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Potential of eye-tracking simulation software for analyzing landscape preferences
Uta Schirpke1,2, Erich Tasser2, Alexandros A Lavdas3,4
1Department of Ecology, University of Innsbruck, Innsbruck, Austria.
Plos One
|October 27, 2022
Summary
Eye-tracking simulation software can identify key landscape features but has limitations in explaining broad landscape preferences. Further research should explore specific visual elements for deeper insights.
Area of Science:
- Environmental Psychology
- Landscape Ecology
- Human-Computer Interaction
Background:
- Understanding landscape preferences is crucial for sustainable spatial development and maintaining attractive environments.
- Traditional eye-tracking studies offer valuable insights but are often time-consuming and expensive.
Purpose of the Study:
- To explore the utility of eye-tracking simulation software for analyzing mountain landscapes.
- To identify the type of information obtainable from eye-tracking simulation.
- To assess the contribution of this information to explaining landscape preferences.
Main Methods:
- Utilized 78 panoramic photographs of Central European Alpine landscapes.
- Collected 19 hotspot indicators using Visual Attention Software by 3M (3M-VAS).
- Estimated 18 photo content variables and calculated 12 landscape metrics.
Main Results:
- An average of 3.3 hotspots per photograph were identified, frequently containing trees, buildings, and horizon lines.
- Hotspot indicators improved model explanatory power by 24% in regression analyses.
- The analysis effectively identified important landscape elements and areas.
Conclusions:
- Eye-tracking simulation software can aid in identifying salient landscape features.
- Its current capability to explain landscape preferences across diverse types is limited.
- Future research should focus on specific landscape characteristics for enhanced analysis depth.
