ONLINE COURSE – Introduction to spatial analysis of ecological data using R (ISPE05) This course will delivered live
11 July 2022 - 14 July 2022
£450.00
Event Date
Monday, July 11th, 2022
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Details
The aim of the course is to introduce you to a spatial data processing, analysis, and visualization capabilities of the R programming language. It will teach a range of techniques using a mixture of lectures, computer exercises and case studies.
By the end of the course participants should:
- Understand the basic concepts of spatial data analysis
- Know R’s spatial capabilities
- Understand how to import a range of spatial data sources into R
- Be confident with using R’s command-line interface (CLI) for spatial data processing
- Be able to perform a range of attribute operations (e.g. subsetting and joining), spatial operations (e.g. distance relations, topological relations), and geometry operations (e.g. clipping, aggregations)
- Understand coordinate reference systems (CRSs), be able to decide which CRS to use, and how to reproject spatial data
- Know how to visualize the results of a spatial analysis in the form of static and interactive maps
- Have the confidence to apply spatial analysis skills to their own projects
Intended Audiences
- Academics and post-graduate students working on projects related to spatial data and want access to a powerful (geo)statistical and visualization programming language.
- Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data.
Venue
Delivered remotely
Course Information
Venue – Delivered remotely
Time zone – Poland local time (UTC+2)
Availability – 20 places
Duration – 6 days
Contact hours – Approx. 27 hours
ECT’s – Equal to 2 ECT’s
Language – English
Teaching Format
The course will be a mixture of theoretical and practical. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.
Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data.
Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames.
However, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types, and some programming experience, the course may also be appropriate for you.
Equipment and software requirements
Attendees are expected to have a working version of R or RStudio installed.
R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links
https://cloud.r-project.org/
You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com
Assumed quantitative knowledge
Coming soon..
Assumed computer background
Coming soon..
Equipment and software requirements
Attendees will need to install/update R/RStudio and various additional R packages.
This can be done on Macs, Windows, and Linux.
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/
PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Monday 11th – Classes from 09:00 – 17:00
Introduction to the course
Key concepts related to spatial data
R’s spatial ecosystem
Reading data from spatial file formats
Understanding R’s spatial classes
Creating static and interactive maps:
Customizing maps
Making facet maps
Tuesday 12th – Classes from 09:00 – 17:00
Attribute data operations:
Vector attribute subsetting, aggregation and joining
Creating new vector attributes
Raster subsetting
Summarizing raster objects
Spatial data operations:
Spatial subsetting
Wednesday 13th – Classes from 09:00 – 17:00
Spatial data operations:
Topological relations
Spatial joining
Aggregation
Map algebra
Local, focal, and zonal raster operations
Geometry operations:
Geometric operations on vector data
Geometric operations on raster data
Thursday 14th – Classes from 09:00 – 17:00
Geometry operations:
Interactions between rasters and vectors
Understanding of the coordinate reference systems (CRSs)
Reprojecting geographic data
Modifying map projections
Retrieving open data from web sources
Using R packages for spatial data retrieval
Writing spatial data
Course Instructor
Dr. Antoine Becker-Scarpitta
Works at – University of Helsink
Teaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.