How Well Do We Perceive Maps?

Spatial representations can help us perceive geographical phenomena.

The reassurance that information can be reliably perceived is an obligatory condition whenever visual techniques are used for data analysis. So, does it imply that what we see in the visualization — the visual effect, is close to any underlying statistical effect that we’re interested in?

It is an inherent fact that the perceived effect created by the visualization might not match the statistical effect obtained from calculations on the data. Based on the choice of visual encoding, it is possible that a given visualization might exaggerate certain structures over others. Moreover, an increase in complexity makes it even more challenging to identify graphical phenomena.

To illustrate, observe the two scatterplots in the figures below:

Just a quick glance at Figure 1 should be enough to tell you that the scatterplot on the left-hand side is more correlated.

But when you look at Figure 2, you’ll find that it is now much harder to judge purely based on visual characteristics of the graph.

Here, it is the graph on the right (r = 0.35) that is more correlated. While the scatterplot on the left had an almost identical correlation coefficient value (r = 0.3), it is interesting to note that in both figures, the difference in correlation coefficients was exactly the same (In figure 1, r = 1.0 and 0.95).

There are many studies that demonstrate how the perception of statistical effects varies with the intensity of correlation and type of visualization. In such cases, it is easier to compare similar-looking data sets using statistical techniques. However, to measure visual similarity, Wickham et al.’s line-up protocol can be used to provide some…

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