1. Method Name

Heatmap Analysis (Temperature Map)

2. Alternative Names

Heatmap, Temperature Map

3. Brief Description

A heat map is a data visualization technique that uses colors to represent values in a matrix or image. The intensity of the color (e.g., from cool blue to hot red) corresponds to the magnitude of the value, allowing for the quick and intuitive identification of “hotspots”—areas with high concentration or intensity.

4. Purpose / When to Use

It is used to visualize complex datasets and to quickly identify patterns, clusters, and anomalies. It finds application in many areas:
- Web analytics: Tracking where users click on a page or where the mouse moves most frequently.
- Process analysis: Displaying the frequency of problems in a matrix (e.g., error type vs. production line).
- Financial analysis: Visualizing the performance of stocks in a portfolio.
- Operational analysis: Displaying the utilization of machines or facilities.

5. Procedure / How to Apply It

1. Organize the data into a matrix: Organize the data into a table (matrix) where the rows and columns represent the two different dimensions you want to analyze (e.g., rows = products, columns = months, values = number of units sold).
2. Define a color scale: Choose a color scheme. The most common approach is to use a gradient from cool colors (blue, green) for low values to warm colors (yellow, orange, red) for high values.
3. Apply colors to the cells: Color each cell in the matrix according to its value. The higher the value, the “warmer” the color.
4. Analyze patterns: Look for the following in the heatmap
: - Hotspots: Cells or areas with intense color that require attention.
- Clusters: Groups of similarly colored cells that indicate a connection.
- Trends and patterns: Regular patterns in rows or columns.

6. A Real-World Example

The quality manager wants to determine whether there is a correlation between the type of defect and the production shift. He creates a table where the rows are defect types (scratch, wrong dimension, crack) and the columns are shifts (morning, afternoon, night). He enters the number of occurrences per month into the cells. He converts this table into a heat map. The heat map immediately highlights a deep-red cell at the intersection of “scratch” and “night shift.” This is a clear signal that the problem with scratches is concentrated during the night shift, and that is where the search for the cause should begin.

7. Benefits

- Intuitive and quick interpretation: The human brain processes colors much faster than numbers in a table
.- Effective visualization of big data: It can clearly display thousands of values in a single image.
- Immediate identification of important areas: It helps quickly focus attention where it is most needed
.- Versatility: Applicable in any field where data can be organized into a matrix.

8. Risks / Limits

- Dependence on the color scale: A poorly chosen or unintuitive color scale can lead to misinterpretation.
- Loss of accuracy: A heat map is great for a quick overview, but it does not show exact values. A table is still necessary for detailed analysis
.- Color perception: Approximately 8% of men have some form of color blindness. When designing, it’s a good idea to choose color schemes that are readable for them (e.g., blue-orange instead of red-green).
- Static view: A traditional heatmap shows the state at a single moment in time.

9. Practical Tips

- Use interactive heatmaps: Modern analytics tools (e.g., Tableau, Power BI) allow you to create interactive heatmaps where you can see the exact value when you hover your mouse over a cell.
- Normalize the data: If you’re comparing rows or columns with very different ranges of values, it’s a good idea to normalize the data (e.g., to a scale of 0–1) so that the heatmap is comparable.
- Combine with other charts: Heatmaps are particularly effective when combined with dendrograms (in cluster analysis) or other types of visualizations.