IRC Seminar, presented by Antonia Gieschen: "SynthEco: A digital system for analyzing multi-dimensional mechanisms of human behaviour in a multi-layered and dynamic geospatial environment"

Abstract:

Biological, social, cultural and economics factors are important determinants of health and well-being throughout life. We are presenting the open-source ecosystem “SynthEco” which utilizes statistically representative synthetic populations derived from census data for a given geospatial granularity to create a complex digital ecosystem to analyse brain-to-society pathways of human behavior within their environment. In this context, we are introducing a way of parameterising and re-parameterising individuals through a combination of genomic, health as well as behavioural and social data, using a geospatially referenced synthetic population as merging layer into which we embed through linkage and statistical extrapolation longitudinal geo-referenced discovery (MAVAN; birth cohort) and population (CLSA; Canadian Longitudinal Study of Aging, FCAC; financial wellbeing) cohorts. Through the geospatial layer the individuals are also anchored into context such as surrounding infrastructure and social environment, allowing for the analysis of individuals’ multidimensional behaviour and health outcomes over time while considering the context they are acting within. The use of such an ecosystem will be demonstrated through several application cases in Montreal, Canada, such as modelling access to healthy food and spatial inequality in financial wellbeing, as well as to introduce potential future projects across the USA and the UK.

Short Bio on Presenter: Antonia Gieschen is a lecturer in Predictive Analytics at the University of Edinburgh Business School. Previously, she was a postdoctoral research associate at the Pittsburgh Supercomputing Center with Carnegie Mellon University, where her research dealt with population synthesis through iterative proportional fitting and the integration of representative and non-representative samples into interconnected ecosystems of data. Her research utilises approaches from machine learning and computational social sciences to address topics in different application areas, including public health, financial wellbeing, food access, and tourism. In relation to that, Antonia is also interested in the use of open source software and open data. Currently, she is investigating financial wellness and relating it to geospatial inequality and the wider wellbeing of a person, as well as food systems and local access to healthy food. She holds a PhD from UEBS which focused on cluster analysis in the context of spatial and spatio-temporal data.