2013-05-03-Visualization

• Most people, vision is the strongest sense
• Recall improves 55% (10%=>65%) with addition of a picture
• We've talked about the need to understand the data before using algorithms on it. Visualization can speed that process up.

• For gaining intuition, focus on simple visualizations that help you see relationships in the data.
• At this time, labels, titles, etc. not very important. Multiple dimension in multiple windows? Fine!
• We'll discuss, but the context a visualization is going to be used in matters a lot. Don't feel like you have to import every cool infographic into your project
• Clustering, classification, outlier selection can be verified visually, eg. highlighting points. Use it to gut check conclusions, even if you have to drastically reduce dimensionality

• DM bread and butter. Often deal with high dimensionality, so scatter is one of the best ways to visualize
• Wide variety of patterns can be searched

• This data is for body positions over time
• Dimensions are the different angles for different body parts, like hip ankle, knee, over time
• We can see some strong patterns. Maybe we'll need to kernelize them to make them learnable, but we have a good understanding that there are, or are not relationships between the data

• Coordinates intuitively understandable
• Lots of ways to bucket/aggregate
• Dependence on geographical area (eg. when you'd like to depend on human impact instead)

• Box plots, for real data, still carry a lot of data
• Bar charts nice for summarizing, not great for exploring
• Same for pie charts. Pie charts are mostly bad, but can use in particular circumstances

• What are some of the techniques we can use to tie data to a visual representation?
• img: Kevin Lynagh, http://keminglabs.com/talks/

• Position
• Length / Angle
• Area / Volume
• Color: Chroma Luminance

• Hadley Wickham slides, OSCON

• Many other color spaces, probably most familiar with RGB
• HCL is useful because it separates the properties of a color into ones that can be mapped to data
• Hue: nominal, can't compare
• Chroma, Luminosity: numerical / comparable value
• Chroma vs Saturation: chroma perception relative to white, saturation measure of color intensity
  • http://rourkevisualart.com/wordpress/2008/02/22/the-difference-between-chroma-and-saturation/

• http://colorbrewer2.org/
• Type of comparison => type of color difference
• Lots of other practical features

• Line lengths can appear to look smaller when extended instead of right next to each other

Motion silences awareness of color changes from Jordan Suchow on Vimeo.

• We've talked about different aesthetics of showing data, we've talked about data, all that's needed is to bring them together
• Wilkinson, L. (2005), The Grammar of Graphics (2nd ed.). Statistics and Computing, New York: Springer.
• Rigorous way of describing graphics beyond "scatter plot" or "bar chart"

• Plot shows hypothetical sales of ice cream vs temperature
• Geoms: points (actually, ticks are geoms, too)
• Data: sales, temperature (and context: how large is the potential plot size)
• Mapping: sales
• img: http://www.mathsisfun.com/data/scatter-xy-plots.html

• Plot shows fruit popularity
• Geoms: bars (and ticket, text)
• Data:
• Mapping: sales
• img: http://www.mathsisfun.com/data/bar-graphs.html

• Shows travel options from SFO to Ithica, connecting flights, airports, etc.
• More complex, but still expressible via Grammar
• img: http://www.hipmonk.com

• Reading will go a further extension of this, where the geoms are themselves other plots

• No talk on visualization would be complete without mentioning Tufte
• Great examples