Heat maps are invaluable visual tools that quickly reveal valuable insights from vast datasets, empowering businesses to move forward with a clear understanding of their data. This article explores the diverse applications of heat maps, some of their different forms and their impact on data analysis and communication.
What is a Heat Map?
At the core of a heat map lies its ability to distil intricate datasets into visually impactful representations. By effortlessly highlighting trends, patterns and outliers, heat maps empower decision makers and analysts to quickly detect and understand hidden trends within datasets.
How Do Heat Maps Work?
A heat map represents values for a primary variable of interest on a grid of coloured squares, with two axis variables defining the grid. Similar to a bar chart or histogram, the axis variables are divided into ranges. The colour of each cell in the grid represents the value of the main variable within the corresponding cell range.
Why Businesses Are Using Heat Maps
Heat maps serve as effective tools for visualising relationships between two variables, with each variable plotted on a respective axis. By observing the changes in cell colours across each axis, patterns in the values of one or both variables can be identified.
The variables plotted on the axes can take various forms, including categorical labels or numeric values. In the case of numeric values, data binning is required, much like in a histogram, to create the grid cells where colours corresponding to the main variable of interest will be displayed.
Cell colours can represent diverse metrics, such as frequency counts of points in each bin or summary statistics like mean or median, for a third variable. Conceptually, a heat map can be thought of as a table or matrix, with colour encoding layered on top of the cells. In certain scenarios, non-numeric values can also dictate cell colours, such as general qualitative levels like low, medium, or high.
Choosing the Right Colour Scale
The palette selected can make all the difference. Opting for a colour scale that faithfully represents the data distribution, utilising sequential or diverging colour schemes tailored to the data can make a significant impact in comprehending the visualisation.
Colour plays a pivotal role in this chart type, making it crucial to select a suitable colour palette that complements the data. Typically, a sequential colour ramp is employed, with lighter shades denoting smaller values and darker shades representing larger values, or vice versa. In cases where values possess a meaningful zero point, a diverging colour palette may be preferred.
Users may also consider utilising stepped or discrete colour palettes to display groups or levels in the chosen measure.
Heat Map Best Practices
Limit Colour Usage
Simplicity is key. Avoid overwhelming the heat map with too many colours and maintain a consistent palette for a harmonious and visually striking visualisation.
Provide a Clear Legend
Be the guide through the visualisation by incorporating a well-defined legend. Explain the colour scale and data values to offer meaningful context to the audience.
Employ Proper Data Aggregation
Tailor the data aggregation to the analysis goals. Whether daily, weekly or monthly, the right aggregation level enhances data legibility.
Normalise your data for more accurate comparisons
Ensure equitable comparisons by normalising data, especially when dealing with datasets of varying scales.
Show values in cells
When it comes to mapping colour to value, there is a degree of imprecision, especially when compared to other encodings like position or length. To enhance clarity, it is advisable to include cell value annotations within the heat map – offering a double encoding of the underlying data. This approach can improve the accuracy of data interpretation and reduce potential ambiguity.
Embrace Interactivity
Empower users with interactive heat maps that delve deeper into the data. Encourage engagement and allow stakeholders to explore insights at their own pace.
Sort Dimensions by Value of Similarity
When dealing with categorical axis variables in a plot, explore different orders in which these variables are plotted. If the categories lack an inherent ordering, selecting an arrangement that facilitates better data pattern comprehension becomes essential. A frequently used approach is sorting categories based on their average cell value, arranging them from the largest to the smallest values. This allows the reader to easily discern trends and insights within the data.
Types of Heat Maps Explained
Different types of heat maps can be developed depending on the specific use cases:
Classic Heat Map
The classic heat map stands as the foundation of all heat maps. It depicts data using colours to indicate varying degrees of intensity or concentration. As data values shift, the colours respond accordingly, revealing patterns and trends that might remain hidden in raw data.
Geographic Heat Map
Geographic heat maps marry data with geographical locations, enabling analysts to discern spatial patterns. By superimposing data on maps, these visualisations offer a spatial understanding of trends, such as population density, sales distribution or disease outbreaks.
Website Heat Map
For businesses utilising website analytics, website heat maps are indispensable as they reveal user behaviour on web pages. ‘Click’ maps show the most engaging elements and ‘scroll’ maps indicating users’ scrolling behaviour.
Tree Maps
Tree maps are hierarchical and display data in nested rectangles. These visualisations are particularly useful for showcasing data relationships in intricate hierarchies, with colours and sizes representing data values.
Choropleth Map
Choropleth maps excel in showcasing data variation across geographical regions. Colours are assigned to specific areas, representing data values, enabling easy comparisons between regions.
Calendar Map
Calendar maps present data patterns over time, with each day or month coloured to represent data values. These visualisations offer a powerful way to identify trends and seasonal variations.
Summary
Heat maps have proven to be invaluable perspectives in the realm of data visualisation, offering a diverse array of insights across various industries and domains. From the classic heat map highlighting intricate data distributions to the geographic heat map revealing spatial trends, each type possesses distinct potential to elevate decision-making and communication. These visualisations empower users to navigate through complex datasets seamlessly, facilitating a deeper understanding of crucial patterns and relationships. With precision, clarity, and impact, allow your data to narrate its story, laying the foundations for data-driven insights.
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