Statistical Methods for Understanding Human and Environment Interactions: Building on Latent Processes
Conference
Proposal Description
Understanding the environment and how human activities impact it is crucial to building a resilient world. This session will present current statistical methodologies developed to model the environmental processes correlated to human activities. It will emphasize current challenges such as data integration, continuous data sources and complex spatiotemporal interactions.
This session will present contributions in spatiotemporal statistics, data fusion, functional data analysis, and rare events. It will be conducted by a diverse range of early career researchers, PhD students, Postdocs, and Lecturers.
Submissions
- A Bayesian Multisource Fusion Model for Spatiotemporal PM2.5 and NO2 Concentrations: For Exposure Health Assessment in an Urban Setting
- Estimating sources of particle matter: A functional data modelling approach
- GEOBEx a Statistical Method for Air Quality Level Predictions
- Monitoring and predicting food insecurity from population non-representative survey and satellite imagery