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Since the 1960s researchers have developed a range of techniques for evaluating landscape preference. In parallel with this trend, eye-tracking technology has become cheaper, more mobile and more accurate, heralding a new era of big data capture and analysis for landscape preference. In this project our objective was to capitalise on the increasing mobility, sophistication and cheapness of eye-tracking technology to examine its utility in analysing landscape preference. In the following we describe how we eye-tracked 35 participants as they viewed walks through two different parks in the urban center of Melbourne, Australia. We show how participants dwelt on trees and bushes more than other objects. When we compared this to the time and space that objects occupy, participants overwhelmingly dwelt on artificial objects such as lamp-posts, distant buildings and benches. Overall we provide an exploration and method for analysing eye-tracking data in parks by normalising the dwell time by the content, providing a robust means of comparing different dynamic stimuli such as videos.


School of Science

Document Type

Open Access Journal Article

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Open Access

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Sunday, March 01, 2020