Beyond choropleth: Advanced map making with R and ggplot2


6-8 December 2023


To foster international participation, this course will be held online



Course overview

The best known cartographic representation is certainly the univariate choropleth. The first choropleth, created by Baron Charles Dupin in 1826, represented the schooling rate of boys in each French department. On a choropleth, the entities are colored according to the value of a qualitative variable (such as population density or schooling rate). But there are many other ways to represent data on a map such as dot maps or cartograms. This course presents the preliminary data processing as well as the visualization methods to realize such maps with R and ggplot2.



This course is intended for all persons concerned by the analysis and representation of spatial data (doctoral students, researchers, journalists...). No previous experience is mandatory as the course will detail the whole process of map creation (from data loading, cleaning and processing to map creation) but experience in data processing with R and/or Geographic Information Systems will facilitate the understanding of the concepts covered.

Session content




  Day 1 - 1.30 -5.30 pm Berlin time

- Univariate choropleth (data loading, cleaning, map creation and customization)
- Multivariate choropleths (bivariate, trivariate)





                   Day 2 -1.30 -5.30 pm Berlin time

                   - Cartograms (Dorling cartogram, contiguous cartogram...)
                    - Dot maps





                 Day 3 - 1.30 -5.30 pm Berlin time
                 - How to customize the designs discussed previously to        

                    create your own original maps


Available Packages

Package 1




350 €









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.