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SSTM02

Spatial and Spatial-Temporal Modelling Using R-INLA

Bayesian modelling of spatial data using R-INLA. Learn to fit, interpret, and visualise spatio-temporal models.

  • Duration: 5 Days, 7 hours per day
  • Next Date: September 22-26, 2025
  • Format: Live Online Format
TIME ZONE

UK (GMT+1) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.

£500Registration Fee

£450Early Bird Fee (EARLY BIRD SOLD OUT)

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5.0

from 200+ reviews

  • Overview
  • Instructors
  • Schedule

Course Description

The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata.

What You’ll Learn

During the course will cover the following:

  • Know the different types of spatial and spatio-temporal data available and how to work with them in R).
  • Know the different modelling approaches for spatial and spatio-temporal data).
  • Know how to visualize and produce maps of spatial and spatio-temporal data).
  • Be able to fit models with the R-INLA package).
  • Know how to interpret the output from model fitting).
  • Be confident with the use of INLA for data analysis).
  • Understand the different models that can be fit with INLA to spatial and spatio-temporal data).
  • Know how to define the different parts of a model with INLA).
  • Have the confidence to use INLA for their own projects).

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 aimed at academics and postgraduate students working on projects involving spatial and spatio-temporal data analysis and modelling, who wish to incorporate the INLA methodology for Bayesian inference into their skillset. It is also suitable for applied researchers and analysts in public, private, or third-sector organizations who require the reproducibility, speed, and flexibility offered by a command-line language like R. Designed for intermediate-to-advanced R users with an interest in data analysis and Bayesian modelling, the course assumes participants have a foundational understanding of probability, statistics, and Bayesian inference—such as the ability to define simple Bayesian models and a basic grasp of methods for approximating prior distributions, including conjugate priors and MCMC techniques. While an introduction to the INLA method will be provided, attendees should already be comfortable using R, handling various data formats (e.g., CSV, tab-delimited), creating basic plots, and manipulating data frames. Familiarity with fitting generalized linear (mixed) models using functions like glm or lme4 will be beneficial. No prior experience with spatial or spatio-temporal data is required.

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.

Download R Download RStudio Download Zoom

Dr. Virgilio Gómez-Rubio

Dr. Virgilio Gómez-Rubio

Virgilio is a statistician with deep expertise in Bayesian inference, spatial statistics, and statistical computing. His research focuses on the development and application of Bayesian methods for complex data structures, particularly using the Integrated Nested Laplace Approximation (INLA). He has made significant contributions to the R programming ecosystem through the development of widely used packages and tools for Bayesian modeling and spatial analysis.

Virgilio is the author of Bayesian Inference with INLA, a widely adopted reference in the field that received the 2022 SEIO–BBVA Foundation Award in Data Science and Big Data. He is committed to making advanced statistical methods accessible to a broad audience through clear documentation, open-source software, and active engagement with the research community via platforms like GitHub and ResearchGate.

 

Education & Career
• PhD in Statistics
• Author of Bayesian Inference with INLA
• Contributor to the R ecosystem with a focus on Bayesian and spatial modeling

 

Research Focus
Virgilio’s work centres on efficient Bayesian computation and the modelling of spatial and spatio-temporal data. He is particularly interested in applied Bayesian inference using INLA, as well as the integration of statistical methods into reproducible and scalable R workflows.

 

Current Projects
• Development of R packages for Bayesian and spatial analysis
• Applied research in epidemiology, environmental science, and spatial statistics
• Contributions to the ongoing development and documentation of the INLA methodology

 

Professional Consultancy
Virgilio provides expert support to academic and applied research teams in the areas of Bayesian modeling, spatial analysis, and statistical computing. His consultancy includes guidance on model design, computational methods, and reproducible workflows.

 

Teaching & Skills
• Teaches Bayesian inference, spatial statistics, and INLA in R
• Experienced in package development, reproducible research, and scientific communication
• Advocates for open-source tools and transparent, reproducible science

 

Links
• ResearchGate
• Google Scholar
• ORCID
• GitHub

Session 1 – 01:00:00 – Intro to INLA
Practical 1 – 01:00:00 – Intro to INLA

Session 2 – 01:00:00 – Model fitting with INLA
Practical 2 – 01:00:00 – Model fitting with INLA

Session 3 – 01:00:00 – GLMM’s with INLA
Practical 3 – 01:00:00 – GLMM’s with INLA

Q and A and end of day summary – 00:30:00

Session 4 – 01:00:00 – Spatial Data
Practical 4 – 01:00:00 – Spatial Data

Session 5 – 01:00:00 – Spatio-Temporal Data
Practical 5 – 01:00:00 – Spatio-Temporal Data

Session 6 – 01:00:00 – Advanced Visualisation
Practical 6 – 01:00:00 – Advanced Visualisation

Q and A and end of day summary – 00:30:00

Session 7 – 01:00:00 – Spatial Models for Lattice Data
Practical 7 – 01:00:00 – Spatial Models for Lattice Data

Session 8 – 01:00:00 – Spatial Models for Continuous Data
Practical 8 – 01:00:00 – Spatial Models for Continuous Data

Session 9 – 01:00:00 – Spatial Models for Point Patterns
Practical 9 – 01:00:00 – Spatial Models for Point Patterns

Q and A and end of day summary – 00:30:00

Session 10 – 01:00:00 – Spatio-Temporal Models for Lattice Data
Practical 10 – 01:00:00 – Spatio-Temporal Models for Lattice Data

Session 11 –01:00:00 – Spatio-Temporal Models for Continuous Data
Practical 11 –01:00:00 – Spatio-Temporal Models for Continuous Data

Session 12 – 01:00:00 – Spatio-Temporal Models for Point Patterns
Practical 12 –01:00:00 – Spatio-Temporal Models for Point Patterns

Q and A and end of day summary – 00:30:00

Case studies, own data and problem solving.

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?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

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?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

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£500.00
22 September 2025 - 26 September 2025
Delivered remotely (United Kingdom), Western European Time, United Kingdom
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