Tagged visualization

GPS Tracks

I am building water quality sensors which will capture geolocated data. This was my first test with this technology. This is part of my ongoing research at the Santa Fe Water Rights residency (March-April) and for the American Arts Incubator program in Thailand (May-June).

This GPS data-logging shield from Adafruit arrived yesterday and after a couple of hours of code-wrestling, I was able to capture the latitude and longitude to a CSV data file.

This is me walking from my studio at SFAI to the bedroom. The GPS signal at this range (100m) fluctuates greatly, but I like the odd compositional results. I did the plotting in OpenFrameworks, my tool-of-choice for displaying data that will be later transformed into sculptural results.

The second one is me driving in the car for a distance of about 2km. The tracks are much smoother. If you look closely, you can see where I stopped at the various traffic lights.

Now, GPS tracking alone isn’t super-compelling, and there are many mapping apps that will do this for you. But as soon as I can attach water sensor data to latitude/longitude, then it can transform into something much more interesting as the data will become multi-dimensional.

Data-Visualizing + Tweeting Sentiments

It’s been a busy couple of weeks working on the EquityBot project, which will be ready for the upcoming Impakt Festival. Well, at least some functional prototype in my ongoing research project will be online for public consumption.

The good news is that the Twitter stream is now live. You can follow EquityBot here.

EquityBot now tweets images of data-visualizations on its own and is autonomous. I’m constantly surprised and a bit nervous by its Tweets.

exstasy_sentimentAt the end of last week, I put together a basic data visualization using D3, which is a powerful Javascript data-visualization tool.

Using code from Jim Vallandingham, In just one evening, I created dynamically-generated bubble maps of Twitter sentiments as they arrive EquityBot’s own sentiment analysis engine.

I mapped the colors directly from the Plutchik wheel of emotions, which is why they are still a little wonky like the fact that the emotion of Grief is unreadable. Will be fixed.

I did some screen captures and put them my Facebook and Twitter feed. I soon discovered that people were far more interested in images of the data visualizations than just text describing the emotions.

I was faced with a geeky problem: how to get my Twitterbot to generate images of the data visualizations using D3, a front-end Javascript client? I figured it out eventually, after stepping into a few rabbit holes.

Screen Shot 2014-10-21 at 11.31.09 AM

I ended up using PhantomJS, the Selenium web driver and my own Python management code to solve the problem. There biggest hurdle was getting Google webfonts to render properly. Trust me, you don’t want to know the details.

Screen Shot 2014-10-21 at 11.31.29 AM


But I’m happy with the results. EquityBot will now move to other Tweetable data-visualizations such as its own simulated bank account, stock-correlations and sentiments-stock pairings.