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.

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  Reading vector and raster data into R

  50 xp
• 

  Reading vector data

  100 xp
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  Reading raster data

  100 xp
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  Getting to know your vector data

  50 xp
• 

  sf objects are data frames

  100 xp
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  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

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

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.

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  Buffers and centroids

  50 xp
• 

  Buffer layers

  100 xp
• 

  Compute polygon centroids

  100 xp
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  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

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.

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  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