This course will take place over 6 x 1/2 days (17, 19, 21, 24, 26 and 28 May)
TIME ZONE – Poland local time (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 overview:
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:
1) Understand the basic concepts of spatial data analysis
2) Know R’s spatial capabilities
3) Understand how to import a range of spatial data sources into R
4) Be confident with using R’s command-line interface (CLI) for spatial data processing
5) 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)
6) Understand coordinate reference systems (CRSs), be able to decide which CRS to use, and how to reproject spatial data
7) Know how to visualize the results of a spatial analysis in the form of static and interactive maps
8) Have the confidence to apply spatial analysis skills to their own projects
Email oliverhooker@prstatistics.com with any questions
Course Programme
Monday 17th – Classes from 13:00 – 17:30
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
Wednesday 19th – Classes from 13:00 – 17:30
Creating static and interactive maps:
Customizing maps
Making facet maps
Creating animations
Using specific-purpose mapping packages
Friday 21st – Classes from 13:00 – 17:30
Attribute data operations:
Vector attribute subsetting, aggregation and joining
Creating new vector attributes
Raster subsetting
Summarizing raster objects
Monday 24th – Classes from 13:00 – 17:30
Spatial data operations:
Spatial subsetting
Topological relations
Spatial joining
Aggregation
Map algebra
Local, focal, and zonal raster operations
Wednesday 26th – Classes from 13:00 – 17:30
Geometry operations:
Geometric operations on vector data
Geometric operations on raster data
Interactions between rasters and vectors
Friday 28th – Classes from 13:00 – 17:30
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
ONLINE COURSE – Introduction to spatial analysis of ecological data using R (ISPE04) This course will delivered live
https://www.prstatistics.com/course/introduction-to-spatial-analysis-of-ecological-data-using-r-ispe04/
This course will take place over 6 x 1/2 days (17, 19, 21, 24, 26 and 28 May)
TIME ZONE – Poland local time (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 overview:
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:
1) Understand the basic concepts of spatial data analysis
2) Know R’s spatial capabilities
3) Understand how to import a range of spatial data sources into R
4) Be confident with using R’s command-line interface (CLI) for spatial data processing
5) 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)
6) Understand coordinate reference systems (CRSs), be able to decide which CRS to use, and how to reproject spatial data
7) Know how to visualize the results of a spatial analysis in the form of static and interactive maps
8) Have the confidence to apply spatial analysis skills to their own projects
Email oliverhooker@prstatistics.com with any questions
Course Programme
Monday 17th – Classes from 13:00 – 17:30
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
Wednesday 19th – Classes from 13:00 – 17:30
Creating static and interactive maps:
Customizing maps
Making facet maps
Creating animations
Using specific-purpose mapping packages
Friday 21st – Classes from 13:00 – 17:30
Attribute data operations:
Vector attribute subsetting, aggregation and joining
Creating new vector attributes
Raster subsetting
Summarizing raster objects
Monday 24th – Classes from 13:00 – 17:30
Spatial data operations:
Spatial subsetting
Topological relations
Spatial joining
Aggregation
Map algebra
Local, focal, and zonal raster operations
Wednesday 26th – Classes from 13:00 – 17:30
Geometry operations:
Geometric operations on vector data
Geometric operations on raster data
Interactions between rasters and vectors
Friday 28th – Classes from 13:00 – 17:30
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