Modern Toolbox for Spatial and Functional Data

About

Spatial and functional data analysis has become increasingly important across environmental sciences, economics, finance, and many other fields where complex data structures are now routinely collected. Spatial data can appear in several forms: in some applications they are aggregated over regions or lattices and can be described using classical spatial models; in others they are observed continuously over space, as in air pollution or climate studies, where ideas from functional data analysis provide a natural framework. Functional data methods themselves have a wide and well-developed literature, with applications ranging from modelling curves and surfaces to analysing dynamic processes such as volatility or yield curves. While both areas are broad, this course focuses on a selection of classical models together with a few recent developments, offering an introductory yet coherent view of how these tools are used in practice

Course leader

Dr. Yicong Lin (Vrije Universiteit Amsterdam)

Target group

This course is suitable for (research) master students, doctoral candidates, and postdoctoral researchers who have an interest in quantitative modelling and wish to learn the fundamentals of spatial and functional data analysis. It also welcomes professionals from research or analytical units who intend to apply these methods in their data analysis work. No prior background in spatial or functional methods is required

Course aim

By the end of the course, participants will:

Understand the conceptual background of key spatial and functional data models, including how the estimation and inference methods are obtained and how these derivations can be adapted to more general settings.
Gain familiarity with the mathematical theory underlying these techniques, with attention to assumptions, properties, and theoretical implications.
Develop practical skills for implementing the models discussed in the course, including data handling, estimation, and interpretation of results.
Acquire the foundation needed to begin reading and engaging with the research literature in spatial and functional data analysis

Fee info

Early Bird: applications must be submitted before April 13, 2026 to avail of this discount. The course fee covers tuition, course materials, daily lunches and coffee/tea during short breaks, social event including a dinner and farewell drinks. The course fee does not include accommodation. Payment