Time: 30 minutes

Purpose: In this lesson we will introduce some of the key concepts associated with spatial data including:

We will also define raster vs. vector data structures.

Background: Often we need to use spatial data in our analysis - that is data that correspond to some x,y (and z) location on the Earth. however, not all spatial data are created the same. understanding the basic foundations of working with spatial data will save you a lot of time when it comes to processing the data in tools like R, QGIS and python!

Learning outcomes

At the end of this 30 minute overview you will be able to:

  1. Explain the difference between the raster vs vector data structures.
  2. Define spatial extent, resolution, coordinate reference system.
  3. List the 3 vector data structures (point, line, polygon).
  4. List 2-3 R packages that can be used to work with spatial data.

Getting started with R

the R / Python learning curve

the R / Python learning curve

Why R for GIS - the power of automation

It may be a bit of extra effort at the beginning to learn a tool like R. However, the payoff is large once you learn the basic skills to support task automation.

Geeks and automation

Geeks and automation

Specific to spatial data, R allows you to:

In this lesson we are going to learn the basics of working with spatial data in R. Some of you may think this is the boring stuff - however understanding these concepts will allow you to do just about anything that you want with spatial data in R!

About vector data

Intro to vector data -

Attributes of vector data