Overview

This course provides a comprehensive introduction to spatial data analysis, with a strong emphasis on visualizing spatial matrices and point patterns. Participants will explore key challenges in scientific visualization accessibility, particularly in relation to colorblind-friendly design. The course offers hands-on experience using R packages for spatial analysis, focusing on practical applications in ecological studies.

No specific requirements are needed. Prveious experience with R is not mandatory.

Learning outcomes

By the end of this course, participants will be able to:

• Visualizing Remote Sensing Data

    Use RGB plotting and ggplot2 to visualize remote sensing data with raster matrices in ecological studies.

• Understanding Spatial Variability

Differentiate between distance-based and abundance-based measures of spatial variability and apply them to ecological data analysis.

• Analyzing Temporal Variability

  Create and interpret ridgeline plots to explore and visualize temporal variability in ecological datasets.

• Utilizing Spatial Analysis Techniques

Generate scatterplots and hexagon binning plots to visualize spatial data from remote sensing sources.

• Addressing Accessibility in Scientific Visualization

Understand colorblindness challenges in scientific graphs and design colorblind-friendly visualizations to ensure accessibility in spatial data representation.

Daily schedule: 3 - 6 PM (Berlin time)

Day 1: Spatial Matrices (17 June, 3:00 PM – 6:00 PM Berlin time)

    *Visualizing ecosystems through remote sensing data: RGB plotting and proper use of ggplot2 with raster matrices
    *Spatial variability: Distance-based vs. abundance-based measures
    *Multivariate analysis of ecological data
    *Temporal variability: Creating ridgeline plots
    *Scatterplots of remote sensing data and hexagon binning

Day 2: Point Patterns (18 June, 3:00 PM – 6:00 PM Berlin time)

   *Species distributions:
        - Cartograms, overlap, and bivariate maps
        - Spatial density maps in R
        - Scatterplot matrices
    *Simulating spatial distributions of species in data cubes using the gcube package
    *Modeling virtual species

Day 3: Colorblindness in Scientific Visualization (19 June, 3:00 PM – 6:00 PM Berlin time)

    *Challenges of colorblindness in scientific graphs
    *Improving accessibility using the tidyverse
    * Designing colorblind-friendly visualizations for spatial data

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.