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The Relationships between Perceived Design Intensity, Preference, Restorativeness and Eye Movements in Designed Urban Green Space

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  • Yu Wu

    (College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou 350000, China)

  • Zhixiong Zhuo

    (Xiamen University Tan Kah Kee College, Zhangzhou 363105, China)

  • Qunyue Liu

    (College of Architecture and Urban Planning, Fujian University of Technology, Fuzhou 350000, China)

  • Kunyong Yu

    (College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350000, China)

  • Qitang Huang

    (College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou 350000, China)

  • Jian Liu

    (College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou 350000, China)

Abstract

Recent research has demonstrated that landscape design intensity impacts individuals’ landscape preferences, which may influence their eye movement. Due to the close relationship between restorativeness and landscape preference, we further explore the relationships between design intensity, preference, restorativeness and eye movements. Specifically, using manipulated images as stimuli for 200 students as participants, the effect of urban green space (UGS) design intensity on landscapes’ preference, restorativeness, and eye movement was examined. The results demonstrate that landscape design intensity could contribute to preference and restorativeness and that there is a significant positive relationship between design intensity and eye-tracking metrics, including dwell time percent, fixation percent, fixation count, and visited ranking. Additionally, preference was positively related to restorativeness, dwell time percent, fixation percent, and fixation count, and there is a significant positive relationship between restorativeness and fixation percent. We obtained the most feasible regression equations between design intensity and preference, restorativeness, and eye movement. These results provide a set of guidelines for improving UGS design to achieve its greatest restorative potential and shed new light on the use of eye-tracking technology in landscape perception studies.

Suggested Citation

  • Yu Wu & Zhixiong Zhuo & Qunyue Liu & Kunyong Yu & Qitang Huang & Jian Liu, 2021. "The Relationships between Perceived Design Intensity, Preference, Restorativeness and Eye Movements in Designed Urban Green Space," IJERPH, MDPI, vol. 18(20), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10944-:d:658925
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    References listed on IDEAS

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    1. Robert B. Noland & Marc D. Weiner & Dong Gao & Michael P. Cook & Anton Nelessen, 2017. "Eye-tracking technology, visual preference surveys, and urban design: preliminary evidence of an effective methodology," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Taylor & Francis Journals, vol. 10(1), pages 98-110, January.
    2. Helena Nordh & Caroline M. Hagerhall & Kenneth Holmqvist, 2013. "Tracking Restorative Components: Patterns in Eye Movements as a Consequence of a Restorative Rating Task," Landscape Research, Taylor & Francis Journals, vol. 38(1), pages 101-116, February.
    3. Youngeun Kang & Eujin Julia Kim, 2019. "Differences of Restorative Effects While Viewing Urban Landscapes and Green Landscapes," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    4. Lien Dupont & Marc Antrop & Veerle Van Eetvelde, 2014. "Eye-tracking Analysis in Landscape Perception Research: Influence of Photograph Properties and Landscape Characteristics," Landscape Research, Taylor & Francis Journals, vol. 39(4), pages 417-432, August.
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    Cited by:

    1. Ruochen Ma & Yuxin Luo & Katsunori Furuya, 2023. "Gender Differences and Optimizing Women’s Experiences: An Exploratory Study of Visual Behavior While Viewing Urban Park Landscapes in Tokyo, Japan," Sustainability, MDPI, vol. 15(5), pages 1-14, February.
    2. Tingting Su & Kaiping Wang & Shuangshuang Li & Xinyan Wang & Huan Li & Huanru Ding & Yanfei Chen & Chenhui Liu & Min Liu & Yunlu Zhang, 2022. "Analysis and Optimization of Landscape Preference Characteristics of Rural Public Space Based on Eye-Tracking Technology: The Case of Huangshandian Village, China," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    3. Xinhui Fei & Yanqin Zhang & Deyi Kong & Qitang Huang & Minhua Wang & Jianwen Dong, 2023. "Quantitative Model Study of the Psychological Recovery Benefit of Landscape Environment Based on Eye Movement Tracking Technology," Sustainability, MDPI, vol. 15(14), pages 1-19, July.

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