Postal Codes in Color

Confession: I’m a data nerd. 

If you aren’t a data nerd, or you don’t want to risk becoming a data nerd, you may want to keep scrolling. This is going to get rather data nerdy.

Think of your postal code — or ZIP Code, as we call them in the US. As a kid, I lived in 02919 in the Northeast. Later, I lived in Texas in 75771. At some point, I learned that American postal codes are sorted roughly from northeast to southwest.

Later, I lived in the Netherlands. My postal code was 6226 in the southeast. I noticed that postal codes near Amsterdam in the north, however, all had lower numbers.

I realize as I write this how nerdy this must sound, but I have long wondered: how do other countries sort their postal codes? North to south? East to west? At random?

What! You’ve never wondered that?

Naturally, I knew the best way to see and understand postal code sort orders around the world… would be to use Tableau, the world's leading tool for seeing and understanding data.

Here’s the result:

I’ll share my process in detail; hopefully you’ll find this interesting and useful. 

To get started, I found a database of global postal codes. (link) 

Then I indexed them all from one to 100,000 alphabetically and colored them by that index. Naturally, I did this with Tableau Prep:

Now, to visualize. Tableau’s built-in geocoding meant I had [nearly] all postal code geometry available immediately.

But how to make the colors pop? Enter custom color palettes. Borrowing an open source palette called “Turbo Colormap,” I created a custom palette using the preferences.tps file. For those of you younger than 30, this is called “XML” and it’s like an early form of JSON. 

Then I started to have some fun w/ the data: putting together fun ways to explore and navigate so anyone could explore the postal code sort orders of any country. 

Finally, I assembled my story — using Story Points, naturally. Then I ran the result by several incredibly talented experts — friends from the Tableau community. Many thanks to Christian Felix , Candra McRae , Yoshihito Kimura , Pahola Diaz , and Mark Bradbourne, CBIP .

Check out the finished product here on Tableau Public — and download Tableau Public (it’s free!) to play with this data yourself.