Stickers and Stamps: A Bright Spot in 2020
By: Kristin Henry
Tags: Citizen Science, Us Postal Service, Maps, Art, Data Visualization

2020 has been a rough year in so many ways. The relentless news cycle, ubiquitous uncertainty, social isolation, existential dread, and so much more. All amplified by social media.

The attack on the US Postal Service hit me harder than I’d expect it to. I felt driven to buy as many stamps as I could, to support the USPS.

At some point, I found myself staring at the pile of art stickers I was going to give away at conferences and shows…all cancelled because of Covid.

What was I going to do with them? Could I mail them to people?

I started with a little group of artist-friends and set up an online signup form, so they could send me their mailing addresses privately.

USPS Forever Stamps honoring Ruth Asawa

With perfect timing, the USPS was about to release new stamps honoring local artist Ruth Asawa, just as I was launching an art+data project from right in her neighborhood in San Francisco.

The process of writing little notes and addressing the envelopes by hand was surprisingly soothing. I started inviting more people to signup, and asked them to tell me when their sticker arrived. Before I knew it, I had a tiny data set of delivery times for US mail. By profession, I develop data visualizations. So, of course I started charting the data.

Patterns started emerging very early:

Distance from San Francisco did not seem to indicate delivery time,

Most US mail arrived in 2–4 days,

Slow mail in the US, was very slow,

Letters to Canada were slower to arrive than most letters within the US.

Over the months, more and more folks signed up from across the US and around the world.

Scatter plot showing that distance mail travels does not determine when it will be delivered in the US.

These charts may not be pretty, but they speak volumes to me. Yes, I love scatter plots! Especially when I start digging into data.

These show me that the distance an envelope travels through the US does not predict when it will arrive. Most arrive in 2–4 days, but some take a lot longer. The international destinations add interesting context. For one of my US recipients, it looks like it took longer than the journey of another envelope of mine to reach Australia. This pattern started to appear very early in the project, but I wasn’t sure if it would continue as the project continued and the data set grew.

In the second scatter plot, a lot of points are right on top of each other, making it tricky to see how many there were. A histogram can help with that. This one shows how many envelopes arrived in each time bracket. Most of my letters were delivered in the US in 2–4 days, with most arriving in three days.

Histogram chart, showing how long it took for my envelopes to arrive at their destinations.

While the histogram is interesting, the most common delivery time dominates the chart. It doesn’t reveal as much about our outliers…the envelopes that arrived later than the expected time of 2–4 days. To me, these are more visible in a less conventional dual axis chart with curved links.

In this chart, each line represents an envelope I mailed from San Francisco to another address in the US. The more orange the line, the longer it took that envelope to arrive.

Well, it sure does look like distance alone does not determine how long it will take for a letter to travel from San Francisco to another location in the United States. This is not surprising, given the interference with the USPS this year.

But can we see traces of this interference in our project? Can we bring personal experience back into the story? Where did I send my letters? How long did it take to get there? Time to plot some data on a map…