As I recently tweeted, “Information is no longer a scarce resource – attention is”. While technology, the internet, and the web of things have made amazing things possible, they have also given rise to vast amounts of information that are accessible almost anywhere at anytime. Given this overload of information and often trying to find better way to determine what information is relevant and to improve the effectiveness of consuming it, I’ve recently found myself intrigued by data visualization – taking lots of data, analyzing it , and presenting it in a visual way that conveys a different point of view then the data could alone. As a photographer, the phase “a picture is worth a thousand words” comes to mind. And so to learn a bit more about this area of tinkuring, a few months ago I started reading Visualize This: The FlowingData Guide to Design, Visualization, and Statistics in which Nathan Yah describes his approach to visualization, including tools, approach, design – quite an interesting mash up of disciplines.
Realizing the first step towards visualization if to have data to analyze, I created a Arduino project called RoadLogger to use on a roadtrip to New England last summer with Val. RoadLogger (v1.0) logged the location, speed, altitude, direction, and driver of our car ever second, using a USB GPS antenna and a microSD card to data storage. While I haven’t had time to post info about RoadLogger and finish analyzing the 32,000+ datapoints, I’ve been thinking about what might be interesting way to analyize the data and present it visually. Here’s a quick list of thoughts:
- How many miles did each driver drive?
- What was our average speed per state?
- Who was the faster driver?
- What was the average time from 0 – 60 mph per driver?
Until I get around to doing some more work on this project, check out my first basic visualization “Am I A Foodie” at another one of my blogs Until It’s Done.