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.
Trust, but verify – working with administrative data
Open data has become a mantra for governments and, in many cases, the backbone of commercial and academic projects. In particular, data collected by public bodies in the pursuit of regulatory and legal functions tend […]
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
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.
PhD opportunity: Agent-based methods for human fire activity in Dynamic Global Vegetation Models
Applications are invited for a three-year fully-funded PhD project as part of the new Leverhulme Centre for Wildfires, Environment and Society.
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.
CUSP London Seminar: Dani Arribas-Bel
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’ […]
Fully Funded PhD Studentship Opportunity
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 […]
Our first Python coding dojo!
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.
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.