King’s Geocomputation members Yijing Li and Chen Zhong – along with colleague Qunshan Zhao – are guest editing a special issue of the journal, Annals of GIS. The call for papers on research in GIS and urban data science is now open. Read on for more details.
This funded PhD project seeks to employ data science and text mining methods to enhance our understanding of how a ‘gendering’ of the research pipeline might offer insight into the challenges faced by women as they make the transition from students to independent researchers. We are looking for a passionate, curious, and careful candidate with data science and programming skills, and an interest in NLP and the ethics of data science/AI to work on an exciting collaborative CASE Studentship involving the British Library and supervisors at King’s College London and the Alan Turing Institute/University of Warwick!
Image source: Marta Manso / Wikipedia
Despite decades of research on neighborhood change, there has been little corresponding methodological development: studies still tend to either rely primarily on demographic data aggregated at the neighborhood level (which masks complex and micro-scale causal dynamics), or on in-depth case studies (which present challenges for generalization). Advances in data science, particularly if informed by critical urban theory, offer the potential to remedy some of these methodological shortcomings. To the extent that these and other approaches support an early warning system designed to be readily understood by stakeholders, they have the ability to empower communities, at a minimum, and potentially to transform policy as well.
We are inviting applications for a fully funded PhD in ‘Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling‘. See full details below. ——————- Job posting – Full-funded PhD position (LISS-DTP 1+3/3+) Overview of the positionWe are looking for […]
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.
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.
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?
I’m really excited to announce the latest addition to our department’s growing stable of computational geography research: a fully-funded 1+3 ESRC CASE studentship involving 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 really pleased to announce that on Wednesday, 22 February Professor Sergio Rey, of the School of Geographical Sciences and Urban Planning at Arizona State University will be discussing the Python Spatial Analysis Library (PySAL). […]