In business decision making, knowing when to strike while the iron is hot may require knowing where things are hot and where they are not.
Let's say you are an operations head of a chain of multi-brand retail stores, and you are trying to visualize and identify areas with highest population density or with highest economic activity to open new stores. In this scenario, a heatmap could prove extremely useful. What is a heatmap? A heatmap is a graphical representation of data that uses a system of color-coding to represent different values. In this blog, we will discuss the heatmap visualization feature in the latest version of Oracle Analytics Cloud.
Heatmaps have various applications and can take different forms. For example, heatmaps can be used to visualize variance across, and correlation between, variables in a dataset. They can also be used to provide a visual representation of user clicks on a website. Another good use of a heatmap is to depict the intensity of data at geographical points on a map-oriented visualization. The image below is an example of how a heatmap visualization in Oracle Analytics, which visualizes sales by city, may look.
How to create heatmap-based map visualizations in Oracle Analytics
Heatmaps can be created using point geometry-based map layers. In Oracle Analytics, you can choose this option in the following dialog box to visualize data using a point geometry-based map layer.
Heatmaps in Oracle Analytics can be density-based or metric-based. In a density-based heatmap, color intensity at a point depicts the density or number of points in a region on the map visualization. Density heatmaps can be generated by populating the Category (Geography) grammar field with the geographic column and by leaving the Color grammar field empty. In a metric-based heatmap, color intensity at a point is influenced by the aggregated value of metrics in a region on map visualization. To visualize a metric-based heatmap, populate the Color grammar field with a metric.
Users are provided multiple options to influence and customize how data is visualized on a heatmap. Options include things like what the radius of each point on a heatmap should be; the color spectrum (i.e., points and areas) with the highest density or metric value and what color to use; layer transparency; interpolation; and more. Following is a screenshot that shows all options.
The interpolation option is specific to heatmap visualizations and it influences the way in which points are rendered on a heatmap. For example, if interpolation is set to maximum, points with a maximum value are highlighted more. Similarly for minimum, points with the lowest or minimum value are displayed to draw attention. Below is a heatmap visualization with interpolation set to maximum.
In addition to settings such as cumulative, maximum, and minimum, other options for an interpolation style are average and average (constant).
Depending on the aggregate rule that is set for a metric, Oracle Analytics chooses the interpolation style automatically. For example, in columns where the aggregate rule is set to sum, cumulative would be chosen as the interpolation style automatically. Similarly, metrics for which the aggregation rule is set to maximum, minimum, or average, interpolation would be automatically set to maximum, minimum, or average, respectively. Users can change these auto-assigned interpolation styles later.
Note that since the heatmap feature does not treat individual points as discrete, brushing, selection, and tool tips will not work for points on a heatmap.
To summarize, Oracle Analytics lets users visualize their data through a map visualization, with many options to influence the way data is rendered on a heatmap. Watch our video to learn about the many features of heatmaps in Oracle Analytics.