Papers
Quantifying The Coherence And Divergence Of Planned, Visual And Perceived Streets Greening To Inform Ecological Urban Planning
Qing Yang The University of Queensland
Chufan Cao The University of Queensland
Haimiao Li The University of Queensland
Waishan Qiu Cornell University
Wenjing Li Center for Spatial Information Science, The University of Tokyo
Dan Luo The University of Queensland
1. Problem Statement Urban ecology refers to the scientific research on the relationship between living things and their surrounding environment in the context of urban environment, and has significant effects on urban culture and aesthetic experience. (Mostafavi and Doherty, 2016) Therefore, urban designers have long been committed to facilitate urban ecology to promote health and well-being of the urban population. (Dempsey and Dobson, 2020; Breuste et al., 1998; Gaston, 2010 ) However, there is no agreement on the consistent connotation nor the measurement of ecology . Urban green spaces (UGSs) are often used to proxy the social benefits of greening and its role in sustainable urban development. Within this regard, greening ratio measured from satellite images and land use GIS data is one of the most common indicators in many studies (Kabisch et al., 2015). While the green ratio works as a satisfactory proxy for the “planned green”, emerging studies indicate that a more precise definition of ecology is needed when analyzing at the street scale. For instance, Ma Xiangyuan (Ma et al., 2021) took Google Street View images rather than satellite imagery, to measure human-scale and eye-level “visual green”. In addition, with increased interdisciplinary communication, scholars have begun to study the positive effects of green space on the psychological aspects of people (i.e., perceived green), including stress relief, improved mental health and increased well-being (Parsons, 1991). For example, Reid Ewing (Ewing and Handy, 2009) recorded the video clips of 48 commercial streets in the United States, and asked experts to rate the imageability, enclosure, human scale, transparency, and complexity of these streets. They developed a statistical model between the physical characteristics of the city and people’s subjective perceptions. However, less has been discussed regarding the coherence and divergence between these three types of greening, namely first, the ‘planned green’ which reflects the proportion of planning green space and objective ecology, second, ‘visual green’ which represents the proportion of vegetation within the line of sight, and third, ‘perceived green’ which captures people ‘ s subjective perception of environmental ecology. We hypothesize that these three types of green are intercorrelated however have different impacts to urban ecology, which has not been addressed adequately by current urban ecology research. This study attempts to synthesis theories from various fields including urban planning, urban design and cognitive psychology, through statistical analysis of satellite images, street images and subjective perceptions of urban residents about the spatial environment, combined with image recognition and big data analysis methods, proposes quantitative definitions of three types of green systems. 2. Research Question This study was set to discuss the effectiveness of urban space greening in a quantifiable way through image processing and big data statistics and establish a related database. This study quantified the coherence and divergence of planned, visual and perceived streets gree ning. Explore whether the more greening, the better the urban ecological experience, and the correlation between different urban space types and elements and urban ecological experience. 3. Article Aim This research aims at to evaluate the ecology of an area more clearly and accurately, discover the neglected aspects in urban design, and then guide the urban ecological design, provide residents with more effective greening, and make people truly feel green. 4. Methodology Planning green and visual green are the physical characteristics of the street and are objective data obtained through analysis. By extracting the green pixels in the urban satellite image, we obtained the proportion of the green pixels in the study area as the basis for planning green, which reflects the objective distribution of vegetation in the urban area. Planning green objectively measures the green from overlook perspectives. For visual green, this research uses the PsPnet (Pyramid Scene Parsing Network) algorithm to identify various objects in street view photos, such as trees, buildings, cars, etc., and calculates the proportion of each photo. The proportion of pixels occupied by all vegetation types will serve as the basis for our visual green. Visual green objectively measures the green from human eye perspectives. Perceptual green is people’s subjective rating of ecological experience. This study collected 500 street-view photos of Berlin city streets as training data. Then the photos were randomly divided into pairs, and visitors were invited to score based on the seven spatial environmental qualities, such as choosing which one is more ecological or orderly. And based on the Microsoft TrueSkill Algorithm, the photos are ranked by scores. Then, based on the 38 street physical characteristics obtained from the PsPnet, the Pearson correlation coefficient is used to establish a correlation model with seven spatial environmental qualities. (For example, the data reveals that the enclosure has a strong positive correlation with the area of sky, tree and building.) After that, this study collected more than 16,000 Google Street View photos in 22 areas of Brisbane as a database, combined with the statistical model obtained to score these photos, and finally screened four representative areas for comparative research. Perceptual green subjectively measures the green from human perceptual perspectives. 5. Main Conclusions The result reveals that the varying tendency of planning green, visual green and perception green is not always consistent in a continuous space. On the one hand, the reason of the distributing difference between planning green and visual green in a certain area is that the green is hidden from the street view, which causes the fact that people can’t see the green even though there is much vegetation. Then this paper explores four spatial types to illustrate the issue. On the other hand, by comparing the distribution of visual green and perceptual green, the study found that people’s ecological experience is not entirely attributable to how much green they see. In different city area, perception green is also closely associated with Space Syntax. For example, spatial order is strongly related to perception green in the suburbs, whereas in cities, there is few relevance between spatial order and perception green. This means that effectively organizing the spatial order in the suburbs will greatly enhance people’s green perception. In addition, the enclosure, aesthetics, earth proportions, building proportions, sky proportions, vegetation proportions all have a high correlation with perception green. Through spatial typology research, it can intuitively reveal the comprehensive factors that affect people’s green perception, and provide urban designers with more objective and targeted urban design strategies. 6. Sustainable Development Goals It is known that urban ecology is an extremely important sustainable factor. Effective ecological designs can improve people’s well-being and health. This research proposes typology studies for physical space by defining and quantifying three urban green indicators, which provides design reference with specific data for the consistency of objective ecological feature and subjective ecological experiences. Therefore, it satisfies the following goals: GOAL 3: Good Health and Well-being GOAL 11: Sustainable Cities and Communities GOAL 13: Climate Action 7. Bibliography Mostafavi, M., & Doherty, G. (2016). Ecological urbanism (Revised edition.). Lars Müller Publishers. Dempsey, N., & Dobson, J. (2020). Naturally Challenged: Contested Perceptions and Practices in Urban Green Spaces (1st ed. 2020.). Springer International Publishing : Imprint: Springer. Breuste, J., Feldmann, H., & Uhlmann, O. (1998). Urban ecology. Springer-Verlag. Gaston, K. J. (2010). Urban ecology. Cambridge University Press. Kwon, OH., Hong, I., Yang, J. et al. Urban green space and happiness in developed countries. EPJ Data Sci. 10, 28 (2021). https://doi.org/10.1140/epjds/s13688-021-00278-7 Qiu, W.; Li, W.; Liu, X.; Huang, X. Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai. ISPRS Int. J. Geo-Inf. 2021, 10, 493. https://doi.org/10.3390/ijgi10080493 Van den Berg, A. E., Maas, J., Verheij, R. A., & Groenewegen, P. P. (2010). Green space as a buffer between stressful life events and health. Social Science & Medicine (1982), 70(8), 1203–1210. https://doi.org/10.1016/j.socscimed.2010.01.002 Kabisch, N., Qureshi, S., & Haase, D. (2015). Human–environment interactions in urban green spaces — a systematic review of contemporary issues and prospects for future research. Environmental Impact Assessment Review, 50, 25–34. https://doi.org/10.1016/j.eiar.2014.08.007 Jim, C. Y., & Chen, W. Y. (2006). Perception and attitude of Residents toward urban green spaces in GUANGZHOU (China). Environmental Management, 38(3), 338–349. https://doi.org/10.1007/s00267-005-0166-6 Schetke, S., Qureshi, S., Lautenbach, S., & Kabisch, N. (2016). What determines the use of urban green spaces in highly URBANIZED Areas? – examples from two fast growing Asian cities. Urban Forestry & Urban Greening, 16, 150–159. https://doi.org/10.1016/j.ufug.2016.02.009 Ma, X., Ma, C., Wu, C., Xi, Y., Yang, R., Peng, N., Zhang, C., & Ren, F. (2021). Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing. Cities, 110, 103086. https://doi.org/10.1016/j.cities.2020.103086 Ewing, R., & 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 Gavrilidis, A.-A., Popa, A.-M., Nita, M.-R., Onose, D.-A., & Badiu, D.-L. (2020). Planning the “UNKNOWN”: Perception of urban green infrastructure concept in Romania. Urban Forestry & Urban Greening, 51, 126649. https://doi.org/10.1016/j.ufug.2020.126649 Ma, X., Ma, C., Wu, C., Xi, Y., Yang, R., Peng, N., Zhang, C., & Ren, F. (2021). Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing. Cities, 110, 103086. https://doi.org/10.1016/j.cities.2020.103086 Parsons, R. (1991). The potential influences of environmental perception on human health. Journal of Environmental Psychology, 11, 1–23.
Keywords: Urban Green Space；Urban Ecology；Green Perception；Subjective Measure；Space Syntax