FREE ONLINE COURSE

Introduction to GIS

Introduction to GIS

Manipulating and Mapping Geospatial Data in R

Manipulating and Mapping Geospatial Data in R

R is a fantastic open source language for working with data, and geospatial data is no exception. This online course will teach you how to get up and running with extracting, processing, analyzing and mapping geospatial data in R.

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Never used R or need a refresher?

This course was designed for people who are comfortable working with data in R. If you're new to R, first check out the excellent free ebook, R for Data Science by Hadley Wickham and Garrett Grolemund. The introduction will help you set up R and download common packages.

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Don't want to take this course online? Prefer reading offline? View and download the entire course as a PDF.

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Use cases of geospatial data

LESSON #1

Use Cases of Geospatial Data

Use Cases of Geospatial Data

LESSON #1

Use Cases of Geospatial Data

What is geospatial data, where does it come from, and why is it worth your attention? In this lesson, you'll get an overview of how geospatial data is being used today across sectors to segment markets, detect and prevent fraud, improve delivery routes, identify vulnerable populations, and more.

KEY TOPICS:

  • Basic information about geospatial data

  • Business use cases for geospatial data

  • Public use cases for geospatial data

Manipulating geospatial data

LESSON #2

Manipulating Geospatial Data in R

Manipulating Geospatial Data in R

LESSON #2

Manipulating Geospatial Data in R

This lesson starts with the basics — why we recommend R as a GIS, and a comparison of two common R packages for geospatial analysis. Next, you'll walk through fundamental geospatial operations, illustrated with state-level population and economic data for India.

KEY TOPICS:

  • Importing spatial data into R with the sf package

  • Storing geospatial & attribute data in a spatial dataframe

  • Simplifying sf geospatial objects before plotting

Creating static geospatial maps

LESSON #3

Creating Static Maps in R

Creating Static Maps in R

LESSON #3

Creating Static Maps in R

The next step after analysis is visualization. This lesson introduces some of the most well-known R packages for creating static geospatial maps. It covers traditional visualizations like choropleth maps, as well as ones that aren't true geographic visualizations but still convey geospatial data.

KEY TOPICS:

  • sf, tmap and ggplot2 packages in R

  • Choropleth, inset, faceted, geofaceted, cartogram, dot density, proportional symbols, and hexbin maps

Creating animated & interactive geospatial maps

LESSON #4

Creating Animated and Interactive Maps in R

Creating Animated and Interactive Maps in R

LESSON #4

Creating Animated and Interactive Maps in R

Animation and interactivity are especially well-suited to geospatial data since they can show change over time. This lesson walks you through 7 different R packages for building animated and interactive maps, plus an overview of how to build geospatial web applications with Shiny.

KEY TOPICS:

  • Animated maps with tmap and gganimate

  • Interactive maps with tmap, ggiraph, geogrid, geofacet, mapview, plotly and leaflet

  • Interactive web applications with Shiny

Performing spatial subsetting

LESSON #5

Performing Spatial Subsetting in R

Performing Spatial Subsetting in R

LESSON #5

Performing Spatial Subsetting in R

Spatial subsetting helps you tap into the actual geometry of geospatial data. This lesson explains how to filter the regions in your data based on their relation to other regions (such as a common border, distance from a certain point, intersection, and more).

KEY TOPICS:

  • Spatial subsetting and when it may be useful

  • Different topological relations

  • 3 methods for spatially subsetting your data

Satellite/raster images

LESSON #6

Exploring Raster Images in R

Exploring Raster Images in R

LESSON #6

Exploring Raster Images in R

Raster data — or the images and data captured by satellites — is an even more complex form of geospatial data. This lesson explains what raster images are, where to get them, how to extract and process them, and what basic operations and analysis you can do on them.

KEY TOPICS:

  • Raster attributes and features

  • Downloading and reading Landsat 8 data

  • Plotting, cropping and building indices on raster images

Feedback, questions, comments or requests?

Email us at [email protected]

Feedback, questions, comments or requests? Email us at [email protected]

This course was published in January 2019. It was written by Sean Angiolillo and Himanshu Sikaria, and edited by Christine Garcia.