AI Topic Competitiveness Heatmap

The AI topic competitiveness heatmap converts subject-topic relationships into a matrix. Rows usually represent topics, columns represent subjects, and color intensity represents the relative competitive strength or activity level.

Recommended Data Preparation

Complete AI topic identification.
Merge institution, author, or other subject names before comparison.
Remove obvious noise and duplicate aliases.
Decide the subject dimension and topic range before generating the matrix.

Generate The Heatmap

Export or calculate the AI topic competitiveness index.
Open the AI topic competitiveness heatmap or terrain view.
Choose the subject dimension.
Set topic and subject limits.
Generate the matrix and adjust display options such as color, labels, background, and scale.

Reading The Heatmap

High-intensity cells show where a subject is highly active or competitive in a topic. A row with many strong cells means the topic is widely contested. A column with several strong cells means the subject has a broad topic layout.

The terrain view can help communicate concentration and high-value areas, but the heatmap is usually easier for precise comparison. Use exported Excel data when a numeric conclusion is needed.

Review Rules

Check whether the subject names were merged correctly.
Avoid comparing topics with very different document volumes without reading supporting counts.
Use selected-paper mode only when the selection rule is clear and recorded.
Keep the exported matrix together with the figure for evidence tracking.