There has never been a better time to use R for spatial analysis! The brand new
sf package has made working with vector data in R a breeze and the
raster package provides a set of powerful and intuitive tools to work gridded data like satellite imagery. Instead of the painful process of performing your spatial analysis in GIS systems like ArcGIS or QGIS and then shuffling your results into another system for analysis you can move your entire spatial analysis workflow into R. In this course you will learn why the
sf package is rapidly taking over spatial analysis in R. You will read in spatial data, manipulate vectors using the
dplyr package and learn how to work with coordinate reference systems. You’ll also learn how to perform geoprocessing of vectors including buffering, spatial joins, computing intersections, simplifying and measuring distance. With rasters you will aggregate, reclassify, crop, mask and extract. The last chapter of the course is devoted to showing you how to make maps in R with the
tmap packages and performing a fun mini-analysis that brings together all your new skills.
Spatial Analysis in R with
raster features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you an expert in spatial analysis in R!
What you’ll learn
1. Vector and Raster Spatial Data in R
An introduction to import/export, learning the formats and getting to know spatial data. Some discussion of why we’re using
sf rather than
2. Preparing layers for spatial analysis
In this lesson, you will learn how to prepare layers so that you can conduct spatial analysis. This includes ensuring that the layers all share the same coordinate reference system.
3. Conducting spatial analysis with the sf and raster packages
Now that you have learned about
raster objects, and have prepared your layers for analysis, we can begin conducting true spatial analysis. Both
raster have a suite of functions that allow you to do single-layer kinds of analysis like buffering and computing hulls as well as multi-layer operations like intersections, overlaps, masking and clipping.
4. Combine your new skills into a mini-analysis
You are now ready to combine your skills into a mini-analysis. The goal is to evaluate whether the average canopy density by NYC neighborhood is correlated with the number of trees by neighborhood and to create a nice plot of the result.