Upcoming intensive online courses in spatial analysis with R at SMSC
NZCBI-SCBI Training
The Smithsonian-Mason School of Conservation (SMSC), a partnership between George Mason University and the Smithsonian Conservation Biology Institute (SCBI), offers focused intensive training geared toward graduate students and professionals working in biodiversity conservation and management. All courses offer continuing education credits (CEUs) and can be taken for graduate credit at additional cost. Full scholarships are available for citizens of countries outside of the US, Canada and Europe and those interested in scholarships should apply early. You will be able to indicate interest in competing for a scholarship when you apply. If you have additional questions after reviewing the course web page, please contact: SCBITr...@si.edu
GIS in R: Fundamentals and applications for ecologists - ONLINE
Website: https://smconservation.gmu.edu/programs/graduate-and-professional/gis-ol/
Dates: October 21 – December 15, 2024
Cost: 500.00 USD (full scholarships available for eligible applicants)
Deadline: August 19, 2024
Description: This 8-week online course is designed to introduce R users to the expanding range of tools available for working with spatial data in R. More and more, it is feasible to conduct all processing and manipulation of spatial data, as well as generation of publication-quality maps, inside the R environment. Learning to do this effectively provides huge advantages to those already working in the R environment, including seamless integration between spatial and non-spatial data, as well as a lack of reliance on costly, menu-based GIS programs. The course will teach participants how to import, project, and manipulate both vector/shape and raster data and will cover a wide range of available functions. The primary R packages used in the course will be sf and terra, with some use of sp and raster. The course closely integrates tidyverse functions with GIS tools to develop efficient and intuitive coding. Content will also focus on visualizing spatial data in R with both static and dynamic maps, as well as with web applications in RShiny. See the course webpage for a complete list of course topics. This is course is ideal for those already working in the R environment who are looking to expand their R skills to include working with spatial data. Those without any background in R should work to become familiar with basic R programming before attending. Some pre-course preparation work will be provided to help toward building this familiarity.
Format: This course is taught in an asynchronous format over 8 weeks, with at least 2 weekly opportunities for live feedback and instructor interaction over Zoom. This format allows for maximum flexibility for participants juggling complicated work schedules, classes and other commitments.
Animal Space Use and Movement Analysis - ONLINE
Website: https://smconservation.gmu.edu/programs/graduate-and-professional/animal_movement-ol/
Dates: October 21 – December 15, 2024
Cost: 500.00 USD (full scholarships available for eligible applicants)
Deadline: August 19, 2024
Description: This 8-week online course teaches the latest approaches for the summary and analysis of animal movement data in the R environment. Course material focuses on the application of these data to research questions involving home ranges, movement behavior, habitat selection, and connectivity. The goals of the course are to: 1) teach the core themes and concepts underpinning animal movement behavior and the determinants of animal space use; 2) familiarize participants with the range of tools available to import, clean, summarize, visualize and analyze animal movement data in R and; 3) expose participants to the challenges and potential biases inherent in movement data, and how to address them at the design and analytical stage. See the course webpage for a complete list of course topics. This course is ideal for graduate students and professionals already familiar with how to interact with spatial data in R, but looking to apply those skills to more advanced analytical approaches specific to animal tracking data. Class lessons and exercises will use real animal tracking datasets, and will integrate tidyverse functions to maximize coding efficiency. Additional packages covered in the course include ctmm, adehabitatLT, adehabitatHR, moveHMM, gdistance and others.
Format: This course is taught in an asynchronous format over 8 weeks, with at least 2 weekly opportunities for live feedback and instructor interaction over Zoom. This format allows for maximum flexibility for participants juggling complicated work schedules, classes and other commitments.