This week we had our first ever Python Coding Dojo! Around 25 current and former undergraduate students, PhDs and staff got together for evening of coding, pizza and fun.
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.
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). […]
This week we started advertising a post-doctoral Research Associate position to work with James on a project looking at the global food system, local land use change and how they’re connected. The successful candidate will drive the development and application of an integrated computer simulation model that represents land use decision-making agents and food commodity trade flows as part of the Belmont Forum (NERC) funded project, ‘Food Security and Land Use: The Telecoupling Challenge’.