Mapping Geospatial Data

The folks at Sharp Sight Labs have a great blog post on mapping geospatial data using R:

If you’ve learned the basics of data visualization in R (namely, ggplot2) and you’re interested in geospatial visualization, use this as a small, narrowly-defined exercize to practice some intermediate skills.

There are at least three things that you can learn and practice with this visualization:

  1. Learn about color: Part of what makes this visualization compelling are the colors. Notice that in the area surrounding the US, we’re not using pure black, but a dark grey. For the title, we’re not using white, but a medium grey. Also, notice that for the rivers, we’re not using “blue” but a very specific hexadecimal color. These are all deliberate choices. As an exercise, I highly recommend modifying the colors. Play around a bit and see how changing the colors changes the “feel” of the visualization.

  2. Learn to build visualizations in layers: I’ve emphasized this several times recently, but layering is an important principle of data visualization. Notice that we’re layering the river data over the USA country map. As an exercise, you could also layer in the state boundaries between the country map and the rivers. To do this, you can use map_data().

  3. Learn about ‘Spatial’ data: R has several classes for dealing with ‘geospatial’ data, such as ‘SpatialLines‘, ‘SpatialPoints‘, and others. Spatial data is a whole different animal, so you’ll have to learn its structure. This example will give you a little experience dealing with it.

I also like the iterative approach they discuss.  You’ll almost never get it right the first go-around, but one of the nice things about ggplot2 is that it’s designed to be iterative:  you layer your changes on, making it a bit easier to fiddle with them to get the visualization just right.

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