Here’s a screen-cast of my final project for Data Representation. It’s an iPad app called Cupping Data, and it visualizes the cupping scores of over 700 specialty coffees.

A huge thanks goes out to Sweet Maria’s for granting me access to their data and allowing me to publish the app. Also, their coffee is damn good.

You can download Cupping Data for free from the AppStore.

This week in Data Representation we’re looking at different ways that data can lie. So as any good data-scientist would, we’re immersing ourselves in the dark art of lying with numbers. For this assignment I’m looking at the US Government’s Fuel Economy Data.  These graphs are all looking at the same data: miles per gallon of cars available in a given year. The first graph paints a negative trend by selecting a subset of the data and looking at only the extreme cases. The second graph paints a positive picture by looking at the inverse subset (but also only the extreme points). The third graph is a “non-lying” depiction which shows the average of all of the data points for the entire data set.

This week in Data Rep we’re using the Beads library to generate audio based on a data set. Here are a few sketches that I created:

Image Sonifiers

This image sonifier sketch reads the pixel value from an image and converts those to tones. The intensity of the red, green and blue blocks correspond to the pixel that it’s currently reading. Clicking within the image allows you to jump around. You can download the sketch here.

Rainbow

NOTE: YouTube audio is out-of-sync with the frames.

 

Image Sonifier: Black and White

NOTE: YouTube audio is out-of-sync with the frames.

 

Tweet Sonifier

This sketch plays the tweets from my personal archive. A tone is generated for each letter that’s on-screen, and the pitch is mapped to the characters’ ASCII code (multiplied for range).

NOTE: YouTube audio is out-of-sync with the frames.

Since I started drinking coffee—and later wine—I’ve been interested in the language around describing flavors, and more specifically, how we remember flavors. It takes a long time to build up a palette memory especially when you’re dealing with differences that are as nuanced as coffee. Despite being a barista (back in the day) and roasting my own beans for the past few years, I still struggle to recall anything more than the most general characteristics of coffees from growing regions around the world.

The Question

Is it possible to use data to construct a memorable, visual “signature” of a coffee growing region? In other words, can I create a tool that generates images for specific countries, acting as mnemonic devices, with which I can backtrace the region’s coffee characteristics?

The Data

Sweet Maria’s is the company who I buy my green beans from and they have an extensive library of cupping scores from coffee growers around the world. I’ll be using these data to generate composites for the world’s most popular coffee growing countries. Here’s an example of the “spider graphs” that seem pretty standard in the industry:

 

The Medium

I plan on creating an iPad app that lets you explore the various signatures and allows you to generate your own combination by manipulating the data.

This week in Data Representation we looked at the Open Paths data that we’ve been collecting since the beginning of the semester. This video is a Processing sketch, and the map is generated with Unfolding Maps. The library allows you to use custom map tiles designed in TileMill or CloudMade.

The mobile photos that appear above the timeline were taken during the same period. I wanted to incorporate an additional dataset to provide a little context. There’s a brief time after I shoot up to LaGuardia Airport that I was in MN for Trivia Weekend.

For this week’s Data Representation assignment, we were asked to select a dataset from the Guardian Data Store and represent it in two ways. The first is the Tufte way, which focuses on simplicity and clarity. The other is to look at the unique character of the dataset, and try to represent it in a way that only applies to our data.

I’ve chosen the Close Earth Encounters dataset, which looks at asteroid flybys circa 2011.

For the first representation, I’ve done a straight-forward chart with distance on one axis and size on the other:

 

For the character representation, I’m plotting the asteroids in a faux-orbital path around the earth to represent the distance in relation to other satellites (e.g. The Moon, the Space Shuttle and Mars), and also indicate the scale of the system. The length of the tail corresponds to the velocity of the asteroid, and the brightness of the tail maps to the diameter of the object. I’ve also included data from the recent DA14 flyby, as a benchmark that people may be familiar with.

Move your mouse along the Y axis to change the scale.