About Heat Maps

What Makes Heat Maps Special?

Heat maps leverage the human visual system to help users gain deeper and faster insights than other visualizations. Users can visually aggregate, determine relevance and detect micro-patterns in their data in ways other visualizations can’t match.

Preattentive Processing

Preattentive processing refers to the ability of the low-level human visual system to rapidly identify properties such as color and size in less than 250 milliseconds. The heat maps in Heat Map Explorer have been designed to take advantage of preattentive processing.

Visual Attributes Represent Data

Visual Attributes Represent DataEach data item is represented on a heat map by a shape.  On tree maps and box maps, the size and color of each box are determined by data for that item. One metric from the data, such as Budget, determines the size, while another metric, such as % Change, determines the color.

On geographic maps, org charts and other diagrams, the shape is determined by the diagram itself and only the color of the shape is mapped from the data.

Visual Aggregation

Visual aggregation occurs when the human visual system averages or sums visual information. Visual aggregation can reinforce the aggregations occurring in the data model, leading to faster insights.

Visual summing occurs when shapes are placed directly next to each other. Heat maps are one of the few visualizations that take advantage of visual summing.

In Figure 1, it’s difficult to determine which is larger, the sum of all four boxes on the left or the box on the right. But once we place the boxes next to each other, they visually aggregate and we can quickly see the difference.


Figure 1

Figure 2

Visual averaging occurs when colors are placed directly next to each other. Heat maps are one of the few visualizations that take advantage of visual averaging.

In the figure on the left, it’s hard to tell what the average value of the A’s is versus the B’s. But once we place the boxes next to each other, they visually aggregate and we can quickly get a sense of the average value.


Visual Relevance

The human visual system gives more importance to objects that are bigger than those that are smaller. Heat maps leverage this fact to help users focus on what is important.

visual-relevanceTraffic lights, along with business intelligence dashboards, have trained us to view the colors green, yellow and red as good, warning and bad.

By combining color and size, heat maps can help users distinguish urgency and importance in their data—ensuring they focus on the most relevant data.

Micro Pattern Detection

By showing individual data items and organizing them into logical groups, box heat maps help users find micro patterns that remain hidden on traditional charts and spreadsheets.


Anomalies get hidden within summary values in traditional bar and pie charts. By showing details in context, heat maps can highlight exceptions not only within the entire data set, but within specific groups and sub-groups.

For instance, in the bar chart on the left, no anomaly is shown. In the heat map in the middle, the anomaly gets lost with the rest of the data set. But the heat map on the right clearly shows a micro-anomaly: one red box in a group of green boxes.


Anomalies represent problems or opportunities. Detecting these at the micro level allows users to see deeper into their data and get more value.

Heat Map Explorer includes special color schemes to highlight anomalies.


Trends also get hidden within summary values of traditional charts. By organizing related items into groups and sub-groups, heat maps can uncover hidden trends.

For instance, in the heat map on the left, the negative changes in performance appear random. But in the heat map on the right with sub-groups applied, a trend in one sub-group immediately appears, pointing to a potential root cause.


Trends can be detected deep within a hierarchy or across a combination of multiple dimensions. Only box heat maps can highlight trends at such a deep level.


Visualizing how value is allocated among a set of items and how each item contributes to the whole is a classic “parts-of-the-whole” or portfolio problem. Pie charts, traditionally used to solve this problem, suffer from readability problems and can only show one level of allocation at a time.

Box heat maps have the ability to show allocations at multiple levels at once.

For instance, in the heat map below, the Technology group dominates about ¾ of the portfolio. Within Technology, Communications Equipment contributes the least to the portfolio. And while Technology has a few sub-groups with multiple stocks each, Services has 10 different sub-groups, with most groups having just one or two stocks.