Spatial Data Visualisation and Mapping using TMAP
Visualise spatial data in R using the tmap package. Learn to create static and interactive maps, customise layouts, and publish high-quality visualisations.
Duration: 5 Days, 5 hours per day
Next Date: November 10-14, 2025
Format: Live Online Format
UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£450Registration Fee
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- Overview
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Course Description
R statistical software is becoming increasingly popular for spatial analysis and visualization—and for good reason. It is reproducible, flexible, and supported by a vast ecosystem of R packages dedicated to spatial data. An essential part of working with spatial data is visualization, not only for communication but also for exploration and analysis. This in-depth course focuses on the R package tmap, one of the most widely used packages for spatial data visualization. The course covers all key steps, from reading spatial data to publishing high-resolution static maps or interactive maps that can be embedded in web articles and dashboards. Participants will work with essential spatial data packages in particular sf, terra, and stars. The course also addresses key methodological aspects of spatial data visualization, including map projections, selecting the most appropriate visualization method for a given task, and choosing colorblind friendly color schemes that account for accessibility and cultural considerations. Innovative spatial visualization techniques are also explored via several tmap extension packages, including cartograms, glyph-based visualizations, and 3d maps.
What You’ll Learn
- Know how to use the core packages sf, terra, and stars to read and process spatial data in R, including joining data sources and performing geospatial data manipulations.
- Understand the methodological advantages and limitations of different map types, enabling informed decisions based on data characteristics and target users.
- Create various types of thematic maps.
- Be able to use tmap for exploring, analysing, and presenting spatial data.
- Recognize key considerations when selecting a suitable colour palette.
- Know how to export maps in various static and interactive formats.
- Be able to fine-tune map layouts in tmap, including adding map components, customizing legends, and incorporating map insets.
- Learn how to use tmap extension packages.
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is intended for academics and postgraduate students working on spatial data projects, as well as applied researchers and analysts in the public, private, or third sectors who require the reproducibility, speed, and flexibility of a command-line language like R for spatial data analysis and visualisation. It is also well-suited for data journalists aiming to produce engaging and informative maps for news stories, dashboards, and infographics, and for current R users seeking to update their skills with spatial data packages, particularly tmap. Participants should have a basic understanding of mathematical and statistical concepts and be comfortable using R, including tasks such as importing and exporting data, manipulating data frames, and creating simple exploratory plots. Familiarity with tidyverse packages—especially dplyr and tidyr—is recommended, as is experience with git.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Martijn Tennekes
Martijn is a statistician and data scientist with two decades of experience in teaching, academic research, and applied consulting. He specialises in data visualisation, spatial data analysis, and statistical programming, with a strong focus on open-source tools and reproducible research. An R enthusiast, Martijn is the main author of several widely used R packages for data analysis and mapping.
His career spans collaborations with numerous academic institutions, including Eindhoven University of Technology and the University of Oxford, and he has led multiple training courses for the European Commission (Eurostat). Martijn is currently based at Statistics Netherlands, where he works in the methodology department contributing to international research and innovation in statistical methods.
Martijn holds a PhD in game theory from Maastricht University (2010) and has since applied his theoretical background to practical challenges in data science and spatial analytics.
Education & Career
• 20 years of experience in statistical consulting, academic research, and teaching
• PhD in Game Theory, Maastricht University (2010)
• Senior Methodologist at Statistics Netherlands
• Course leader for Eurostat (European Union) training programs
• Collaborator with universities (including Eindhoven and Oxford) and intergovernmental organisations.
Research Focus
Martijn’s work focuses on the development and application of statistical programming techniques for data visualisation and spatial data analysis. He is particularly interested in leveraging R for creating reproducible, insightful data products and visual tools that support decision-making in research and policy.
Current Projects
• Development of R packages for spatial and statistical visualisation
• Applied research in statistical methodology at Statistics Netherlands
• Training on statistical computing and spatial data analysis with R across Europe
Teaching & Skills
• Expert in R programming, spatial analysis, and statistical visualisation
• Experienced in teaching R for applied data science, mapping, and modelling
• Active contributor to the R community and open-source development
Links
• GitHub
• TMAP
• LinkedIn
• ResearchGate
Session 1 – 01:00:00 – Overview of the core R packages for spatial data analysis and visualisation.
Session 2 – 01:00:00 – Creating Common Thematic Map Types Using the R Package tmap
Break
Session 3 – 01:00:00 – Exploring Colour Palettes with the R Package cols4all
Session 4 – 01:00:00 – Methodology of Spatial Data Visualisation
Session 5 – 01:00:00 – Review of exercises
Session 6 – 01:00:00 – Introduction to Map Projections (Coordinate Reference Systems)
Break
Session 7 – 01:00:00 – Working with Vector Data in R Using the sf Package
Session 8 – 01:00:00 – Visualisation of Vector Data with tmap
Session 9 – 01:00:00 – Review of exercises
Session 10 – 01:00:00 – Creating Cartograms with the R package tmap.cartogram
Break
Session 11 – 01:00:00 – Using Basemaps in tmap
Session 12 – 01:00:00 – Working with Raster Data in R Using the terra package
Session 13 – 01:00:00 – Review of exercises
Session 14 – 01:00:00 – Working with Spatiotemporal Data Cubes in R using the stars Package
Break
Session 15 – 01:00:00 – Visualisation of Raster Data with tmap
Session 16 – 01:00:00 – Exporting Static and Interactive Maps
Session 17 – 01:00:00 – Review of exercises
Session 18 – 01:00:00 – Integrating tmap with the R Package Shiny for Dashboards
Break
Session 19 – 01:00:00 – Fine-tuning Map Layouts for High-quality Publications
Session 20 – 01:00:00 – Extension packages of tmap: tmap.cartogram, tmap.networks, tmap.glyphs, and tmap.mapgl
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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