The annotations provide some high level insights: different eras have suicide hot spots in different age cohorts. You can hover over the lines to see single years.
But a closer look shows something fascinating: the propensity for suicide follows a cohort. Generation X (people born in the sixties and seventies) began committing suicide in their late teens and continue at a higher rate to this day. Similarly, people born during WWI and the early 1920s have an elevated rate well into their eighties.
This drives me to speculate what is causing this. Did living your teen years in the Great Depression predispose you for suicide? Or perhaps living through or fighting in WWII is the cause? And what is so different about Generation X coming of age in the eighties?
The visual is fascinating. You find yourself looking for other patterns and speculating on causes and effects.
Why is the visual so effective? There are lots of elements we could evaluate, but let’s start by looking at two dimensions: clarity and engagement.
Clarity
Clarity can be measured by how quickly you “get it” (normalized by information density). Or maybe how long it takes for you to make a decision based on it.
When someone shows you a clear visualization, you don’t need an explanation. You look at it for a few moments and you start to draw conclusions. Visuals with more dimensions or more data points may take longer to digest, but a dense, clear visual is still far more efficient than a lengthy explanation from an analyst.
The image above is exceptionally clear – especially when you consider that it is displaying around 3000 data points.
The key driver of clarity is the signal to noise ratio. The creator has chosen the appropriate chart or map or table, she’s showing the right data, the right amount of data, and the right contextual data. There are few if any distractions. There is no distortion. The data-ink ratio is high.
Consider the visualization below:
