Much in the same way that statistical models often compete with one another for relevancy and efficiency, these two works on paper strive for emotional poignancy in displaying the exact same data set in different fashions. Each composition is derived from 720 data points, ranging from 0-10, which describe my overall emotion state (as subjectively assigned by me), once per minute, for 12 hours.

In the first piece, the data reads from left to right, in 30 rows of 24 columns, representative of the hours in a day, the days in a month. It is meant to allow the data to create its own narrative, as well as an emotional plot line which shifts according to real life occurrences, for me, but may very nearly be an abstract representation of emotion reactions that have taken place in the personal life of the viewer.

The second work is much more contrived and draws from multiple cooperating mathematical principles. Firstly, it splits the data from the initial set into 12 separate hours, instead of a single flow of points from start to finish. Next, the number of similar occurrences (i.e. all of the “4”s) within each hour is totaled. Based on the mathematical principle that any number can be written as the sum of 4 square numbers, each of these totals was split into its 4-square factorizations.

An example, the numbers 7 and 10 can be written:

7 = 4 + 1 + 1 + 1, or 7 = 2^2 + 1^2 + 1^2 + 1^2

10 = 9 + 1 + 0 + 0, or 10 = 3^2 + 1^2 + 0^2 + 0^2

The largeness of these 4 squares dictated the largeness of the shapes represented in the actual work. Reading from top left to bottom right the 12-hour span runs its course a second time.

As the data proceeds throughout the day, some specific values which may not be closely representative of the entire hour’s data have been placed further away from the general streamline of the main set, thus representing a statistical outlier. Finally, as the the day reaches its end, the set seems to deviate from its relatively stagnant correlation and spreads out in a funnel shape, which, in statistics, is descriptive of a property known as heteroscedasticity. Heteroscedasticity implies that although parts of the data seem to show a reliable statistical trend, the set as a whole contains some confounding factor that discredits the sanctity findings. Even with the utmost transparency, we can never obtain full understanding of another person’s mind, or for that matter, our own.

### Like this:

Like Loading...