Data Visualization and Maps I
Content for Monday, November 27, 2023
We’ve spent the last few weeks learning about operations to compile geographic information into databases for visualization and analysis. Because analysis requires you to know something about your data and because visualization is a great way to explore your data (especially when there’s a lot of it), we’ll turn to that next. For the next few weeks, we’ll be looking at different ways to visualize spatial data and the associated approaches in R. Note that this could be an entire course by itself, but hopefully you’ll get enough to get started making publication quality maps by the time we’re done
Resources
The Introduction and Visualizing Geospatial Data chapters Principles of Figure Design section in (Wilke 2019) provide a useful set of general introductions to data visualization principles and practce that is “platform agnostic” (though much of Wilke’s work is done in
R).The Look at Data and Draw Maps chapters in (Healy 2018) revisits many of the same ideas, but focuses specifically on
Randggplot2.This post on making maps people want to look at from ESRI is a nice, concise depiction of some core principles for planning a cartographic project.
Making maps with
Rby (Lovelace et al. 2019) introduces thetmappackage for making nice maps with relatively minimal syntax.
Objectives
By the end of today you should be able to:
Describe some basic principles of data visualization
Extend principles of data visualization to the development of maps
Distinguish between several common types of spatial data visualization