Spatial Data Visualization in R
From Static Maps to Deployed Geospatial Dashboards for Ecology & Public Health Research
Rolling Basis – Course offering for new cohort begins every 7 weeks
Live via Zoom
Spatial Data Visualization in R (12-Day Weekend Program)
Duration: 12 days (6 weeks)
Schedule: Saturday/Sunday,7-8 PM ET (New York Time)
Registration closes: October 31, 2025
Format: Live via Zoom
Payment Options: Bank Transfer, Venmo, or Zelle accepted.
Course Fee: $250 (limited scholarships and variable pricing available based on Country of participant)
About the Training
This course provides a practical introduction to spatial data visualization in R, covering the full path from your first map to a deployed interactive geospatial application.
Participants begin with foundational spatial data concepts and static cartography, progress through interactive mapping and raster analysis, and conclude with spatial statistics and a deployed mapping dashboard.
The course emphasizes applied cartographic judgment and real spatial-analysis workflows — appropriate projections, classification schemes, and spatial statistics — for use in ecology, public health, and biodiversity research contexts.
- Instructor-led, live Zoom sessions
- Real ecological & public-health spatial datasets
- Static → interactive → raster → spatial statistics progression
- Dual-track design for GIS beginners & experienced analysts
- Capstone: a deployed interactive spatial dashboard
Who Should Register
Analysts and researchers new to R, R users who are new to spatial/GIS work, Experienced analysts wanting production-grade mapping and dashboard skills
- Basic R and statistics familiarity
- No prior ML experience required
- Conceptual, not purely mathematical approach
Learning Objectives
- Represent, manipulate, and visualize vector spatial data (points, lines, polygons) in R using the sf package, including coordinate reference systems and spatial joins.
- Produce publication-quality static choropleth and thematic maps using ggplot2's grammar of graphics, including proper map composition elements.
- Build interactive maps with leaflet and tmap, including clustering, layering, and popups for exploratory and public-facing use.
- Work with raster data in R using terra, including resampling, zonal statistics, and multi-band composites.
- Apply spatial statistics (e.g., Moran's I, LISA cluster maps) and deploy a spatial dashboard combining vector and raster data.
Apply machine learning workflows in R for real-world datasets.
Build, tune, and evaluate predictive and unsupervised models.
Implement feature engineering and interpret model outputs.
Communicate findings using reproducible R Markdown or Shiny dashboards.
Design and present an end-to-end ML project.
Weekly Breakdown
Each week includes:
🎓 Lecture slides (PowerPoint)
💻 R lab exercises (sf + terra)
📊 Synthetic regional/ecological datasets
Computing & Tools
All analyses are done in R using packages like sf, ggplot2, leaflet, tmap, and terra.
All example files and syntax are downloadable from the course portal.