The Department of Geography’s PhD small grant for fieldwork in Thailand

By Felicia H M Liu

The Department of Geography’s PhD small grants supported two trips at the early scoping stages of my fieldwork to Bangkok, Thailand.

I attended the 18th Meeting of the UNFCCC Standing Committee on Finance (SCF) from 10-12 September, taking place right after a preparatory meeting for COP24 taking place later in the year in Katowice. I represented The Research and Independent Non-Governmental Organisations (RINGO) and attended as an observer. The build up to the meeting was exciting as I was reading about the progress and sticking points in the meeting from 4-9 September in Bangkok as I prepared for the SCF meeting. At the same time, as I have not been following in great detail the UNFCCC process, I also realised the learning curve to all the processes and technicalities would be steep. As I arrived at Bangkok, I was both excited and nervous.

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Academic Carbon Footprints

by Mary Langsdale

As part of our annual criteria, each member of the Sustainability Champions for the Department of Geography was asked by King’s Sustainability team to calculate our carbon footprint. I’m someone who cycles daily, doesn’t own a car, tries to eat a meat-free diet and lives in a shared house with electricity provided by a renewable energy provider. I have to admit, I was expecting my carbon footprint to be fairly small (and even to feel a bit smug). However, when I calculated it using the WWF carbon footprint calculator, I was shocked to discover that I was using 216% of my share (with 100% the average for each UK citizen to meet the UK’s 2020 emissions reduction targets). Of my share, 86% was from travel contributions, as shown in Figure 1. Continue reading

Understanding Gentrification through ML

Although it has taken rather a long time to see the light of day, our just-published paper is one of the reasons I love my job: drawing on a mix of data science and deep geographical knowledge, we look at the role that new Machine Learning (ML) techniques – normally seen as just a ‘black box’ for making predictions – can play in helping us to develop a deeper understanding of gentrification and neighbourhood change. For those of a ‘TL;DR’ nature (or without the privilege of an institutional subscription!), we wanted to share some of our key ideas in a more accessible format. Continue reading

Geography & Computers: Past, present, and future

I’m really pleased to share a piece that Dani Arribas-Bel and I recently co-authored in Geography Compass on the sometimes fraught relationship between (human) geography and computers, and advocating for the creation of a Geographic Data Science. For those of a ‘TL; DR’ nature (or without the privilege of an institutional subscription!), we wanted to share some of our key ideas in a more accessible format.

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MoDS: Mapping Knowledge with Data Science (MSc + PhD Studentship)

Although we had some great responses to our initial call, we’re still looking for the ‘right’ candidate for this fully-funded studentship that is open to both undergraduate finalists as well as completing Masters students. The project involves the application of data science techniques (text-mining, topic modelling, graph analysis) to a large, rich data set of 450,000+ PhD theses in order to understand the evolving geography of academic knowledge production: how are groundbreaking ideas produced and circulated, and how does researcher mobility and institutional capacity shape this process?

We’re looking for a great candidate (see ‘pathways’ below) with a demonstrable interest in interdisciplinary research – you will be working in collaboration with the British Library at the intersection between geography, computer science, and the humanities, and this will present unique challenges (and opportunities!) that call for resourcefulness, curiosity, and intellectual excellence. Continue reading

MoDS: Mapping Knowledge with Data Science

I’m really excited to announce the latest addition to our growing stable of computational geography research: a fully-funded ESRC studentship involving the application of cutting-edge techniques (text-mining, topic modelling, graph analysis) to a large, rich data set of 450,000 PhD theses in order to understanding the evolving geography of academic knowledge production: how are groundbreaking ideas produced, circulated, and ultimately succeeded, and how do issues such as researcher mobility and institutional capacity shape this process?

We’re looking for a stellar candidate (either undergraduate or Masters-level) with a demonstrable interest in interdisciplinary research – you will be working at the intersection between disciplines and this will present unique challenges (and opportunities!) that call for resourcefulness, curiosity, and intellectual excellence.

Project Overview

The British Library manages EThOS, the national database of UK doctoral theses, which enables users to discover and access theses for use in their own research. But the almost complete aggregation of metadata about more than 450,000 dissertations also enables us to begin asking very interesting questions about the nature and production of knowledge in an institutional and geographic context across nearly the entire U.K., and this anchors the project in quintessentially social science questions about the impact of individuals, work, and mobility on organisations and cultures.

However, textual data of this scale is solely interpretable and navigable through ‘distant reading’ approaches; so although it remains rooted in the interests and episteme of the social sciences, the research involves genuinely interdisciplinary work at the interfaces with both the natural sciences and the (digital) humanities! At its heart, this project is therefore an exciting example of ‘computational social science’ (Lazer et al. 2009) in that it involves the application of cutting-edge computational techniques to large, rich data sets of human behaviour.

Ultimately, this project seeks to understand changes in the U.K. geography of academic knowledge production over time and across two or more disciplines. All applicants are therefore expected to demonstrate an interest in the underlying social science research questions and (at a minimum) basic competence in programming. Additionally, the successful applicant for the 1+3 route would be expected to successfully complete King’s MSc Data Science programme, while the successful +3 applicant would be expected to demonstrate a degree of existing facility with core analytical approaches.

For more information on the project, please see here.

Studentship type

1+3 (1 year Masters + 3 year PhD) or +3 (PhD only), subject to candidate’s existing academic/professional background. For applicants with a social science background we are suggesting King’s MSc Data Science programme. For applicants with a natural science background we will need to discuss how best to achieve a grounding in the social sciences.

Application deadline

31 January 2018