What is a Treemap?
Treemaps are hierarchical representations of data in nested rectangles that use colour gradients to represent the density or intensity of a particular variable.
In a treemap, each rectangle represents a specific data item and the size and colour of the rectangle usually represent different attributes of the data. The hierarchy is displayed by nesting rectangles within larger ones, with each level of the hierarchy represented by a different colour or shading.
Treemaps provide a compact and efficient way to display large amounts of hierarchical information and allow users to compare and identify patterns or trends. They can be created easily using data visualisation software like Tableau.
Facilitating in Tableau
1. Open Tableau Desktop and connect to the data source. In Tableau, a treemap’s structure is defined by its dimensions and each rectangle’s size or colour is defined by its measures.
2. Drag one or more dimensions onto the ‘Label’ card. In this dataset, Product is a hierarchy consisting of Category, Sub-Category, Manufacturer and Product Name.
3. Drag a measure to be analysed onto the ‘Size’ card and ‘Colour card’. The measure that is on the ‘Size’ card and ‘Colour’ card can be different if analysing two different measure variables. A basic tree map will be generated by Tableau. The label can then be edited by changing the text in the ‘Label’ card.
4. Users can add more dimension fields onto the ‘Detail’ card or ‘Label’ card to further segment the nested rectangles. This will break the treemap further into smaller rectangles.
5. Alternatively, they can click on the ‘Show Me’ toolbar and select the tree map option after adding the relevant measure and dimension fields to the worksheet.
Limitations of Treemaps
These maps provide a big-picture overview of data and allow users to identify outliers easily. However, there are a few limitations:
1. Difficulty in comparing values: Treemaps represent data as varying sizes and colours of rectangles, which can make it challenging to accurately compare the values of different elements. The perception of size can be influenced by factors such as colour, proximity and other visual cues.
A good practice to overcome this limitation is to use appropriate colour palettes and avoid overusing colours. Labels can also be added to provide context to the data points.
2. Limited scalability: Treemaps can become cluttered and difficult to interpret when there are many data points. As the number of data points increases, the rectangles can become too small to display labels, leading to decreased usability.
A good practice to overcome this is to avoid adding too many dimensions or categories to the tree map. Be clear about what analysis needs to be shown.
Treemaps provide an effective way to visualise large amounts of data and enable users to easily compare the sizes of different values or categories. They offer a compact and intuitive visualisation method for hierarchical data.
Not sure what heat map you should be using? Check out our Heat Maps 101 post to get caught up!