What are the most effective ways to handle missing data in tree map visualization?

1 Using placeholders

One option is to use placeholders, such as blanks, zeros, or question marks, to indicate that some data is missing or unknown. This can help you preserve the structure and layout of your tree map, but it can also create confusion or ambiguity for the viewers. For example, if you use blanks, the viewers might not notice that some data is missing, or they might interpret it as zero or null. If you use zeros, the viewers might think that the data is actually zero, not missing. If you use question marks, the viewers might wonder why the data is missing or how reliable the rest of the data is.

2 Imputing values

Another option is to impute values, which means to estimate or infer the missing data based on some assumptions or methods. This can help you fill in the gaps and create a more complete and consistent tree map, but it can also introduce errors or biases in your data. For example, if you use the mean, median, or mode to impute values, you might ignore the variability or distribution of the data. If you use regression, clustering, or machine learning to impute values, you might overfit or underfit the data. If you use expert opinion or domain knowledge to impute values, you might rely on subjective or outdated information.

3 Excluding data

A third option is to exclude data, which means to remove or ignore the missing data from your tree map. This can help you avoid misleading or inaccurate representations of your data, but it can also reduce the sample size and the information content of your tree map. For example, if you exclude entire rows or columns that have missing data, you might lose important or relevant data that is not missing. If you exclude specific cells that have missing data, you might create gaps or distortions in your tree map.

4 Explaining data

A final option is to explain data, which means to provide some context or justification for the missing data in your tree map. This can help you communicate the limitations and uncertainties of your data, but it can also distract or overwhelm the viewers with too much information. For example, if you use annotations, labels, or legends to explain data, you might clutter or obscure your tree map. If you use footnotes, references, or sources to explain data, you might divert or interrupt the attention of your viewers.

As you can see, there is no single best way to handle missing data in tree map visualization. You need to consider the nature and extent of the missing data, the purpose and audience of your tree map, and the trade-offs and implications of each option. By doing so, you can create a more effective and trustworthy tree map visualization.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?