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Data Art

Week 1: Hemlock Tree Visualizations

This week I used a Hemlock dataset to experiment with various visualizations. I used the year and the raw ring width to build my visuals.

Figure 1 creates an animated visual.  As you watch, a new circle is drawn. Each circle represents a new year.  The size of the circle correlates to the raw ring width value measured in that year.  In the end you can see the ring widths that are more common over the year since those end up being the darker areas.  The lighter areas are the outliers.

Figure 1: Tree Ring Animated Visualization

I modified this sketch just a bit to create the next visualization. Figure 2 shows the circles in a linear path.  Placing the circles in a timeline allows you to see the changes of tree ring widths over time.  

Figure 2: Tree Ring Timeline Visualization

Figure 3 is slightly modified so that each year is represented by a line instead of a circle.  The length of the lines correlates to the ring width in each respective year. This too provides a nice way to visual the trend over time.

Figure 3: Line Timeline Visualization

In the next visualization (Figure 4) I added an element of interactivity. As you scroll over the timeline, the corresponding year comes to the front of the screen.  The height of the rectangle correlates to the ring width for that respective year. The rectangles fade but to not fully so that you can still see the trend over the years. 

Figure 4: Interactive Timeline Visualization

Figure 5 uses grayscale to represent the ring width.  Black is the largest width while white is the smallest width.

Figure 5: Interactive Grayscale Visualization

The final visualization is another adaptation of the timeline animation.  This time I experimented with the arc.  The largest ring width is represented by an arc that goes from 0° degrees to 180°.  Smaller arcs mean smaller ring widths.  The inner arcs are the earlier years while the outer arcs are the later years. 

Figure 6: Arc Timescale Visualization

From this exercise, I learned that the way you represent data can really dictate and inform the way you understand data.  In this case, if you were interested in the trend over time, Figure 3 might be the most straight forward visualization.  However if you are more interested in the outliers and what sizes are more likely to occur, Figure 1 may be a better visualization.  It is also important to consider the shapes that you are using to create your visualization because shapes themselves can sometimes misrepresent data.  Figure 6, for example, does not do the best job of representing this particular data.  The length of the arc is what represents the ring width, not the radius.  However it is easy to perceive the arc with the largest radius as the year that had the largest ring width. Overall, this exploration was a fun way to think about data and how to represent it.

Eva Philips