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.
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’ […]
Last week several members of King’s Geocomputation activity hub participated and contributed to a fieldwork mapping and monitoring party held at The Royal Geographical Society in London. Presentations and demos included crowdsourcing & OpenStreetMap, low-cost research drones and Arduino micro-controllers. This blog post summarises another presentation that explored the options for using mobile apps for fieldwork .
Today is the first day of our new Gecomputation and Spatial Analysis (GSA) pathway on our undergraduate degree. Over the summer Jon Reades, Naru Shiode and I have been developing module material and today we (well, Jon and I) finally get to use it with our students. We provide a very brief overview of the pathway on the About page of this website, but I thought today is opportune moment to discuss it in a little more depth.