
Image: Random Institute/Unsplash
Geographers, including Chartered Geographers and Fellows of the Society, will be featured in keynote presentations, workshops and panels at Carto’s international Spatial Data Science Conference next week. The conference was planned to take place in-person at the Society, but was moved online and made free to attend due to the COVID-19 pandemic.
Advances in sensor and positioning technologies have increased the volume of spatial big data, with location as the golden thread that pulls together many different data sets to unlock new insight. The intersection of data science and GIS has moved the conversation from looking at where things happen, to understanding why they happen there, and looking ahead to what might happen, and where, next.
Geographers and other spatial data specialists now contribute their unique combination of spatial computation and analysis, visualisation/mapping and communication skills to an increasing range of research, commercial and not-for-profit applications, from climate change to health, real estate to transport/mobility, and consumer behaviour to community cohesiveness. This conference will explore cutting-edge techniques in spatial modelling, machine learning, spatial statistics, geo-processing at scale, and novel uses of spatial data sets in these fields and more.
Among the geographers contributing to the conference are Alistair Edwards CGeog (Lead Data Scientist, ONS) exploring how data can encourage brownfield site development for new homes; Tim Rains CGeog (Senior Data Scientist, Sainsbury’s) on building a suite of store typologies; Alex Singleton (Professor of GIS, University of Liverpool) on supporting the COVID-19 response with consumer data; and Dr Daniel Arribas-Bel (University of Liverpool) and Dr Levi John Wolf (University of Bristol), both leading members of the Society’s Quantitative Methods Research Group, will get ‘hands on’ with a focus on skills needed by today’s spatial data scientists.
Find out more about the conference and how to attend.