Differences in Visual Attraction between Historical Garden and Urban Park Walking Scenes
Abstract
:1. Introduction
2. Method
2.1. Study Sites
2.2. Stimulus Materials
2.3. Participants
2.4. Apparatus
2.4.1. Visual Attraction Measurement
2.4.2. Visual Perception Measurement
2.5. Procedure
2.6. Data Analysis
3. Results
3.1. Differences in Visual Attention between HGWSs and UPWSs
3.1.1. Landscape Elements of Visual Attention
3.1.2. Horizontal and Vertical Visual Attention Area
3.2. Differences in Visual Perception between HGWS and UPWS
3.3. Correlation Comparison of Visual Attraction between HGWS and UPWS
3.3.1. Correlation Comparison of Visual Attention Elements
3.3.2. Correlation Comparison of Visual Attention Area
4. Discussion
4.1. Confirming the Differences of Visual Attention between HGWS and UPWS
4.2. Comparison of Related Factors Predicting Visual Attraction between HGWS and UPWS
4.3. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Mean | Standard Deviation | 95% CI Lower Limit | 95% CI Upper Limit | Min | Max | ||
---|---|---|---|---|---|---|---|
Sky | HGWS | 0.57 | 0.87 | 0.20 | 0.95 | 0.00 | 3.27 |
UPWS | 13.53 | 13.77 | 7.57 | 19.48 | 0.00 | 50.21 | |
Rock | HGWS | 29.29 | 22.85 | 19.41 | 39.17 | 0.00 | 67.99 |
UPWS | 0.12 | 0.59 | −0.13 | 0.38 | 0.00 | 2.82 | |
Waterscape | HGWS | 1.41 | 4.33 | −0.46 | 3.28 | 0.00 | 18.35 |
UPWS | 1.96 | 4.73 | −0.09 | 4.00 | 0.00 | 14.57 | |
Tree | HGWS | 25.43 | 9.76 | 21.21 | 29.65 | 8.55 | 49.69 |
UPWS | 34.58 | 16.23 | 27.56 | 41.60 | 0.00 | 60.00 | |
Bush | HGWS | 7.56 | 5.71 | 5.09 | 10.03 | 0.00 | 18.90 |
UPWS | 26.69 | 13.09 | 21.03 | 32.34 | 2.03 | 54.42 | |
Building | HGWS | 18.00 | 18.35 | 10.07 | 25.94 | 0.00 | 53.29 |
UPWS | 0.65 | 0.83 | 0.29 | 1.01 | 0.00 | 2.78 | |
Visually dominant elements | HGWS | 5.34 | 11.33 | 0.44 | 10.24 | 0.00 | 44.20 |
UPWS | 3.84 | 5.53 | 1.45 | 6.24 | 0.00 | 19.77 | |
Road | HGWS | 12.47 | 4.24 | 10.64 | 14.31 | 5.38 | 20.79 |
UPWS | 18.77 | 7.36 | 15.59 | 21.96 | 3.61 | 33.57 |
References
- Andrade, I.E.-J. Construction and deconstruction of the historic garden concept. J. Arch. Urban 2008, 8, 138–144. [Google Scholar]
- de Oliveira Paiva, P.D.; de Brito Sousa, R.; da Silva Cavalcante, S.F.N. Patchwork quilt: A methodology proposed for the study of historic gardens. Urban For. Urban Green. 2021, 62, 127169. [Google Scholar] [CrossRef]
- Meyer-Fong, T. Civil war, revolutionary heritage, and the Chinese Garden. Cross Curr. East Asian Hist. Cult. Rev. 2015, 4, 309–332. [Google Scholar] [CrossRef]
- Connell, J. Managing gardens for visitors in Great Britain: A story of continuity and change. Tour. Manag. 2005, 26, 185–201. [Google Scholar] [CrossRef]
- Pérez-Martínez, G.; Torija, A.J.; Ruiz, D.P. Soundscape assessment of a monumental place: A methodology based on the perception of dominant sounds. Landsc. Urban Plan. 2018, 169, 12–21. [Google Scholar] [CrossRef] [Green Version]
- Liang, H.; Li, W.; Lai, S.; Jiang, W.; Zhu, L.; Zhang, Q. How to survey, model, and measure rockeries in a Chinese classical garden: A case study for Huanxiu Shanzhuang, Suzhou, China. Landsc. Res. 2020, 45, 377–391. [Google Scholar] [CrossRef]
- Liang, H.; Li, W.; Lai, S.; Zhu, L.; Jiang, W.; Zhang, Q. The integration of terrestrial laser scanning and terrestrial and unmanned aerial vehicle digital photogrammetry for the documentation of Chinese classical gardens—A case study of Huanxiu Shanzhuang, Suzhou, China. J. Cult. Herit. 2018, 33, 222–230. [Google Scholar] [CrossRef]
- Li, J.; Zhang, Z.; Jing, F.; Gao, J.; Ma, J.; Shao, G.; Noel, S. An evaluation of urban green space in Shanghai, China, using eye tracking. Urban For. Urban Green. 2020, 56, 126903. [Google Scholar] [CrossRef]
- Dupont, L.; Antrop, M.; Van Eetvelde, V. Does landscape related expertise influence the visual perception of landscape photographs? Implications for participatory landscape planning and management. Landsc. Urban Plan. 2015, 141, 68–77. [Google Scholar] [CrossRef] [Green Version]
- Gobster, P.H.; Ribe, R.G.; Palmer, J.F. Themes and trends in visual assessment research: Introduction to the Landscape and Urban Planning special collection on the visual assessment of landscapes. Landsc. Urban Plan. 2019, 191, 103635. [Google Scholar] [CrossRef]
- Eriksson, L.; Nordlund, A. How is setting preference related to intention to engage in forest recreation activities? Urban For. Urban Green. 2013, 12, 481–489. [Google Scholar] [CrossRef]
- Chiang, Y.-C.; Nasar, J.L.; Ko, C.-C. Influence of visibility and situational threats on forest trail evaluations. Landsc. Urban Plan. 2014, 125, 166–173. [Google Scholar] [CrossRef]
- Al-Akl, N.M.; Karaan, E.N.; Al-Zein, M.S.; Assaad, S. The landscape of urban cemeteries in Beirut: Perceptions and preferences. Urban For. Urban Green. 2018, 33, 66–74. [Google Scholar] [CrossRef]
- Herzog, T.R.; Kropscott, L.S. Legibility, Mystery, and Visual Access as Predictors of Preference and Perceived Danger in Forest Settings without Pathways. Environ. Behav. 2004, 36, 659–677. [Google Scholar] [CrossRef] [Green Version]
- Kuper, R. Restorative potential, fascination, and extent for designed digital landscape models. Urban For. Urban Green. 2017, 28, 118–130. [Google Scholar] [CrossRef]
- Kuper, R. Preference and restorative potential for landscape models that depict diverse arrangements of defoliated, foliated, and evergreen plants. Urban For. Urban Green. 2020, 48, 126570. [Google Scholar] [CrossRef]
- Wang, R.; Zhao, J.; Meitner, M.J.; Hu, Y.; Xu, X. Characteristics of urban green spaces in relation to aesthetic preference and stress recovery. Urban For. Urban Green. 2019, 41, 6–13. [Google Scholar] [CrossRef]
- Wang, Z.; Li, M.; Zhang, X.; Song, L. Modeling the scenic beauty of autumnal tree color at the landscape scale: A case study of Purple Mountain, Nanjing, China. Urban For. Urban Green. 2020, 47, 126526. [Google Scholar] [CrossRef]
- Gundersen, V.S.; Frivold, L.H. Public preferences for forest structures: A review of quantitative surveys from Finland, Norway and Sweden. Urban For. Urban Green. 2008, 7, 241–258. [Google Scholar] [CrossRef]
- Arnberger, A.; Schneider, I.E.; Ebenberger, M.; Eder, R.; Venette, R.C.; Snyder, S.A.; Gobster, P.H.; Choi, A.; Cottrell, S. Emerald ash borer impacts on visual preferences for urban forest recreation settings. Urban For. Urban Green. 2017, 27, 235–245. [Google Scholar] [CrossRef]
- Ribe, R.G. Is Scenic Beauty a Proxy for Acceptable Management? The Influence of Environmental Attitudes on Landscape Perceptions. Environ. Behav. 2002, 34, 757–780. [Google Scholar] [CrossRef]
- Gobster, P.H.; Westphal, L.M. The human dimensions of urban greenways: Planning for recreation and related experiences. Landsc. Urban Plan. 2004, 68, 147–165. [Google Scholar] [CrossRef]
- Appleton, J. The Experience of Landscape; John Wiley and Sons: New York, NY, USA, 1975. [Google Scholar]
- Kaplan, S.; Kaplan, R. Humanscape: Environments for People; Duxbury Press: North Scituate, RI, USA, 1982. [Google Scholar]
- Daniel, T.C.; Boster, R.S. Measuring Landscape Esthetics: The Scenic Beauty Estimation Method; USDA Forest Service: Rocky Mountain Forest and Range Experiment Station: Fort Collins, CO, USA, 1976.
- Ulrich, R.S.; Simons, R.F.; Losito, B.D.; Fiorito, E.; Miles, M.A.; Zelson, M. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 1991, 11, 201–230. [Google Scholar] [CrossRef]
- Kaplan, S. The restorative benefits of nature: Toward an integrative framework. J. Environ. Psychol. 1995, 15, 169–182. [Google Scholar] [CrossRef]
- Dupont, L.; Antrop, M.; Van Eetvelde, V. Eye tracking Analysis in Landscape Perception Research: Influence of Photograph Properties and Landscape Characteristics. Landsc. Res. 2014, 39, 417–432. [Google Scholar] [CrossRef]
- De Lucio, J.V.; Mohamadian, M.; Ruiz, J.P.; Banayas, J.; Bernaldez, F.G. Visual landscape exploration as revealed by eye movement tracking. Landsc. Urban Plan. 1996, 34, 135–142. [Google Scholar] [CrossRef]
- Nordh, H.; Hagerhall, C.M.; Holmqvist, K. Tracking Restorative Components: Patterns in Eye Movements as a Consequence of a Restorative Rating Task. Landsc. Res. 2013, 38, 101–116. [Google Scholar] [CrossRef]
- Amati, M.; Parmehr, E.G.; McCarthy, C.; Sita, J. How eye-catching are natural features when walking through a park? Eye tracking responses to videos of walks. Urban For. Urban Green. 2018, 31, 67–78. [Google Scholar] [CrossRef]
- Elsadek, M.; Sun, M.; Sugiyama, R.; Fujii, E. Cross-cultural comparison of physiological and psychological responses to different garden styles. Urban For. Urban Green. 2019, 38, 74–83. [Google Scholar] [CrossRef]
- Xiang, Y.; Liang, H.; Fang, X.; Chen, Y.; Xu, N.; Hu, M.; Chen, Q.; Mu, S.; Hedblom, M.; Qiu, L.; et al. The comparisons of on-site and off-site applications in surveys on perception of and preference for urban green spaces: Which approach is more reliable? Urban For. Urban Green. 2021, 58, 126961. [Google Scholar] [CrossRef]
- Cottet, M.; Vaudor, L.; Tronchère, H.; Roux-Michollet, D.; Augendre, M.; Brault, V. Using gaze behavior to gain insights into the impacts of naturalness on city dwellers’ perceptions and valuation of a landscape. J. Environ. Psychol. 2018, 60, 9–20. [Google Scholar] [CrossRef]
- Berto, R.; Massaccesi, S.; Pasini, M. Do eye movements measured across high and low fascination photographs differ? Addressing Kaplan’s fascination hypothesis. J. Environ. Psychol. 2008, 28, 185–191. [Google Scholar] [CrossRef]
- Franěk, M.; Petružálek, J.; Šefara, D. Eye movements in viewing urban images and natural images in diverse vegetation periods. Urban For. Urban Green. 2019, 46, 126477. [Google Scholar] [CrossRef]
- Tatler, B.W. The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. J. Vis. 2007, 7, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tseng, P.H.; Carmi, R.; Cameron, I.G.; Munoz, D.P.; Itti, L. Quantifying center bias of observers in free viewing of dynamic natural scenes. J. Vis. 2009, 9, 4. [Google Scholar] [CrossRef] [Green Version]
- Cheng, J. The Craft of Gardens; Yale University Press: London, UK, 1988. [Google Scholar]
- Li, X.; Xia, B.; Lusk, A.; Liu, X.; Lu, N. The Humanmade Paradise: Exploring the Perceived Dimensions and Their Associations with Aesthetic Pleasure for Liu Yuan, a Chinese Classical Garden. Sustainability 2019, 11, 1350. [Google Scholar] [CrossRef] [Green Version]
- Fung, S. The interdisciplinary prospects of reading Yuan ye. Stud. Hist. Gard. Des. Landsc. 1998, 18, 211–231. [Google Scholar] [CrossRef]
- Bureau, S.L. Basic Profile. 2021. Available online: http://ylj.suzhou.gov.cn/szsylj/jbgk2/nav_wztt.shtml (accessed on 28 July 2021).
- Liu, M.; Nijhuis, S. Mapping landscape spaces: Methods for understanding spatial-visual characteristics in landscape design. Environ. Impact Assess. Rev. 2020, 82, 106376. [Google Scholar] [CrossRef]
- Nielsen, A.B.; Heyman, E.; Richnau, G. Liked, disliked and unseen forest attributes: Relation to modes of viewing and cognitive constructs. J. Environ. Manag. 2012, 113, 456–466. [Google Scholar] [CrossRef]
- Lappi, O. Eye Tracking in the Wild: The Good, the Bad and the Ugly. J. Eye Mov. Res. 2015, 8, 1. [Google Scholar] [CrossRef]
- Diedenhofen, B.; Musch, J. Correction: Cocor: A Comprehensive Solution for the Statistical Comparison of Correlations. PLoS ONE 2015, 10, e0131499. [Google Scholar] [CrossRef] [PubMed]
- Goldberg, J.H.; Kotval, X.P. Computer interface evaluation using eye movements: Methods and constructs. Int. J. Ind. Ergon. 1999, 24, 631–645. [Google Scholar] [CrossRef]
- Franěk, M.; Šefara, D.; Petružálek, J.; Cabal, J.; Myška, K. Differences in eye movements while viewing images with various levels of restorativeness. J. Environ. Psychol. 2018, 57, 10–16. [Google Scholar] [CrossRef]
- Mackworth, N.H.; Morandi, A.J. The gaze selects informative details within pictures. Percept. Psychophys. 1967, 2, 547–552. [Google Scholar] [CrossRef] [Green Version]
- Koffka, K. Principles of Gestalt Psychology; Lund Humphries: London, UK, 1935. [Google Scholar]
- Yin, L.; Wang, Z. Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery. Appl. Geogr. 2016, 76, 147–153. [Google Scholar] [CrossRef]
Category | HGWS Example | UPWS Example |
---|---|---|
Sky | Slightly visible, overcast sky | Massively visible, overcast sky |
Plant | Scattered trees, shrubs without trimming | Neat street trees and woods, neatly trimmed shrubs and lawn |
Road | Decorative pave with pebble, tile and stone, steps, winding and narrow road | Asphalt, cement and pave stone, wide path |
Rock | Decorative rockery stacked by Taihu stone | Natural rock |
Waterscape | Natural pond | Artificial waterscape |
Building | Exquisite historical building, such as corridors and pavilions | Simple modern architecture |
VDE | Wooden railing, decorative lantern, stone fence | Light poles, signage, metal railing |
Sky | Tree | Bush | Waterscape | Rock | Road | Building | VDE | ||
---|---|---|---|---|---|---|---|---|---|
HGWS | r | 0.143 ** | −0.118 ** | −0.014 | −0.108 * | −0.285 ** | 0.051 | 0.196 ** | 0.148 ** |
UPWS | r | 0.137 ** | −0.102 * | −0.290 ** | 0.368 ** | 0.535 ** | 0.058 | 0.133 ** | 0.120 ** |
z | 0.129 | 1.145 | 7.160 | −5.251 | −9.268 | −0.185 | −0.938 | 0.490 | |
p | 0.897 | 0.252 | 0.000 ** | 0.000 ** | 0.000 ** | 0.852 | 0.0348 | 0.624 |
Horizontal | Vertical | |||||
---|---|---|---|---|---|---|
Top | Middle | Bottom | Left | Middle | Right | |
Fascination | 0.061 | 0.116 ** | 0.196 ** | 0.079 | 0.160 ** | 0.198 ** |
Coherence | 0.013 | 0.106 * | 0.146 ** | 0.041 | 0.091 | 0.134 ** |
Complexity | 0.014 | 0.154 ** | 0.089 | −0.042 | 0.015 | 0.132 ** |
Legibility | 0.001 | 0.036 | 0.068 | 0.046 | 0.194 ** | 0.029 |
Mystery | −0.005 | 0.126 ** | 0.140 ** | 0.050 | 0.081 | 0.144 ** |
Horizontal | Vertical | |||||
---|---|---|---|---|---|---|
Top | Middle | Bottom | Left | Middle | Right | |
Fascination | −0.108 * | 0.064 | 0.090 | −0.072 | 0.106 * | 0.166 ** |
Coherence | −0.168 ** | 0.094 | 0.079 | −0.136 * | 0.038 | 0.130 * |
Complexity | 0.278 ** | 0.054 | −0.005 | 0.226 ** | 0.068 | −0.048 |
Legibility | −0.316 ** | 0.176 ** | −0.023 | −0.304 ** | 0.228 ** | 0.048 |
Mystery | 0.244 ** | −0.184 ** | 0.012 | 0.242 ** | −0.172 ** | 0.044 |
Horizontal | Vertical | |||||
---|---|---|---|---|---|---|
Top | Middle | Bottom | Left | Middle | Right | |
Fascination | 4.485 ** | 1.387 | 2.866 ** | 4.003 ** | 1.455 | 0.875 |
Coherence | −4.831 ** | −0.321 | −1.796 | −4.706 ** | −1.408 | −0.108 |
Complexity | −7.183 ** | 2.677 ** | 2.493 * | −7.196 ** | −2.198 * | 4.783 ** |
Legibility | 8.683 ** | −3.752 ** | 2.410 * | 9.523 ** | −0.941 | −2.038 * |
Mystery | −6.721 ** | 8.275 ** | 0.752 | −5.208 ** | 6.744 ** | 2.671 ** |
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Li, C.; Huang, X. Differences in Visual Attraction between Historical Garden and Urban Park Walking Scenes. Land 2022, 11, 1766. https://doi.org/10.3390/land11101766
Li C, Huang X. Differences in Visual Attraction between Historical Garden and Urban Park Walking Scenes. Land. 2022; 11(10):1766. https://doi.org/10.3390/land11101766
Chicago/Turabian StyleLi, Chang, and Xiaohui Huang. 2022. "Differences in Visual Attraction between Historical Garden and Urban Park Walking Scenes" Land 11, no. 10: 1766. https://doi.org/10.3390/land11101766