Visualizing Spatial Ecological Data (VSED01)
Bring your spatial ecological datasets to life in R. Learn to interpret and
present spatial and temporal patterns with clarity, accessibility, and
scientific rigour.
------------------------------
Course Details & Format
-
*Next Session:* November 17–21, 2025 (Monday–Friday)
-
*Duration:* 5 days × ~8 hours/day = *40 hours total*
-
*Schedule:* Live online sessions in Portugal local time (GMT+1); all
sessions recorded and available immediately for on-demand access.
-
*Format:* Interactive remote classroom featuring lectures, practical R
exercises, participant data discussions, and peer collaboration.
------------------------------
What You’ll Learn
-
Visualise ecosystems using remote sensing data with RGB raster plotting
-
Measure spatial variability using both distance-based and
abundance-based methods
-
Apply multivariate and temporal visualisations—including ridgeline
plots—for dynamic ecological data
-
Manage dense datasets using scatterplots and hexagon binning
-
Create cartograms, bivariate maps, overlap metrics, and spatial density
maps to analyse species distributions
-
Design accessible, colourblind-friendly scientific graphics using the
tidyverse ecosystem
------------------------------
Who It’s For
-
Ecologists and environmental scientists interpreting spatial patterns in
ecosystems
-
Graduate researchers in ecology, geography, or related fields working
with remote sensing or species distribution data
-
Conservation practitioners and policy analysts communicating complex
spatial insights
-
Academic professionals designing teaching materials for ecological
modelling or spatial analysis
*No prior experience with R or GitHub is required.* Guided instruction and
hands-on support ensure confident learning from the ground up
------------------------------
Fees & Registration
-
*Early bird (first 10 places): £430*
-
*Standard fee: £480*
------------------------------
Why Choose VSED01?
-
*In-depth & focused:* A comprehensive five-day exploration of spatial
visualization in R
-
*Highly practical:* Real-world ecological examples using remote sensing,
raster matrices, and spatial density mapping
-
*Inclusive & accessible:* Colour-safe graphics ensure clarity for all
audiences
-
*Flexible learning:* Live instruction plus recordings, immediate
follow-up support, and 30-day access to materials
-
*Bring your own data:* Tailor the learning to your research with
dedicated data sessions and post-course email assistance
------------------------------
*Elevate your spatial ecology skills—visualise with precision, clarity, and
accessibility using R.*
Questions or want to discuss whether this fits your work? Email
oli...@prstats.org
--
Oliver Hooker PhD.
PR stats
[[alternative HTML version deleted]]
_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
