Spatial Statistics

Oct 27, 2021 · Zürich, Switzerland

After a long break, Zurich R User Meetup is finally back... We are looking forward to seeing you at our upcoming event!
Please read the details below.


*Safety Concept for the Zurich R User Meetup @ UZH on WED, 27 SEP 2021*
All Zurich R meetup attendees must bring a valid Covid certificate as well as an ID card. The event organizer will check this information for all meetup attendees. Moreover, all attendees and organizers must wear masks except for the speakers during the presentation.


6.15 pm Doors open, checking Covid Certificates --> Entry is restricted to participants with a valid Covid certificate -- everyone must wear masks. [Please read above.]
6.30 pm Welcome Zurich RUG
6.40 pm Talks (see below)
~7.55 pm The swisstopo map Challenge (see below)
~ 8.05 pm Are you hiring? Looking for an R related job? ... or organising an interesting event? Take the stage for one minute. (DM us if you need a slot!)
~ 8 - 9 pm Beer outside, location tbd


Patrick Schratz -- M.Sc. in Geoinformatics, member of the mlr-org core team, developer of the mlr/mlr3 machine learning framework in R
The R package {mlr3} and its associated ecosystem of extension packages implements a powerful, object-oriented and extensible framework for machine learning (ML) in R.
It provides a unified interface to many learning algorithms available on CRAN, augmenting them with model-agnostic general-purpose functionality that is needed in every ML project, for example train-test-evaluation, resampling, preprocessing, hyperparameter tuning, nested resampling, and visualization of results from ML experiments.
The package is a complete reimplementation of the mlr (Bischl et al., 2016) package that leverages many years of experience and learned best practices to provide a state-of-the-art system that is powerful, flexible, extensible, and maintainable.
We target both practitioners who want to quickly apply ML algorithms to their problems and researchers who want to implement, benchmark, and compare their new methods in a structured environment.
{mlr3} is suitable for short scripts that test an idea, for complex multi-stage experiments with advanced functionality that use a broad range of ML functionality, as a foundation to implement new ML (meta-)algorithms (for example AutoML systems), and everything in between.
Functional correctness is ensured through extensive unit and integration tests.
This talk showcases how to use {mlr3} with spatiotemporal data for performance estimation and prediction, making use of the extension packages {mlr3spatiotempcv} and {mlr3spatial}.

Jakob Dambon -- PhD, Data Scientist Intern @ Swiss Re, External Lecturer @ Lucerne University of Applied Sciences and Arts
varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models
Gaussian processes (GPs) are well-known tools for modeling dependent data with applications in spatial statistics, time series analysis, or econometrics. In this talk, we present the R package varycoef that implements estimation, prediction, and variable selection of linear models with spatially varying coefficients (SVC) defined by GPs, so called GP-based SVC models. Such models offer a high degree of flexibility while being relatively easy to interpret. Using varycoef, we show versatile applications of (spatially) varying coefficient models on spatial and time series data. This includes model and coefficient estimation with predictions and variable selection. The package uses state-of-the-art computational statistics techniques like parallelization, model-based optimization, and covariance tapering. This allows the user to work with (S)VC models in a computationally efficient manner, i.e., model estimation on large data sets is possible in a feasible amount of time.

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