Elements that work in this chart
✔ Providing a context
The biggest advantage of the chart is putting information about spending in the context of the country’s GDP. This allows a critical approach to the amount of support provided. It sheds new light on the enormity of the gulf between the United States and different countries. Also, as a Polish person, I feel better knowing that the relatively small contribution by the Polish government is a significant percentage of our GDP.
✔ Sorting
The second solution that supports the analysis is selected sorting. There is a separation between the particular countries/institutions that contributed significantly and other countries. This helps focus on the biggest supporters while putting their contributions into the proper context. We can easily see that each listed country contributed more than six other non-European countries combined. Also, because the conflict is located in Europe, the distinction between European and non-European countries makes the most sense.
Elements that don’t work in this chart
✗ Making comparison difficult
The presence of the outliers and the differences in categories’ sizes makes the chart Illegible. Those disproportions flatten categories and harden the comparison. Additionally, although the charts are placed next to each other and share the same legend, they are hard to compare. The data differ in terms of the time range and the included countries. The left contains Feb 22 — Dec 22 data with no country limitation, while the right one contains Jan 22 — Jan 23 data with a limited list of non-European countries.
✗ Lack of the main message
Somehow, even though the chart provides a lot of information, it leaves us with “so what” thinking. There is no clear message, and due to the flattened categories, we can’t process the additional details.
Step-by-step improvements
✎Focus on what you want to show
Let’s start with the most crucial thing and decide what we want to show. Showing everything at once might be overwhelming and not very informative. Separating the charts allows us to focus on one aspect of the data and leaves us more space for presenting details. This way, we can make the most of the contributors’ list.
✎Switch to the panel bar
After removing the left chart, we end up with more space and can address the distortion caused by the outliers. Because the humanitarian aid is significantly smaller than the two other categories, therefore, it’s barely visible on the stacked bar chart. For better comparison, we should switch to the panel bar. This way, we can focus on total aid per country and offer the split by type. With the common baseline, the cross-country comparison is much easier.
✎Clean up the layout
We can also remove some unnecessary elements. In the new setup, the header can easily replace the legend. It will reduce the need for scanning and will place the information closer to the data. Another step is standardizing the headers by moving the date range to the subtitle.
✎Add the context
Since the chart is about the different aid types, we can offer a summary. This way, we can see that military aid wasn’t the biggest category (at least according to the reverse engineering I did when assessing the total values), and we should reorder. And lastly, because of the still flat data, we can remove the scale and replace it with the actual numbers.