Research on the Impact of Panoramic Green View Index of Virtual Reality Environments on Individuals’ Pleasure Level Based on EEG Experiment

Abstract

Green View Index (GVI) is a core indicator to measure urban quality. Identifying proper ranges of GVI has become a significant proposition in Landscape Architecture to design environments that can increase individuals’ pleasure level. However, quantitative research on the pleasure level impacted by varied GVIs is still inadequate. This research explores the changes of pleasure level through EEG data collection and questionnaire survey under panoramic scenarios with different panoramic GVIs, which can represent more environmental elements than two-dimensional images. By adding shrubs and trees gradually, this experiment precisely set five scenarios with the GVI changing from 0 to 30%, 60%, 90%, and 0. Research results show that 1) individuals’ pleasure level dropped to the lowest when they first enter the scenario with a panoramic GVI of 0, but when panoramic GVI increased from 0 to 30% and to 60%, the pleasure level increased and finally researched the highest; 2) in an environment with a panoramic GVI of 90%, individuals’ pleasure level significantly reduced, while some participants self-reported the sense of fear and oppression; and 3) when shifting panoramic GVI from 90% to 0, the bright and open space increased participants’ pleasure level. All these findings reveal that individuals’ pleasure level reached the highest under the scenario with 60% panoramic GVI; extremely high panoramic GVI may lead to negative emotions; and landscape with carefully designed panoramic GVIs can improve one’s pleasure level. Future research may probe into the relationship between GVI and individuals’ pleasure level from more perspectives to provide reference for the design, optimization, and evaluation of outdoor urban greening.

Keywords

Panoramic Green View Index / EEG / Environmental Psychology / Landscape Architecture / Virtual Reality / Pleasure Level

References

[1] Yu, K. (2020). On the „trio” of beautiful cities. Landscape Architecture Frontiers, 8(5), 4-11.

[2] Wang, Z., & Wang, W. (2020). An empirical study on the impact of green spaces in residential areas on the mental health of residents under COVID-19. Landscape Architecture Frontiers, 8(6), 46-59.

[3] Liu, B., & Jiang, Y. (2002). The inclined errors and countermeasures of urban green space system planning in China: The research on indices system of the urban green space system planning. Urban Planning Forum, (2), 27-29, 79. doi:10.3969/j.issn.1000-3363.2002.02.007

[4] Aoki, Y. (1987). Relationship between perceived greenery and width of visual fields. Journal of the Japanese Institute of Landscape Architecture, 51(1), 1-10.

[5] Li, M. (2018). Visual evaluation of green quantity in walking space of city street based on green visual rate: A case study of Beijing Axis (Master’s thesis). Retrieved from CNKI database.

[6] Li, Z., He, Z., Zhang, Y., Jin, S., Wang, X., Zhu, J., & Liu, S. (2020). Impact of greenspace exposure on residents’ mental health: A case study of Nanjing City. Progress in Geography, 39(5), 779-791.

[7] Ministry of Land, Infrastructure and Transport. (2005, August 12). A social experiment survey of the correlation between the amount of greenery in the city and the psychological effect: The green landscape and psychological effects of the city to alleviate the discomfort of midsummer days.

[8] Nielsen, T. S., & Hansen, K. B. (2007). Do green areas affect health? Results from a Danish survey on the use of green areas and health indicators. Health and Place, 13(4), 839-850.

[9] van Dillen, S. M. E., de Vries, S., Groenewegen, P. P., & Spreeuwenberg, P. (2011). Greenspace in urban neighbourhoods and residents’ health: Adding quality to quantity. Journal of Epidemiology and Community Health, 66(6), e8.

[10] Duan, S. (2021). Analyses on the characteristics of greenery amount of roads in residential quarters based on green view index—Taking Rizhao City, Shandong Province as an Example. Xiandai Horticulture, 44(5), 20-21. doi:10.14051/j.cnki.xdyy.2021.05.008

[11] Li, Y., & Huang, J. (2020). Evaluation of green view perception of walking environment in historical blocks based on green view attenuation curve: A case study of Tongwen Area, Zhongshan Road of Xiamen. Landscape Architecture, 27(11), 110-115. doi:10.14085/j.fjyl.2020.11.0110.06

[12] Pei, Y., Kan, C., & Dang, A. (2020). Street greenspace justice assessment study of Dongcheng District in Beijing based on street view data. Chinese Landscape Architecture, 36(11), 51-56. doi:10.19775/j.cla.2020.11.0051

[13] Dong, L. (2021). Research on quality improvement and reconstruction of mixed-function urban villages based on green view index and space syntax. Sustainable Development, 11(7), 531-539.

[14] Wang, T., Yang, X., Hu, S., & Shi, H. (2010). Comparisons of methods measuring green quantity. Journal of Chinese Urban Forestry, 8(4), 36-38. doi:10.3969/j.issn.1672-4925.2010.04.011

[15] Xu, L., Meng, R., & Chen, Z. (2017). Fascinating streets: The impact of building facades and green view. Landscape Architecture, (10), 27-33. doi:10.14085/j.fjyl.2017.10.0027.07

[16] Huang, Q., Yang, M., Jane, H., Li, S., & Bauer, N. (2020). Trees, grass, or concrete? The effects of different types of environments on stress reduction. Landscape and Urban Planning, (193), 103654.

[17] Wang, M., & Peng, H. (2018). A new interpretation on the development of green space in high-density city. Landscape Architecture Academic Journal, (1), 28-33. doi:10.3969/j.issn.1000-0283.2018.01.009

[18] Zhu, H., Kong, Y., & Ohno, R. (2021). Visual analysis method of garden tour experience based on panoramic images: A case study of the lingering garden. Landscape Architecture, 28(12), 77-84. doi:10.14085/j.fjyl.2021.12.0077.08

[19] Liu, B. (1990). Systematization of Landscape Engineering (pp. 23-31). China Architecture and Building Press.

[20] Nukarinen, T., Istance, H., Rantala, J., Mäkelä J., Korpela, K., Ronkainen, K., Surakka, V., & Raisamo, R. (2020). Physiological and Psychological Restoration in Matched Real and Virtual Natural Environments. In R. Bernhaupt, F. F. Mueller, D. Verweij, J. Andres, J. McGrenere, A. Cockburn, I. Avellino, A. Goguey, P. Bjørn, S. Zhao, B. P. Samson, & R. Kocielnik (Eds.), CHI EA ’20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-8). ACM.

[21] Chen, Y. (2019). Research on grading characteristics of EEG signal degree of fear based on VR system [Unpublished master’s thesis, Nanjing University of Posts and Telecommunications]. Retrieved from CNKI Database.

[22] Nie, S. (2017). A study on pleasant degree of campus road landscape: A case study of Huazhong University of Science and Technology. Interior Architecture of China, (6), 126.

[23] Gao, X., Dong, H., & Feng, Y. (2020). Research on the Relationship between Street Plant Space and Pleasure of Pedestrian Activity. In Proceedings of Annual Conference of Chinese Society of Landscape Architecture (Vol. 2) (pp. 290-298). China Architecture and Building Press. doi:10.26914/c.cnkihy.2020.056851

[24] Han, M., & Zhuang, W. (2017). Evaluation on architectural delight and user satisfaction of architectural space. South Architecture, (5), 10-14. doi:10.3969/j.issn.1000-0232.2017.05.010

[25] Aspinall, P., Mavros, P., Coyne, R., & Roe, J. (2013). The urban brain: Analysing outdoor physical activity with mobile EEG. British Journal of Sports Medicine, 49(4), 272-276.

[26] Chen, Z., & Liu, S. (2018). Real-time environmental affective experience assessment via wearable sensors. Chinese Landscape Architecture, 34(3), 12-17. doi:10.3969/j.issn.1000-6664.2018.03.003

[27] Sun, Y. (2021). Research on atrium landscape design optimization of commercial complex based on EEG technology [Master’s thesis]. Retrieved from CNKI database. doi:10.27801/d.cnki.gshyy.2021.000243

[28] Lv, Y., Zhang, W., & Shen, D. (2010). Subliminal affective priming effect by faces with different valence: An ERP study. Acta Psychologica Sinica, 42(9), 929-938.

[29] Deng, L., Chen, B., Pang, Q., Wen, R., & Xie, B. (2018). Application of EEG technology in emotional design of cultural and creative products. Journal of Graphics, 39(2), 327-332. doi:10.11996/JG.j.2095-302X.2018020327

[30] Liu, W. (2019). Influence of different forest landscape structure space on college students’ rehabilitation [Master’s thesis]. Retrieved from CNKI Database.

[31] Liang, J., & Chen, S. (2015). How positive emotion affects cognition: A review on the development of models. Chinese Journal of Applied Psychology, 21(2), 157-165. doi:10.3969/j.issn.1006-6020.2015.02.008

[32] Briesemeister, B. B., Tamm, S., Heine, A., & Jacobs, A. M. (2013). Approach the good, withdraw from the bad—A review on frontal Alpha asymmetry measures in applied psychological research. Psychology, 4(3A), 261-267.

[33] Dou, X. (2021). research on the influence of aromatic plants on the psychology and physiology of college students based on EEG analysis [Master’s thesis]. Retrieved from CNKI Database. doi:10.27809/d.cnki.gsjgc.2021.000418

[34] Li, Z., & Munemoto, J. (2010). Comparative Study on waterscaped and non-waterscaped spaces using electroencephalogram analysis: Audio-visual experiment on outer spaces of Chinese residential quarters basing on EEG measurement. Journal of Architecture and Planning (Transactions of AIJ), 75(647), 67-74.

[35] Shin, Y.-B., Woo, S.-H., Kim, D.-H., Kim, J., Kim, J.-J., & Park, J. Y. (2015). The effect on emotions and brain activity by the direct/indirect lighting in the residential environment. Neuroscience Letters, (584), 28-32.

[36] Chen, B., Deng, L., Cai, C., Liu, Y., Hong, P., Zhou, Y., & Jiang, H. (2017). Evaluation interactive prototype scheme based on emotional EEG. Packaging Engineering, 38(10), 110-114. doi:10.19554/j.cnki.1001-3563.2017.10.025

[37] Jin, M., Zheng, Q., Zheng, C., & Zhao, X. (2018). Study on comfort of micro-climate in urban green space based on EGG. In Proceedings of 2018 CSES Annual Conference (p. 3256-3263). Chinese Society for Environmental Sciences, Hefei, China.

[38] Wei, L., Shen, M., Zhang, G., & Shi, Z. (2004). Artifacts removing techniques on EEG waveforms. Chinese Journal of Applied Psychology, 10(3), 47-52. doi:10.3969/j.issn.1006-6020.2004.03.010

[39] Jansson, M., Fors, H., Lindgren, T., & Wiström, B. (2013). Perceived personal safety in relation to urban woodland vegetation—A review. Urban Forestry & Urban Greening, 12(2), 127-133.

[40] Jorgensen, A., Hitchmough, J., & Calvert, T. (2002). Woodland spaces and edges: Their impact on perception of safety and preference. Landscape and Urban Planning, 60(3), 135-150.

[41] Zheng, L., Yang, S., Pu, H., Liu, J., Lei, Y., & Wang, D. (2020). Visible green index research based on space satisfaction of urban park. Journal of Chinese Urban Forestry, 18(2), 30-34. doi:0.12169/zgcsly.2019.08.18.0001

[42] Han, K.-T. (2017). The effect of nature and physical activity on emotions and attention while engaging in green exercise. Urban Forestry & Urban Greening, (24), 5-13.

[43] Tost, H., Champagne, F. A., & Meyer-Lindenberg, A. (2015). Environmental influence in the brain, human welfare and mental health. Nature Neuroscience, 18(10), 1421-1431.

[44] Li, T., Xue, B., Yang, X., Zhang, L., & Wang, Z. (2020). Research on attention recovery in restorative environment based on wireless physiological sensor and virtual reality technology. Chinese Landscape Architecture, 36(12), 62-67. doi:10.19775/j.cla.2020.12.0062

[45] Zhang, J., Gong, M., Wu, J., Nan, H., & Dong, L. (2020). Effect of different forest environments on people’s physical and mental health. Chinese Landscape Architecture, 36(2), 118-123. doi:10.19775/j.cla.2020.02.0118

[46] Zhang, W., Zhou, Y., & Yang, M. (2019). Research on automatic identification and measurement of panoramic visible green index. Landscape Architecture, 26(10), 89-94. doi:10.14085/j.fjyl.2019.10.0089.06

[47] Wang, Y., & Huang, J. (2021). Construction of evaluation model of urban ecological comfort index based on green view index. Acta Ecologica Sinica, 41(6), 2170-2179.

[48] Zhao, L., Bian, Y., Rong, J., Liu, X., & Shu, S. (2015). Evaluation method for pedestrian level of service on sidewalks based on fuzzy neural network model. Journal of Intelligent & Fuzzy Systems, 30(5), 2905-2913.

RIGHTS & PERMISSIONS

Higher Education Press