.un.org/), World Bank API (https://goo.gl/UpL3t6), Human Rights Data project (http://www.humanrightsdata.com/), V-Dem project (https://www.v-dem.net/en/data/data-version-6-2/) etc. This rich data can be further supplemented by extracting information from online news and social media (e.g. GDELT project, http://www.gdeltproject.org/), creating further and often more fine-grained indicators measuring the state of various societies across the world on the national or regional level. This data provides new opportunities to understand interactive dynamics of political, social and economic change using mathematical and computational modelling techniques (Spaiser et al. 2014, Ranganathan et al. 2015, Spaiser et al. 2016). The data can be used to test conflicting theories e.g. on sustainable development (Spaiser et al. 2016) and democratisation (Spaiser & Sumpter 2016) but it can be also used in more explorative ways to inspire new theoretical thinking (Ranganathan et al. 2014a). The PhD student on this project will be expected to create and constantly update a comprehensive cross-country, time-series dataset from the various sources such as World Bank, UN etc. They will also implement an automatized query to scrape and process data from online news sites and social media such as Twitter for instance, extracting information on critical events (e.g. environmental incidents, protest & civil unrest, disease outbreaks, etc.) and adding/linking them to the cross-country, time-series data. Finally, they will work to develop further the bdynsys R package on Bayesian Dynamical Model (Ranganthan et al. 2014b), building on the recent work by Dr. Richard Mann and Dr. Viktoria Spaiser, in collaboration with researchers at the Uppsala University in Sweden (Prof. David J.T. Sumpter & Björn Blomqvist) and Virginia Tech in USA (Dr. Shyam Ranganathan). The goal is to develop appropriate computational and mathematical tools to analyse the collected data and generate insights that will enhance our understanding of complex international and national processes and dynamics and that may be potentially relevant to policy making (e.g. monitoring & supporting the implementation of the UN Sustainable Development Goals, http://www.undatarevolution.org/). Envisioned is a collaboration with the UK Department for International Development.
Reference number LE06
Deadline for applications – 3rd July 2017