Introduction to spatial data in R

Introduction

Time: 30 minutes

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

• Spatial extent
• Resolution
• Coordinate reference system

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

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

Specific to spatial data, R allows you to:

• Create automated data cleaning workflows that crop, reproject, convert data formats across many, large datasets (Leah’s example from the data institute last year)
• Repeat workflows over and over without having to step through tasks
• Repeat select portions of a workflow over and over if need be
• Add new / updated data to your workflow easily if the processing workflow is robust (e.g. timeseries data)
• Create figures that can be updated easily
• Document your workflow to remind your future self (think you in 3 months or if you’re me - tomorrow :) ) about the steps that you took to implement your workflow
• Create POWERFUL data-driven reports that include not only static plots but INTERACTIVE (e.g. leaflet)

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 - EarthDataScience.org

Attributes of vector data

• Discrete geometric locations
• x,y locations are vertices
• Organization of those vertices determines structure: points, lines, polygons