17 Nov 2019 16:32 GMT
As I mentioned in my last post, I have been playing with uncertainty charts again. In that post, I wanted to simplify the task of creating animated bar charts so that I could easily create uncertainty bar charts with positive and negative values.
More recently, I have been exploring how the idea of animated uncertainty could be extended to other chart types. The following two experiments were inspired by suggestions from Paul Bolton and Harvey Goldstein.
Both examples are based on the same fundamental idea behind the uncertainty bar chart, which is to illustrate the statistical uncertainty in a set of estimates by generating alternative but equally plausible data using random values drawn from the error distribution for each estimate.
In these examples, I use estimates of the number of EU nationals living in the London Borough of Haringey, which are taken from the Annual Population Survey. These figures are published by the ONS in their regular statistical release on the population of the UK by country of birth and nationality.
Haringey is the borough where I live. There has been an increase in the number of EU nationals living in Haringey since 2004, but the precise extent of the increase is uncertain due to sampling error in the APS. Coincidentally, I was also a migrant to Haringey during this period (from Wales).
I have embedded screenshots of the new charts below, but please follow the links, or click the screenshots, to see the live animated versions.
Uncertainty line chart
The uncertainty line chart starts by showing the trend for the estimates as a line. Clicking on the chart generates alternative lines for the same estimates. Each line is drawn in sequence, and then fades gradually over time until it disappears. In this way the chart builds up a constantly evolving representation of the uncertainty in the estimates, showing the range of possible trends.
Uncertainty level chart
The uncertainty level chart takes a slightly different approach. In this chart, each horizontal line represents an estimate of the number of EU nationals living in Haringey in each year.
When you click on the chart, these lines move to newly generated random values, leaving a translucent shadow of the value in each case. Over time these shadows build up to represent the density of the error distribution: the more values drawn in a given region, the darker that region becomes.
Like the animated bar charts, these charts show different trends that are equally likely given the uncertainty in the estimates. But unlike the bar charts, these versions also build up a visual representation of the overall degree of uncertainty.
In some respects, I quite like the epistemological terror that the bar charts can induce. Seeing a trend erased by variation is a useful antidote to the apparent solidity of numbers. But it does mean those charts are missing some important context, which is made explicit in these versions of animated uncertainty.