Interactive Course

Spatial Analysis in R with sf and raster

Analyze spatial data using the sf and raster packages.

4 hours
15 Videos
53 Exercises
4,086 Participants
4,550 XP

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Course Description

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 ggplot2 and tmap packages and performing a fun mini-analysis that brings together all your new skills.

Spatial Analysis in R with sf and raster

An introduction to import/export, learning the formats and getting to know spatial data. Some discussion of why we're using sf rather than sp.

Now that you have learned about sf and raster objects and have prepared your layers for analysis we can begin conducting true spatial analysis. Both sf and 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.

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.

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.

1
Vector and Raster Spatial Data in R
Free

An introduction to import/export, learning the formats and getting to know spatial data. Some discussion of why we're using sf rather than sp.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
Conducting spatial analysis with the sf and raster packages

Now that you have learned about sf and raster objects and have prepared your layers for analysis we can begin conducting true spatial analysis. Both sf and 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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

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Zev Ross

President, ZevRoss Spatial Analysis

Zev is the President of ZevRoss Spatial Analysis, a company that focuses on data science, machine learning, and development of data applications with a focus on spatial data. He is the author or co-author of over 40 peer-reviewed publications that focus on public health, spatial analysis and statistics. Zev also runs a popular data science blog and teaches workshops on spatial analysis and machine learning with R.

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Spatial Analysis in R with sf and raster

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Course Description

Vector and Raster Spatial Data in R

Reading vector and raster data into R

Reading vector data

Reading raster data

Getting to know your vector data

sf objects are data frames

Geometry is stored in list-columns

Extracting geometric information from your vector layers

First look at plotting vector spatial objects

Getting to know your raster data

Learning about your raster objects

Accessing raster data values

Plot your raster object

Conducting spatial analysis with the sf and raster packages

Buffers and centroids

Buffer layers

Compute polygon centroids

Bounding boxes, dissolve features and create a convex hull

Create a bounding box around vector data

Dissolve multiple features into one

Compute a convex hull around vectors

Multi-layer geoprocessing and relationships

Spatial joins

Spatial relationships

Measuring distance between features

Geoprocessing with rasters

Limit rasters to focus areas

Crop a raster based on another spatial object

Extract raster values by location

Raster math with overlay

Preparing layers for spatial analysis

A quick refresher on the coordinate reference system

Vector and raster coordinate systems

Transform your layers to a common CRS

Plot vector and raster together

Manipulating vector layers with dplyr

Dropping geometry from a data frame

Join spatial and non-spatial data

Simplify the neighborhood boundaries

Converting sf objects into sp objects and coordinates

Converting sf objects to sp objects

Converting to and from coordinates

Manipulating raster layers

Change the raster grid cell size using aggregate

Change values and handle missing values in rasters

Combine your new skills into a mini-analysis

Compute tree density and average tree canopy by neighborhood

Compute tree density by neighborhood (I)

Compute tree density by neighborhood (II)

Compute average tree canopy by neighborhood

First look at results with ggplot2

Create plots using ggplot2

Create a map using ggplot2

Create final, polished maps with tmap

Create a map using tmap

Use tmap to create a final pretty map

Wrap-up video