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No, this is not my boyfriend's computer. Source:

Funded PhD Opportunity: Gendering the Research Pipeline (GReP)

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

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Displacement Map SF and Bay Area

Predicting neighborhood change using big data and machine learning: Implications for theory, methods, and practice

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. 

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Aleutia R50

Conjuring: a Self-Contained Jupyter Hub for Teaching

We — me (Jon Reades), Steffen Zschaler (KCL Informatics), and Dani Arribas-Bel (Liverpool Geography) — been awarded money by the SSPP Faculty Education Fund to develop a new approach to using Jupyter notebooks for teaching, conferences, and workshops. Conjuring will use a low-power, small form-factor server running Jupyter Hub without an Internet connection, allowing it to be used in novel environments such as rural schools or in venues (schools, conference centres) where IT and networking support for advanced applications is limited or non-existent.

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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.