NERDS SEMINAR: Marina Georgati, AAU
June 1 @ 12:30 - 13:30
Machine learning for cohort-based high-resolution spatial population projections
Marina Georgati, Aalborg University CPH
Cities expand rapidly with international migration significantly contributing to urban growth and urban population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge on future population distribution due to the lack of established tools and methodologies to project where it is more likely for people of specific socio-demographic groups to set up home. The present work suggests that spatially explicit projections can play a significant role as a tool for urban planning and for managing diversity creatively, especially when a combination of social, demographic, and topographic data is utilized. Machine learning techniques have demonstrated capabilities to capture relationships among this plethora of urban features to estimate future population distribution. We present a flexible, ML-based methodology for high-resolution gridded population projections by demographic characteristics, while combining various socio-demographic and topographic input layers.
PhD fellow with background in architecture and geoinformation. I am interested on working at the intersection of socio-economic studies and computer sciences. I am currently exploring Machine Learning solutions for cohort-based spatial population projections. Passionate with urban analysis, demography and programming, I focus on migration patterns and their spatio-temporal variations at local level in European cities within the scope of the H2020 FUME Project.