Introduction To Spatial Analysis Of Ecological Data Using R (ISPEPR)
1st January 2030
£450.00
Course Format
Pre Recorded
About This Course
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.
Course Details
Last Up-Dated – 26:05:2021
Duration – Approx. 24 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
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Day 1
Approx. 4 Hours
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
Day 2
Approx. 4 Hours
Creating static and interactive maps:
Customizing maps
Making facet maps
Creating animations
Using specific-purpose mapping packages
Day 3
Approx. 4 Hours
Attribute data operations:
Vector attribute subsetting, aggregation and joining
Creating new vector attributes
Raster subsetting
Summarizing raster objects
Day 4
Approx. 4 Hours
Spatial data operations:
Spatial subsetting
Topological relations
Spatial joining
Aggregation
Map algebra
Local, focal, and zonal raster operations
Day 5
Approx. 4 Hours
Geometry operations:
Geometric operations on vector data
Geometric operations on raster data
Interactions between rasters and vectors
Day 6
Approx. 4 Hours
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
Jakub Nowosad
Works at: Adam Mickiewicz University
Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create, collaborate, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book.
Teaches
- Introduction to spatial analysis of ecological data using R (ISPE)
- Making beautiful and effective maps in R (MAPR
- Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ
Teaches
- Introduction to spatial analysis of ecological data using R (ISPE)
- Making beautiful and effective maps in R (MAPR
- Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ