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.
Category: Big Data
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 […]
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.
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’ […]
Geography & Computers: Past, present, and future
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.
MoDS: Mapping Knowledge with Data Science (MSc + PhD Studentship)
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?
MoDS: Mapping Knowledge with Data Science
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?
Geocomputation for Geoscience: The Earth System Data Cube
PhD student and King’s Geocomputation member Alejandro Coca-Castro attended Europe’s premier geosciences event, The European Geoscience Union (EGU) General Assembly, in Vienna, Austria (April 24th – 28th 2017). In addition to presenting his preliminary PhD results in the session “Monitoring the Sustainable Development Goals with the huge Remote Sensing archives”, Alejandro kindly dedicated part of his attendance at EGU to capture the emerging Geocomputation fields applied to Geosciences, and in particular for land and biosphere research. In this post Alejandro summarises the latest advances in Big Data technologies presented at EGU, which he sees as one of the two main emerging fields revolutionizing the data-driven analysis allows knowledge-production.
Urban mobility data analysis
Introducing a new member of King’s Geocomputation – Dr Chen Zhong! Chen joined King’s College London in September 2016 and her work on urban mobility directly contributes to the Geocomputation Research Domain. Here she provides a brief intro to her work.
Big Data and Bayesian Modelling Workshops
In this blog post two PhD students associated with the Geocomputation Hub – Alejandro Coca Castro and Mark de Jong – report back on workshops they recently attended. Alejandro attended a UK Data Service workshop and Mark an ESRC-funded advanced training course on Bayesian Hierarchical Models.