An Evaluation of Visual Search Support in Maps

Area of Science:

• Human-Computer Interaction
• Information Visualization
• Cognitive Psychology

Background:

• Visual search on maps is often inefficient, requiring extensive scanning of details.
• Map elements like cities and streets can be difficult to locate without effective annotation.
• Current map annotation methods may not optimize user search performance.

Purpose of the Study:

• To compare the effectiveness of four map annotation techniques: within-image, grid reference, directional, and miniature.
• To evaluate how different annotations impact visual search efficiency and user strategies.
• To identify the optimal map annotation method for improving map-based visual search.

Main Methods:

• A controlled eye-tracking study was conducted with 30 participants.
• Four map annotation variants were tested: within-image, grid reference, directional, and miniature.
• Task completion times and eye-movement data were analyzed to assess performance and strategy.

Main Results:

• Within-image annotations were significantly outperformed by all other tested methods.
• Miniature annotation resulted in the fastest task completion times.
• Eye-movement analysis revealed distinct visual search strategies for each annotation type.

Conclusions:

• Miniature annotation is the most effective method for enhancing map-based visual search efficiency.
• Annotation design significantly influences user search strategies and performance.
• Future map interfaces should consider miniature annotation for improved usability.