What can users identify using tree maps beyond patterns in data?

Tree maps are a powerful visualization tool that not only reveal patterns in data but also allow users to identify data outliers and problem areas. This capability stems from their design, which represents hierarchical data using nested rectangles. Each rectangle's size is proportional to a particular value, making it visually intuitive to quickly spot anomalies that deviate significantly from other data points.

By examining the dimensions and placements of the rectangles within the tree map, users can highlight areas where the data behaves abnormally, prompting further investigation into those outliers. This feature is particularly useful in identifying inefficiencies or issues within datasets, enabling data-driven decision-making.

In contrast, while tree maps provide some statistical insights through the size of the segments, they do not explicitly convey overall data statistics, average values, or specific linkages between data points. Instead, their primary strength lies in visualizing magnitude and identifying significant variations within the data.