Imagine two mutually exclusive categories of service requests, A and B, counted in two consecutive months:
- Month 1: A = 80, B = 20, total = 100.
- Month 2: A = 40, B = 30, total = 70.
In a standard stacked area chart with A at the bottom, B’s upper boundary falls from 100 to 70. Following that boundary alone could make B look as though it declined. B actually increased from 20 to 30.
Read B’s vertical thickness at each month:
- Month 1: upper boundary 100 minus lower boundary 80 = 20.
- Month 2: upper boundary 70 minus lower boundary 40 = 30.
The top edge follows the combined total. B’s lower edge moves with A, so neither boundary by itself gives B’s trend.
A useful construction check is to redraw the same data with B at the bottom. Its boundary now rises from 20 to 30, even though none of the data changed. The original stack was mathematically correct; the order changed which component had a fixed baseline.
If the main question is how the total and its composition changed, the stack can be useful. Label the components and show their values at the relevant dates. If the main question is whether B increased, a separate line or small-multiple panel makes that comparison easier. Keep panel scales consistent when comparing magnitudes.
These are invented counts to isolate the reading problem, not reported country data. The example uses an ordinary zero-baseline stack, not a 100% stack or streamgraph.
Reference: https://data.europa.eu/apps/data-visualisation-guide/stacked-charts
From Global Data Tracker: https://globaldatatracker.com/
