Spatial Analysis of Health Data - Online Course
A 4-Day Livestream Seminar Taught by
Simon BrewerTuesday, August 11 —
Friday, August 14, 2026
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
The spatial context of health data is increasingly recognized as a critical factor in disease surveillance, resource allocation, and planning. Indexing health outcomes by location allows the exploration of patterns in space and the correlation of those patterns to social and environmental factors. This allows us to gain insights into exposure to environmental hazards, healthcare accessibility, and inequities in health outcomes. However, the spatial nature of these data can lead to biases when using standard inferential approaches (e.g., OLS models), necessitating specialized approaches.
The goal of this seminar is to help participants understand the issues that arise when working with geospatial health data and gain experience using tools to address them. Key topics include accessing and working with spatial data, visualizing spatial patterns through static and interactive maps, exploring spatial dependency, and building spatial regression models. We will also extend these methods to temporal datasets with multiple time points.
Starting August 11, this seminar will be presented as a 4-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. If you can’t join in real time, recordings will be available within 24 hours and accessible for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
Placing public health data in a spatial context allows for the exploration of environmental and social factors that may be linked to health outcomes. Spatial data are characterized by spatial dependency, where similar values cluster, and spatial heterogeneity, where correlations among variables differ across locations. Standard inferential approaches risk ignoring these effects and can lead to inefficient and/or biased model results. This seminar will introduce a structured workflow for managing, visualizing, and modeling spatial health data, with a particular focus on areal data (data aggregated within regions).
Throughout the seminar, you will gain experience using R/RStudio as the computational environment, which will support the entire workflow. Beginning with an introduction to spatial data concepts, you will learn how to manipulate, filter, and organize these data. We will then explore techniques for visualizing spatial data, from simple base plots to dynamic interactive maps.
Concurrently, you will be introduced to spatial regression modeling, learning how to incorporate spatial dependency into your models. Worked examples will demonstrate the setup, execution, and interpretation of spatial regression models, and we will conclude by extending these techniques to spatio-temporal data. Hands-on exercises throughout the course will enhance your understanding of these methods, including advanced techniques such as Integrated Nested Laplacian Approximation (INLA) for fitting complex spatial models.
Placing public health data in a spatial context allows for the exploration of environmental and social factors that may be linked to health outcomes. Spatial data are characterized by spatial dependency, where similar values cluster, and spatial heterogeneity, where correlations among variables differ across locations. Standard inferential approaches risk ignoring these effects and can lead to inefficient and/or biased model results. This seminar will introduce a structured workflow for managing, visualizing, and modeling spatial health data, with a particular focus on areal data (data aggregated within regions).
Throughout the seminar, you will gain experience using R/RStudio as the computational environment, which will support the entire workflow. Beginning with an introduction to spatial data concepts, you will learn how to manipulate, filter, and organize these data. We will then explore techniques for visualizing spatial data, from simple base plots to dynamic interactive maps.
Concurrently, you will be introduced to spatial regression modeling, learning how to incorporate spatial dependency into your models. Worked examples will demonstrate the setup, execution, and interpretation of spatial regression models, and we will conclude by extending these techniques to spatio-temporal data. Hands-on exercises throughout the course will enhance your understanding of these methods, including advanced techniques such as Integrated Nested Laplacian Approximation (INLA) for fitting complex spatial models.
Computing
This seminar uses R as the primary software platform, with various add-on packages for spatial analysis (e.g., spdep, spatialreg, INLA). All necessary software and datasets will be distributed prior to the course, along with instructions for installation.
While basic familiarity with R is recommended, even novice users should be able to follow the presentations and complete the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This seminar uses R as the primary software platform, with various add-on packages for spatial analysis (e.g., spdep, spatialreg, INLA). All necessary software and datasets will be distributed prior to the course, along with instructions for installation.
While basic familiarity with R is recommended, even novice users should be able to follow the presentations and complete the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
This seminar is ideal for anyone working with or intending to work with spatial health data, particularly those interested in building inferential models. You will develop practical skills in data manipulation, visualization, and modeling. Although the focus is on applications in the health sciences, participants who want to apply spatial techniques to other subject areas can definitely benefit from this seminar.
Some prior knowledge of linear or logistic regression is recommended.
This seminar is ideal for anyone working with or intending to work with spatial health data, particularly those interested in building inferential models. You will develop practical skills in data manipulation, visualization, and modeling. Although the focus is on applications in the health sciences, participants who want to apply spatial techniques to other subject areas can definitely benefit from this seminar.
Some prior knowledge of linear or logistic regression is recommended.
Seminar outline
Introduction to spatial data and spatial autocorrelation
-
- Introduction to computing environment, R and RStudio
- Introduction to spatial data (vector vs. raster, coordinates, projections, attributes)
- Manipulating and visualizing spatial data (overlays, buffering, aggregation)
- Developing a general model for spatial data
- Assessing spatial dependency (correlograms, Moran’s I, Getis-Ord G*)
Spatial regression models
-
- Spatial models for point data (GLS)
- Spatial models for areal data (SAR)
- Introduction to INLA
- Spatial areal models (CAR)
- Continuous outcomes
- Count/rate outcomes
Spatio-temporal modeling, spatial heterogeneity
-
- Interactive mapping with leaflet
- Brief introduction to temporal dependency
- Spatio-temporal regression modeling with non-separable dependency
- Analyzing spatial heterogeneity with geographically weighted models
Introduction to spatial data and spatial autocorrelation
-
- Introduction to computing environment, R and RStudio
- Introduction to spatial data (vector vs. raster, coordinates, projections, attributes)
- Manipulating and visualizing spatial data (overlays, buffering, aggregation)
- Developing a general model for spatial data
- Assessing spatial dependency (correlograms, Moran’s I, Getis-Ord G*)
Spatial regression models
-
- Spatial models for point data (GLS)
- Spatial models for areal data (SAR)
- Introduction to INLA
- Spatial areal models (CAR)
- Continuous outcomes
- Count/rate outcomes
Spatio-temporal modeling, spatial heterogeneity
-
- Interactive mapping with leaflet
- Brief introduction to temporal dependency
- Spatio-temporal regression modeling with non-separable dependency
- Analyzing spatial heterogeneity with geographically weighted models
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.