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Gbedegnon Azondekon has completed

Spatial Analysis with sf and raster in R

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4 hours
4,550 XP
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Course Description

There has never been a better time to use R for spatial analysis! The 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.
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  1. 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.

    Play Chapter Now
    Reading vector and raster data into R
    50 xp
    Reading vector data
    100 xp
    Reading raster data
    100 xp
    Getting to know your vector data
    50 xp
    sf objects are data frames
    100 xp
    Geometry is stored in list-columns
    100 xp
    Extracting geometric information from your vector layers
    100 xp
    First look at plotting vector spatial objects
    100 xp
    Getting to know your raster data
    50 xp
    Learning about your raster objects
    100 xp
    Accessing raster data values
    100 xp
    Plot your raster object
    100 xp
  2. 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.

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    A quick refresher on the coordinate reference system
    50 xp
    Vector and raster coordinate systems
    100 xp
    Transform your layers to a common CRS
    100 xp
    Plot vector and raster together
    100 xp
    Manipulating vector layers with dplyr
    50 xp
    Dropping geometry from a data frame
    100 xp
    Join spatial and non-spatial data
    100 xp
    Simplify the neighborhood boundaries
    100 xp
    Converting sf objects into sp objects and coordinates
    50 xp
    Converting sf objects to sp objects
    100 xp
    Converting to and from coordinates
    100 xp
    Manipulating raster layers
    50 xp
    Change the raster grid cell size using aggregate
    100 xp
    Change values and handle missing values in rasters
    100 xp
  3. 3

    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.

    Play Chapter Now
    Buffers and centroids
    50 xp
    Buffer layers
    100 xp
    Compute polygon centroids
    100 xp
    Bounding boxes, dissolve features and create a convex hull
    50 xp
    Create a bounding box around vector data
    100 xp
    Dissolve multiple features into one
    100 xp
    Compute a convex hull around vectors
    100 xp
    Multi-layer geoprocessing and relationships
    50 xp
    Spatial joins
    100 xp
    Spatial relationships
    100 xp
    Measuring distance between features
    100 xp
    Geoprocessing with rasters
    50 xp
    Limit rasters to focus areas
    100 xp
    Crop a raster based on another spatial object
    100 xp
    Extract raster values by location
    100 xp
    Raster math with overlay
    100 xp
  4. 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.

    Play Chapter Now
    Compute tree density and average tree canopy by neighborhood
    50 xp
    Compute tree density by neighborhood (I)
    100 xp
    Compute tree density by neighborhood (II)
    100 xp
    Compute average tree canopy by neighborhood
    100 xp
    First look at results with ggplot2
    50 xp
    Create plots using ggplot2
    100 xp
    Create a map using ggplot2
    100 xp
    Create final, polished maps with tmap
    50 xp
    Create a map using tmap
    100 xp
    Use tmap to create a final pretty map
    100 xp
    Wrap-up video
    50 xp
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GroupTraining 2 or more people?

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Datasets

CanopyImperviousLandcoverManhattanNeighborhoodsParksTrees

Collaborators

Collaborator's avatar
Richie Cotton

Prerequisites

Visualizing Geospatial Data in R
Zev Ross HeadshotZev Ross

President, ZevRoss Spatial Analysis

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