Research on Landscape Perception and Visual Attributes Based on Social Media Data—A Case Study on Wuhan University
Source abstract
Abstract
With the rapid rise of social media, the photo-taking behavior of tourists and their uploaded photos provide a new perspective to explore landscape visual characters. In this study, we provide methodological advancements for assessing landscape visual quality based on content analysis of user-generated photographs. The purpose is to demonstrate an empirical method for evaluating visual indicators reflected in photographs through a case study application. This research takes the core cultural landscape area of Wuhan University as the research scope. The photographs shared on a famous Chinese social media platform Sina Weibo during the Cherry Blossom Festival, together with tourists’ trajectory data, were used as data sources. Based on a fixed-point photography experiment, the spatial relationship between the scenic spot and the observation point was illustrated. Utilizing a semi-automatic photo content analysis founded on computer vision technology, landscape visual attributes of each attraction were studied thoroughly regarding complexity, visual scale, and color. The results indicate that the Old Dormitory is the most popular scenic spot with diverse viewing angles, strikingly vivid colors, and rich color combinations. Complexity and color play key roles in landscape visual quality, while the depth of view has a subtle impact, which suggests the depth-to-height ratio of less than 1 is the best distance for viewers to take photographs. In all, the mapping relationship between landscape visual attributes and viewers’ perception was revealed in the present work.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Land Use and Ecosystem Services · Urban Green Space and Health · Diverse Aspects of Tourism Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Urban Design, Wuhan University, Wuhan 430072, China
China Machinery International Engineering Design & Research Institute Co., Ltd., Changsha 410007, China
Funding
National Natural Science Foundation of China
References (81)
Show source-provided references
1. Council of Europe (2022, May 28). European Landscape Convention and Reference Documents. [Online] Rm.coe.int. Available online: https://rm.coe.int/european-landscape-convention-book-text-feb-2008-en/16802f80c6.
2. Capturing Landscape Visual Character Using Indicators: Touching Base with Landscape Aesthetic Theory10.1080/01426390701773854 ↗
3. Landscape character assessment with GIS using map-based indicators and photographs in the relationship between landscape and roads10.1016/j.jenvman.2016.05.044 ↗
4. Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR10.1016/j.landurbplan.2017.10.010 ↗
5. Zube, E.H., Brush, R.O., and Fabos, J.G. (1975). Application of a landscape-preference model to land management. Landscape Assessment: Values, Perceptions and Resources, National Agricultural Library.
6. Zube, E.H. (1984). Environmental Evaluation: Perception and Public Policy, Cambridge University Press.
7. Rating everyday rural landscapes of the northeastern US
8. Appleton, J. (1996). The Experience of Landscape, Wiley.
9. Litton, R. (1973). Landscape Control Points: A Procedure for Predicting and Monitoring Visual Impacts, Pacific Southwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture.
10. Litton, R. (1984). Visual Vulnerability of the Landscape, U.S. Department of Agriculture, Forest Service.
11. Altman, I., and Wohlwill, J.F. (1976). Environmental Aesthetics: The Environment as a Source of Affect. Human Behavior and Environment, Springer.10.1007/978-1-4684-2550-5 ↗
12. Dominant attributes in the perception and evaluation of the Dutch landscape10.1016/0169-2046(95)00204-9 ↗
13. Key concepts in a framework for analysing visual landscape character10.1080/01426390600783269 ↗
14. The ecology of visual landscapes: Exploring the conceptual common ground of visual and ecological landscape indicators10.1016/j.ecolind.2008.11.008 ↗
15. Wascher, D.M. (2005). European Landscape Character Areas—Typologies, Cartography and Indicators for the Assessment of Sustainable Landscapes. Landscape Europe, Wageningen Environmental Research. Final Project Report as deliverable from the EU’s Accompanying Measure project European Landscape Character Assessment Initiative (ELCAI), funded under the 5th Framework Programme on Energy, Environment and Sustainable Development (4.2.2).
16. A contribution to the assessment of scenic quality of landscapes based on preferences expressed by the public10.1016/j.landusepol.2009.02.007 ↗
17. Landscape Evaluation: A comparative study
18. Ascertaining Landscape Perceptions and Preferences with Pair-wise Photographs: Planning rural tourism in Extremadura, Spain10.1080/01426390220149539 ↗
19. Landscape evaluaton: A research project in East Sussex10.1080/09595236800185041 ↗
20. Development and validation of a multicriteria indicator for the assessment of objective aesthetic impact of wind farms10.1016/j.rser.2007.05.002 ↗
21. Assessing the visual quality of rural landscapes10.1016/j.landurbplan.2003.10.029 ↗
22. Landscape preference and personality10.1016/0169-2046(86)90004-6 ↗
23. Visual landscape quality in landscape planning: Examples of Kars and Ardahan cities in Turkey
24. Daniel Terry, C., and Boster Ron, S. (1976). Measuring Landscape Esthetics: The Scenic Beauty Estimation Method, Department of Agriculture, Forest Service, Rocky Mountain Range and Experiment Station. Res. Pap. RM-RP-167. U.S.
25. Swanwick, C. (2002). Landscape Character Assessment: Guidance for England and Scotland, The Countryside Agency, Scottish Natural Heritage.
26. Bureau of Land Management (2022, May 28). Manual 8400—Visual Resource Management. Washington, DC, Available online: https://www.blm.gov/sites/blm.gov/files/uploads/mediacenter_blmpolicymanual8400.pdf.
27. Dramstad, W., and Sogge, C. (2002, January 7–9). Agricultural impacts on landscapes: Developing indicators for policy analysis. Proceedings of the NIJOS/OECD Expert Meeting on Agricultural Landscape Indicators, Oslo, Norway.
28. Badach, J., and Raszeja, E. (2019). Developing a Framework for the Implementation of Landscape and Greenspace Indicators in Sustainable Urban Planning. Waterfront Landscape Management: Case Studies in Gdańsk, Poznań and Bristol. Sustainability, 11.10.3390/su11082291 ↗
29. Elements, characteristics and character—Information functions of landscapes in terms of indicators10.1016/j.ecolind.2005.08.009 ↗
30. A simplified model to assess landscape quality from rural roads in Spain10.1016/j.agee.2011.05.007 ↗
31. Perception of rural landscapes in Flanders: Looking beyond aesthetics10.1016/j.landurbplan.2007.02.006 ↗
32. Cognitive attributes and aesthetic preferences in assessment and differentiation of landscapes10.1016/j.jenvman.2007.10.016 ↗
33. Wherrett, J.R. (1998). Natural Landscape Scenic Preference: Techniques for Evaluation and Simulation. [Ph.D. Thesis, Robert Gordon University]. Available online: http://hdl.handle.net/10059/531.
34. Presentation mode and question format artifacts in visual assessment research10.1016/0169-2046(87)90032-6 ↗
35. Rating reliability and representation validity in scenic landscape assessments10.1016/S0169-2046(01)00133-5 ↗
36. Advantages of using different data sources in assessment of landscape change and its effect on visual scale
37. Landscape evaluation: Com-parison of evaluation methods in a region of Spain10.1016/j.jenvman.2006.09.018 ↗
38. Indicators of landscape fragmentation: The case for combining ecological indices and the perceptive approach10.1016/j.ecolind.2011.08.016 ↗
39. Evaluating visible spatial diversity in the landscape10.1016/S0169-2046(98)00077-2 ↗
40. Crowdsourcing geospatial data
41. Positional accuracy analysis of Flickr and Panoramio images for selected world regions10.1080/14498596.2013.801331 ↗
42. Casalegno, S., Inger, R., DeSilvey, C., and Gaston, K.J. (2013). Spatial Covariance between Aesthetic Value & Other Ecosystem Services. PLoS ONE, 8.10.1371/journal.pone.0068437 ↗
43. Revealing Cultural Ecosystem Services through Instagram Images: The Potential of Social Media Volunteered Geographic Information for Urban Green Infrastructure Planning and Governance10.17645/up.v1i2.609 ↗
44. Discovering the tourists’ behaviors and perceptions in a tourism destination by analyzing photos’ visual content with a computer deep learning model: The case of Beijing10.1016/j.tourman.2019.07.002 ↗
45. Travel photography: A methodological approach10.1016/0160-7383(88)90076-X ↗
46. Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites
47. Aesthetic appreciation of the cultural landscape through social media: An analysis of revealed preference in the Dutch river landscape10.1016/j.landurbplan.2018.05.002 ↗
48. Evaluation of urban landscape visual quality based on social media trends in Bogor City10.1088/1755-1315/622/1/012022 ↗
49. Xiao, X., Fang, C., and Lin, H. (2020). Characterizing Tourism Destination Image Using Photos’ Visual Content’. ISPRS Int. J. Geo-Inf., 9.10.3390/ijgi9120730 ↗
50. Visualizing the perceived environment using crowdsourced photo geodata10.1016/j.landurbplan.2015.02.022 ↗
51. Spatial assessment of aesthetic services in a complex mountain region: Combining visual landscape properties with crowdsourced geographic information10.1007/s10980-017-0498-7 ↗
52. Analysis of Entrance Space of Wuhan University Based on Behavior Trajectory
53. Ti, L. (2022). Research on Hot Search Agenda Construction of Sina Weibo, Xi’an Technological University. Postgraduate.
54. Analysis of the exterior colour of agroindustrial buildings: A computer aided approach to landscape integration10.1016/S0301-4797(03)00121-X ↗
55. Ashihara, Y. (1981). Exterior Design in Architecture, Van Nostrand Reinhold.
56. Gehl, J. (2010). Cities for People, Island Press. Available online: http://site.ebrary.com/id/10437880.
57. The Specification of a Non-Linear Psychophysical Function for Visual Landscape Dimensions10.1080/00222216.1980.11969449 ↗
58. Kellert, S. (1996). The Value of Life: Biological Diversity and Human Society, Island Press [for] Shearwater Books.
59. Kaplan, R., and Kaplan, S. (1989). The Experience of Nature: A Psychological Perspective, Cambridge University Press.
60. Landscape Ecology: A Hierarchical Perspective Can Help Scientists Understand Spatial Patterns10.2307/1310366 ↗
61. Forman, R.T.T., and Godron, M. (1986). Landscape Ecology, Wiley.
62. Population Responses to Patchy Environments10.1146/annurev.es.07.110176.000501 ↗
63. Sitte, C., and Stewart, C.T. (1945). The Art of Building Cities: City Building According to Its Artistic Fundamentals, Reinhold Pub. Corp.
64. McGarigal, K. (1995). FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Gen. Tech. Rep. PNW-GTR-351.10.2737/PNW-GTR-351 ↗
65. Visual landscape preference: A model and application
66. Human responses to vegetation and landscapes10.1016/0169-2046(86)90005-8 ↗
67. Mystery in an Information Processing Model of Landscape Preference10.3368/lj.4.2.87 ↗
68. Distance and Scenic Beauty: A Nonmonotonic Relationship10.1177/0013916583151004 ↗
69. Parramón, J.M. (1988). Teoría y Práctica del Color (Theory and Practice of the Colour), Parramón Ediciones.
70. Bureau of Land Management (2022, June 08). Manual H-8410-1—Visual Resource Inventory. [online] Blm.gov, Available online: https://www.blm.gov/sites/blm.gov/files/uploads/Media_Library_BLM_Policy_H-8410.pdf.
71. Assessing the visual quality of green landscaping in rural residential areas: The case of Changzhou, China10.1007/s10661-011-2012-z ↗
72. Correlations between aesthetic preferences of river and landscape characters10.3846/16486897.2012.695738 ↗
73. Español, I. (1995). Impacto Ambiental (Environmental Impact), E.T.S.I. Caminos, Canales y Puertos.
74. Indicators of perceived naturalness as drivers of landscape preference10.1016/j.jenvman.2007.10.013 ↗
75. The effect of landscape colour, complexity and preference on viewing behaviour10.1080/01426397.2019.1593336 ↗
76. Kaplan, A., and Hepcan, Ç.C. (2004). Ege Üniversitesi Kampüsü &lsquo. Sevgi Yolu’nun Görsel (Etki) Değerlendirme Çalışması (A Study on Visual Impact Assessment of ‘Sevgi Yolu’ in Ege University Campus), Ege Üniversitesi Ziraat Fakültesi Dergisi.
77. An exploration of the relationships between urban building façade visual elements and people’s preferences in the city of Gorgan, Iran
78. Mapping the intangible: Using geolocated social media data to examine landscape aesthetics10.1016/j.landusepol.2018.05.049 ↗
79. Estimating visual properties of Rocky Mountain landscapes using GIS10.1016/S0169-2046(00)00141-9 ↗
80. Using crowdsourced imagery to detect cultural ecosystem services: A case study in South Wales, UK10.5751/ES-08436-210306 ↗
81. The quality of big (geo)data10.1177/2043820613513392 ↗