Visualize This: The FlowingData Guide to Design, Visualization, and Statistics

Mapping

Some overlap exists between the covered visualization tools and the ones that you use to map geographic data. However, the amount of geographic data has increased significantly in the past years as has the number of ways you can map. With mobile location services on the rise, there will be more data with latitude and longitude coordinates attached to it. Maps are also an incredibly intuitive way to visualize data, and this deserves a closer look.

Mapping in the early days of the web wasn’t easy; it wasn’t elegant either. Remember the days you would go to MapQuest, look up directions, and get this small static map? Yahoo had the same thing for a while.

It wasn’t until a couple of years later until Google provided a slippy map implementation (Figure 3-23). The technology was around for a while, but it wasn’t useful until most people’s Internet speed was fast enough to handle the continuous updating. Slippy maps are what we’re used to nowadays. We can pan and zoom maps with ease, and in some cases, maps aren’t just for directions; they’re the main interface to browse a dataset.

Note

Slippy maps are the map implementation that is now practically universal. Large maps, that would normally not fit on your screen, are split into smaller images, or tiles. Only the tiles that fit in your window display, and the rest are hidden from view. As you drag the map, other tiles display, making it seem as if you’re moving around a single large map. You might have also seen this done with ...