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.
Applications are invited for a three-year fully-funded PhD project as part of the new Leverhulme Centre for Wildfires, Environment and Society.
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.
This past Thursday we were really lucky to catch Dani Arribas-Bel, Senior Lecturer in Geographic Data Science at the University of Liverpool and major contributor to PySAL, on his way back home following two weeks’ […]
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 […]
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.