Neuro-Urbanism: Measurement of the Street Enclosure and Its Influence on Human Physiology Through Wearable Sensors

Öz

In this paper, we explore the influence of the visual qualities of street spaces on human physiology and perception of comfort at selected street corners (i.e., Fumin, Changde, Xinle and Donghu Roads, Jing’an district, Shanghai). The visual qualities of the street were identified by the physical space variations, sky visibility, wall continuity, and the cross-sectional proportion. These three variables contribute to the "enclosure index", a dimensionless numeral which defines the occupant perception of the street space. We used a custom biosensor kit to collect the average heart rate of 15 participants for one minute measured at the aforementioned street corners. We compared the heart rate of the participants when they looked toward the intersection (open street space) and looked down the street (enclosed street space) and asked them to complete a questionnaire on the level of comfort. The questionnaire data was then compared to the corresponding heart rates. The results showed that the heart rates of the participants who looked at the street view (more enclosed) were lower than those who looked at the corner view (less enclosed).

Anahtar Kelimeler

• Urban environment
• Wearable technology
• Neurophysiology
• Street corners

Kaynakça

1. Alkhresheh, M. M. (2007). Enclosure as a Function of Height-to-width Ratio and Scale: Its Influence on User’s Sense of Comfort and Safety in Urban Street Space. University of Florida Press.
2. Arnold, H. F. (1980). Trees in urban design. Van Nostrand Reinhold Company.
3. Augst, T. and Pape. G. (2022). Getting enclosure right: Creating a comfortable public room. Michigan State University, Retrieved April 2, 2024 from https://www.canr.msu.edu/news/getting_enclosure_right_creating_a_comfortable_public_room
4. Benita, F., and Tunçer, B. (2019). Exploring the effect of urban features and immediate environment on body responses. Urban Forestry & Urban Greening, 43(12), 1-14. https://doi.org/10.1016/j.ufug.2019.126365
5. Blecic, I., Canu, D., Cecchini, A., Congiu, T., & Fancello, G. (2017). Walkability and street intersections in rural-urban fringes: A decision aiding evaluation procedure. Sustainability (Switzerland), 9(6), 1-19. https://doi.org/10.3390/su9060883
6. Cassarino, M., and Setti, A. (2015). Environment as ‘Brain Training’: A review of geographical and physical environmental influences on cognitive ageing. Ageing Research Reviews, 23(Part B), 167–182. https://doi.org/10.1016/J.ARR.2015.06.003
7. Chatterjee, A. (2010). Neuroaesthetics: A coming of Age Story. Journal of Cognitive Neuroscience, 23(1), 53-62. https://doi.org/ 10.1162/jocn.2010.21457
8. Dalton, R. C. (2003). The secret is to follow your nose: Route path selection and angularity. Environment and Behavior, 35(1), 1-14. https://doi.org/10.1177/0013916502238867

9. Dixon-Woods, M., Agarwal, S., Jones, D., Young, B., & Sutton, A. (2005). Synthesising qualitative and quantitative evidence: A review of possible methods. Journal of Health Services Research and Policy, 10(1), 45–53. https://doi.org/10.1258/1355819052801804
10. Dover, V., and Massengale, J. (2013). Street Design: The Secret to Great Cities and Towns. John Wiley & Sons Press.
11. Emo, B. (2012, September 3). Wayfinding in real cities: Experiments at street corners. [Oral presentation]. In Spatial Cognition VIII: International Conference, Berlin Heidelberg, Germany. https://doi.org/10.1007/978-3-642-32732-2_30
12. Ewing, R., Hajrasouliha, A., Neckerman, K. M., Purciel-Hill, M., & Greene, W. (2016). Streetscape Features Related to Pedestrian Activity. Journal of Planning Education and Research, 36(1), 5-15. https://doi.org/10.1177/0739456X15591585
13. Ewing, R., and Handy, S. (2009). Measuring the unmeasurable: Urban design qualities related to walkability. Journal of Urban Design, 14(1), 65-84. https://doi.org/10.1080/13574800802451155
14. Galindo, M. P., and Corraliza, J. (2000). Environmental Aesthetics and Psychological wellbeing: Relationships between preference judgements for urban landscapes and other relevant affective responses. Psychology in Spain, 4(1), 13-27.
15. Gehl, J. (2006). Life between buildings: Using public space (Jo. Koch, Trans.). The Danish Architectural Press (Original work published 1987).
16. Gehl, J. (2013). Cities for People. Island Press.
17. Hillier, B. (2007). Space is the machine: A configurational theory of architecture. In Space Syntax Press.
18. Hillier, B. (2003, June 17-19). The knowledge that shapes the city: the human city beneath the social city [Oral presentation]. 4th International Space Syntax Symposium. London, UK.
19. Hillier, B., and Iida, S. (2005, September 14-18). Network effects and psychological effects: A theory of urban movement [Oral presentation]. In International conference on spatial information theory, Berlin, Germany.
20. Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biol Psychol, 84(3), 394-421. https://doi.org/10.1016/j.biopsycho.2010.03.010
21. Li, X., Hijazi, I., Koenig, R., Lv, Z., Zhong, C., & Schmitt, G. (2016). Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique. ISPRS International Journal of Geo-Information, 5(11), 218-236. https://doi.org/10.3390/ijgi5110218
22. Lou, Z., Wang, L., Jiang, K., Wei, Z., & Shen, G. (2020). Reviews of wearable healthcare systems: Materials, devices and system integration. Materials Science and Engineering, 140, 1-43. https://doi.org/10.1016/j.mser.2019.100523
23. Lynch, K. (1960). The Image of the City. MIT Press.
24. Mamun, M. A. A., and Yuce, M. R. (2019). Sensors and Systems for Wearable Environmental Monitoring Toward IoT-Enabled Applications: A Review. IEEE Sensors Journal, 19(18), 7771-7788. https://doi.org/10.1109/JSEN.2019.2919352
25. Meenar, M. R., and Mandarano, L. A. (2021). Using photovoice and emotional maps to understand transitional urban neighborhoods. Cities, 118(1), 1-12. https://doi.org/10.1016/j.cities.2021.103353
26. Morello, E., and Ratti, C. (2009). A digital image of the city: 3D isovists in Lynch’s urban analysis. Environment and Planning B: Planning and Design, 36(5), 837–853. https://doi.org/10.1068/b34144t
27. Natapov, A., and Fisher-Gewirtzman, D. (2016). Visibility of urban activities and pedestrian routes: An experiment in a virtual environment. Computers, Environment and Urban Systems, 58(2), 60–70. https://doi.org/10.1016/j.compenvurbsys.2016.03.007
28. Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17(3), 715–734. https://doi.org/10.1017/S0954579405050340
29. Quercia, D., Schifanella, R., & Aiello, L. M. (2014, September 1-4). The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. [Oral presentation]. In Proceedings of the 25th ACM conference on Hypertext and social media, NewYork, United States. https://doi.org/10.1145/2631775.2631799
30. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714
31. Tang, J., and Long, Y. (2019). Measuring visual quality of street space and its temporal variation: Methodology and its application in the Hutong area in Beijing. Landscape and Urban Planning, 191(10), 1-18. https://doi.org/10.1016/j.landurbplan.2018.09.015
32. Zhang, Z., Zhuo, K., Wei, W., Li, F., Yin, J., & Xu, L. (2021). Emotional responses to the visual patterns of urban streets: evidence from physiological and subjective indicators. International Journal of Environmental Research and Public Health, 18(18), 1-20. https://doi.org/10.3390/ijerph18189677
33. Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017, July 21-26). Pyramid scene parsing network. [Oral presentation]. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, USA. https://doi.org/10.48550/arXiv.1612.01105