Visualizing Travel

Dopplr is a travel social network that helps you find out if your trips are going to coincide with your friends. In addition to alerting you of travel coincidences, Dopplr provides a few basic visualizations of your trips and allows you to export your data in various formats.

Dopplr Travel Data Visualizations

These visualizations are fun to look at and share with your friends, but are they useful? The answer is debatable, but for most of the widgets, the answer is probably no.

Effective visualizations focus on answering questions. You shouldn’t visualize for the sake of visualizing. You shouldn’t take data and dump it onto a graph, chart, map, or table without purpose. One Dopplr widget shows your personal velocity by averaging the distances you’ve traveled over time. The result is an interesting number, and to have your personal velocity compared to a glacier, duck, or butterfly is fun, but what’s the point?

Dopplr should be commended for taking a step into this space. It’s fascinating data and it’s wonderful to see this personal data visualized in fun and interesting ways. But can we take it a step further?

Where Dopplr falls short is in creating visualizations that effectively lead to a better understanding of the data. In order to make people understand, you must first figure out what needs to be understood. What facts can be extracted from this data using visualization? It’s best to formulate this as a fact being an answer to a question. So what are the questions here?

After exporting my personal travel data from Dopplr as a JSON object, I explored some ways of learning more from my data. For example, I was curious how much time I spent at home in Phoenix, Arizona — as a fraction or percent of the year. Since it’s such a simple question and answer, I didn’t bother representing it in a very visual way.

This week, I came up with a more complicated question. With the weekend approaching, I was excited to spend an entire weekend in Phoenix. I felt like I hadn’t have very many full weekends at home. That lead me to want to justify this feeling. How many weekends had I spent at home during the first 3.5 months of 2010? That’s a question that can be answered effectively through visualization.

Visualizing my travel data

Surprisingly, Dopplr doesn’t have a calendar view (or maybe I just don’t know where it is). It looks like you can subscribe to an iCal feed for your trips, but it is likely only your upcoming trips. Displaying my travel travel on a calendar made it easy for me to figure out how much time I’ve spent on the road and how many weekends I’ve spent at home.

The result made me question how I would define a weekend at home.

If a “full” weekend required that my Friday night and early Monday morning not be infringed by travel plans, then I’ve only had one “full” weekend before this weekend (early April). If you don’t include the Monday morning restriction, I had a full weekend in February, too. If you don’t restrict based on Friday night travel, I had two weekends at home in February.

This example was fairly basic and trivial until I added the legend, showing which cities each color on the calendar represented. Using the 1 box = 1 day metaphor already present on the left, the legend was also able to double as a sort of bar chart, expressing the number of days I’ve spent in each city.

It’s worth noting that even though I set out to visualize my geographic distribution, I didn’t jump straight to putting the data on a map. Focus on the questions you are trying to answer, and let that dictate the type of visualization you use.

This entry was posted on Sunday, July 4th, 2010 at 10:56 pm and is filed under data viz on the web, datarealization, philosophy. You can follow any responses to this entry through the RSS 2.0 feed. You can skip to the end and leave a response. Pinging is currently not allowed.

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