Spatial Analysis Techniques using R - Online Course
The R environment is the computing environment of choice for most researchers for spatial statistical analysis. This course aims to:
- Introduce the use of R for geographic information analysis. Although much of what will be covered can be accomplished using a GIS, such use is awkward and often highly inefficient.
- Develop understanding of some topics beyond the basic courses or most standard texts.
After following the course and doing the assignments participants will be able to:
- Install and use the basic R environment.
- Create sensible maps of these same data.
- Select an appropriate R package for point, lattice and geostatistical data and enter spatial data into it.
- Undertake both global and local spatial analysis of the patterns these maps reveal, using the idea of complete spatial randomness as benchmark.
- Most important of all, critically assess the results of these analyses.
"Spatial analysis techniques in R" employs the standard R methods and packages (spatstat, sp, gstat), with an emphasis on the statistical analysis of three major types of spatial objects: point events, spatial lattices and continuous surfaces. If your interest is more in the display of spatial data, and especially area lattice data (e.g. states, counties, provinces), including mapping data acquired over the internet, then "Mapping in R" (using GISTools) might be a better choice.
This course may be taken individually (one-off) or as part of a certificate program.
Who should attend?
You must have a copy of R for the course. Contact The Institute for Statistics Education for information about obtaining a free copy.
Who Should Take This Course:
The course is aimed at anyone with experience either in spatial analysis using a standard GIS (such as ArcGIS), or who already uses R for basic non-spatial analysis wishing to extend their skills in spatial analysis using R. Although it covers some of the same ground, students who have followed the course Spatial Statistics with GIS given by the same instructor will find that this course usefully extends their skills.
You should be familiar with introductory statistics. Try these self tests to check your knowledge.You should also be familiar with R, or, at a minimum, a command line environment, since this course involves:
- Installing and running the R environment.
- Installing a series of packages (spatstat, map, maptools, sp, lattice, spdep and gstat) designed for use in spatial analysis.
- Driving these packages at a command line interface.
- Reading in data using read.table.
Lesson 1 contains an optional and very basic introduction to these operations that will get you started, but you should be confident of your ability to perform these simple operations in the R environment. Any 'programming' we do is simply selecting appropriate inputs to the listed packages. If you are new to R and doubtful about your ability to learn R quickly enough to follow along in the course, we recommend first taking one of the introductory R courses.
Week 1: Introducing R
- spatial and displaying some geographic data including
- visualization of point
- geostatistical data using simple maps
Week 2: Point Pattern Analysis
- global tests against the hypothesis of complete spatial randomness
- kernel density estimation
- dealing with non-homogeneity using the spatstat package
Week 3: Area (lattice) objects
- spatial autocorrelation and local statistics including
- global autocorrelation
- local indicators of spatial association
- geographical weighted regression using the spdep package
Week 4: Geostatistical data
- the analysis of continuous ‘field’ data by variography including
- interpolation by inverse distance decay
- trend surface analysis
- kriging using the gstat package
Note that the course does not concentrate on the analysis of spatially continuous data using methods that are collectively referred to as geostatistics but that Lesson 4 covers the basics.
There are four assignments in this course. These will be marked by the course leader himself, but whether you need to be concerned with these marks depends on your purpose in taking the course. Some students are interested just in learning for learning's sake, others may require a certificate showing they have completed a course, some may need academic credit (offered in selected courses only). Students enrolled in a Program in Advanced Statistical Studies also complete a guided project using spatial data.
In addition to assigned readings, this course also has an end of course data modeling project, and supplemental readings available online.
The Spatial Analysis Techniques using R - Online Course costs $589.
Certification / Credits
There are a number of options for credit and recognition. Please contact The Institute for Statistics Education for more information.
The Institute for Statistics Education
The Institute for Statistics Education at Statistics.com was established in 2002 and is the leading provider of online education in statistics, data science and analytics with 4 certificate programs and over 100 courses at novice, intermediate and advanced levels. Their...
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