SocialCops is offering a free online course: Introduction to GIS: Manipulating and Mapping Geospatial Data in R, will teach you the ins and outs of how to extract, process, analyze and map geospatial data in R.
If you are working with data science, it is highly unlikely that you haven’t heard about geospatial data and all the opportunities that the field has opened for the world. From public health and governance to retail and governance, this form of data has now found proven use cases across sectors. Getting started with learning GIS can sometimes feel too daunting, considering the ample number of resources and guidelines out there. But if you are comfortable working with R, this course will help you get started with manipulating and mapping geospatial data in R.
This text- and code-based course has been developed by data scientists, economists and GIS experts. It includes complete introduction to extracting, processing, analyzing and mapping geospatial data in R. More than 100+ useful R code snippets, step-by-step demos using sample data, 50+ sample maps, and 80+ links to other free resources makes it a complete resource for anyone who is getting started with understanding geospatial data. You can sign up for the course here.
Students and professionals from more than 30 countries across the world have enrolled in the course already, including people from leading organizations like Google, MIT, Gartner, Nielsen, Columbia University, HSBC, and IBM among others.
What will you learn?
This course developed by SocialCops Academy has been divided into 6 lessons, taking you from the basic geospatial code to in-depth analysis of satellite images. At the end of the course you will be able to extract, process, analyze, and map geospatial data in R. You will also learn more about R and its visualization libraries.
Geospatial data today is being used across sectors and has wide range of applications. The lesson covers the basics of geospatial data and how it is used in market segmentation, detection, fraud prevention, and more. The lesson gives complete understanding of the public and business use cases of geospatial data.
Lesson 2: Manipulating Geospatial Data in R
Why you should use R as a GIS? The lesson starts with answering the question for you. It also includes comparison of two common R packages for geospatial analysis. It covers the fundamental geospatial operations like storing geospatial & attribute data in a spatial dataframe illustrated with sample data, along with importing spatial data into R with the sf package and simplifying sf geospatial objects before plotting.
Lesson 3: Creating Static Maps in R
After learning the analysis, the lesson introduces you with visualization of geospatial data with choropleth, inset, faceted, cartogram, dot density, proportional symbols, and hexbin maps. It covers some of the most well-known R packages for creating static geospatial maps including sf, tmap, and ggplot2
Lesson 4: Creating Animated & Interactive Maps in R
Visualization becomes more useful with animation and interactivity that shows change over time. The lesson covers 7 different packages —tmap, ggiraph, geogrid, geofacet, mapview, plotly, and leaflet — that can help you build animated and interactive maps. It also gives an overview of how to build geospatial interactive web applications with Shiny.
Lesson 5: Performing Spatial Subsetting in R
The lesson gives an overview of spatial subsetting and when it may be useful, along with different topological relations. It explains how to filter the regions in data based on their relation to other regions (such as a common border, distance from a certain point, intersection, and more).
Lesson 6: Exploring Raster Images in R
Satellites capture complex form of geospatial data called Raster data. This lesson explains raster images, its attributes and features. It also covers plotting, cropping, and building indices on raster images, and downloading Landsat 8 data, one of the best sources of free satellite data today.
Hope you enjoy taking the course and discovering more about geospatial data!