Dr Margaret Kadiri is a Teaching Fellow in the Geography Department at King’s College London. Her research tackles one of the main challenges facing the tidal renewable energy sector which is the lack of understanding of the hydro-environmental impacts associated … Continue reading
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. Continue reading
Come join us for the first talk of the academic year, on September 20th at 4pm in room 6.05 in the North East Wing of Bush House! Lineu N. Rodrigues will give a brief overview of food production and water … Continue reading
In this ‘year of fire’ a joint UK and Canadian team are currently flying imaging surveys above the boreal wildfires of Northern Ontario, to learn more about their behaviour and to evaluate the performance of algorithms being designed to monitor fires from the infrared data collected by Earth orbiting satellites. The campaign is being led by Professor Martin Wooster of King’s College London and NERC’s National Centre for Earth Observation (NCEO), who travelled to Canada with researcher Dr Kari Hyll who has worked extensively on the methods used to analyse the IR data. Dr Weidong Xu, who is working on the Sentinel-3 data analysis, is supporting the team from London to provide regular updates of what the satellite can see. Continue reading
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.