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A Green View Index Improvement Program for Urban Roads Using a Green Infrastructure Theory - Focused on Chengdu City, Sichuan Province, China -

그린인프라스트럭처 개념을 적용한 가로 녹시율 개선 방안 - 중국 쓰촨성(四川省) 청두시(成都市)을 중심으로 -

  • Hou, ShuJun (Dept. of Landscape Architecture, Kyungpook National University ) ;
  • Jung, Taeyeol (Dept. of Landscape Architecture, Kyungpook National University )
  • Received : 2023.11.22
  • Accepted : 2023.12.11
  • Published : 2023.12.31

Abstract

The concept of "green infrastructure" emphasizes the close relationship between natural and urban social systems, thereby providing services that protect the ecological environment and improve the quality of human life. The Green View Index(GVI) is an important indicator for measuring the supply of urban green space and contains more 3D spatial elements concerning the green space ratio. This study focused on an area within the Third Ring Road in the city of Chengdu, Sichuan Province, China. The purposes of this study were three-fold. First, this study analyzed the spatial distribution characteristics of the GVI in urban streets and its correlation with the urban park green space system using Street View image data. Second to analyze the characteristics of low GVI streets were analyzed. Third, to analyze the connectivity between road traffic and street GVI using space syntax were analyzed. This study found that the Street GVI was higher in the southwestern part of the study area than in the northeastern part. The spatial distribution of the street GVI correlated with urban park green space. Second, the street areas with low GVI are mainly concentrated in areas with dense commercial facilities, areas with new construction, areas around elevated roads, roads below Class 4, and crossroads areas. Third, the high integration and low GVI areas were mainly concentrated within the First Ring Road in the city as judged by the concentration of vehicles and population. This study provides base material for future programs to improve the GVI of streets in Chengdu, Sichuan Province.

그린인프라스트럭처(Green Infrastructure)의 개념은 자연 시스템과 도시 사회 시스템 간의 밀접한 관계를 강조하며, 생태 환경을 보호하고 인간의 삶의 질을 향상시키는 서비스를 제공한다. 녹시율은 도시의 녹지 공급을 측정하는 중요한 지표로, 녹피율보다 더 많은 3차원 공간 요소를 포함한다. 가로 녹지는 도시 그린인프라스트럭처의 중요한 부분이며 가로 녹시율의 개선은 도시 기후 위기에 대처하고 인간의 복지를 향상시키는 데 매우 중요하다. 본 연구는 중국 쓰촨성(四川省) 청두시(成都市)의 3순환가로 이내 지역을 중심으로 한다. 연구 목적은 첫째, 스트리트뷰 이미지 데이터를 활용하여 가로 녹시율의 공간 분포 특성 및 도시 공원 녹지 시스템과의 상관관계를 분석하는 것이다. 둘째, 낮은 녹시율 가로의 특성을 분석한다. 셋째, 공간 구문론을 활용하여 도로 교통과 가로 녹시율의 연결성을 분석한다. 연구 결과를 살펴보면, 첫째, 연구 범위 내에서 남서부 가로 녹시율은 북동부보다 높다. 가로 녹시율의 공간적 분포는 도시 공원 녹지와 상관관계가 있다. 둘째, 낮은 녹시율의 가로 이미지는 주로 상업 시설 집적 지역, 도시 신규 건설 지역, 고가가로 및 주변, 도시 4급 이하 가로, 교차로 지역에 집중되어 있다. 셋째, 도시 교통과 인구 집중 지역에서 높은 통합도와 낮은 녹시율의 가로는 주로 1순환가로 중심 지역에 집중되어 있다. 이는 향후 쓰촨성 청두시 가로 녹시율를 개선하기 위한 기초자료를 제공할 수 있다.

Keywords

References

  1. Aikoh, T., R. Homma and Y. Abe(2023) Comparing conventional manual measurement of the green view index with modern automatic methods using google street view and semantic segmentation. Urban Forestry & Urban Greening 80: 127845. 
  2. Alsaad, H., M. Hartmann, R. Hilbel and C. Voelker(2022) ENVI-met validation data accompanied with simulation data of the impact of facade greening on the urban microclimate. Data in Brief 42: 108200. 
  3. Aoki, Y.(1991) Evaluation methods for landscapes with greenery. Landscape Research 16(3): 3-6.  https://doi.org/10.1080/01426399108706344
  4. Barreira, A. P., J. Andraz, V. Ferreira and T. Panagopoulos(2023) Perceptions and preferences of urban residents for green infrastructure to help cities adapt to climate change threats. Cities 141: 104478. 
  5. Black, K. J. and M. Richards(2020) Eco-gentrification and who benefits from urban green amenities: NYC's high line. Landscape and Urban Planning 204: 103900. 
  6. Chen, X., Q. Meng, D. Hu, L. Zhang and J. Yang(2019) Evaluating greenery around streets using baidu panoramic street view images and the panoramic green view index. Forests 10(12): 1109. 
  7. Dong, J., F. Guo, M. Lin, H. Zhang and P. Zhu(2022) Optimization of green infrastructure networks based on potential green roof integration in a high-density urban area-A case study of Beijing, China. Science of The Total Environment 834: 155307. 
  8. Elsadek, M., B. Liu, Z. Lian and J. Xie(2019) The influence of urban roadside trees and their physical environment on stress relief measures: A field experiment in Shanghai. Urban Forestry & Urban Greening 42: 51-60.  https://doi.org/10.1016/j.ufug.2019.05.007
  9. Han, X., L. Wang, S. H. Seo, J. He and T. Jung(2022) Measuring perceived psychological stress in urban built environments using Google Street View and deep learning. Frontiers in Public Health 10: 891736. 
  10. Hillier, B. and S. Iida(2005) Network and psychological effects in urban movement. In: Cohn, A.G., Mark, D.M. (Eds.), Spatial Information Theory, Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 475-490. 
  11. Hsieh, C. M., F. C. Jan and L. Zhang(2016) A simplified assessment of how tree allocation, wind environment, and shading affect human comfort. Urban Forestry & Urban Greening 18: 126-137.  https://doi.org/10.1016/j.ufug.2016.05.006
  12. Hunter, A. M., N. S. Williams, J. P. Rayner, L. Aye, D. Hes and S. J. Livesley(2014) Quantifying the thermal performance of green facades: A critical review. Ecological Engineering 63: 102-113.  https://doi.org/10.1016/j.ecoleng.2013.12.021
  13. Jiang, B., D. Li, L. Larsen and W. C. Sullivan(2016) A dose-response curve describing the relationship between urban tree cover density and self-reported stress recovery. Environment and Behavior 48(4): 607-629.  https://doi.org/10.1177/0013916514552321
  14. Ki, D. and S. Lee(2021) Analyzing the effects of green view index of neighborhood streets on walking time using Google Street View and deep learning. Landscape and Urban Planning 205: 103920. 
  15. Kim, J. G. and Y. I. Kim(2012) Research on the proximity model of urban park green space - integration of spatial theory and mental model. In Proceedings of the Korean Institute of Landscape Architecture Conference (pp. 11-15). The Korean Institute of Landscape Architecture. 
  16. Kim, J. H., Y. H. Seo, Y. H. Yoon and C. H. Joo(2014) A study on the observer psychological change in accordance with index of greenness in landscape planting space. Journal of Environmental Science International 23(10): 1663-1671.  https://doi.org/10.5322/JESI.2014.23.10.1663
  17. Kim, S.H. (2014). A study on the stormwater green infrastructure strategy for the sound hydrological cycle management in urban areas. Journal of Environmental Studies, 53, 113-113. 
  18. Kim, Y. G. and Y. H. Son(2012) Study on the green infrastructure application with planning system: Focused on green infrastructure planning and policy in the U.K.. Journal of Korea Planners Association 47(5): 69-86 
  19. Kolimenakis, A., A. D. Solomou, N. Proutsos, E. V. Avramidou, E. Korakaki, G. Karetsos, G. Maroulis, E. Papagiannis and K. Tsagkari(2021) The socioeconomic welfare of urban green areas and parks: A literature review of available evidence. Sustainability 13(14): 7863. 
  20. Korkou, M., A. K. Tarigan and H. M. Hanslin(2023) The multifunctionality concept in urban green infrastructure planning: A systematic literature review. Urban Forestry & Urban Greening, 127975. 
  21. Lee, H. K.(2020) Spatial analysis of green infrastructure for urban flood mitigation. KIBIM Magazine 10(4): 81-88. 
  22. Ma, J., X. Li, J. Baoquan, X. Liu, T. Li, W. Zhang and W. Liu(2021) Spatial variation analysis of urban forest vegetation carbon storage and sequestration in built-up areas of Beijing based on i-Tree Eco and Kriging. Urban Forestry & Urban Greening 66: 127413. 
  23. McMahon, E. T. and M. A. Benedict(2000) Green infrastructure. Planning Commissioners Journal 37(4): 4-7. 
  24. Mohtat, N. and L. Khirfan(2023) Epistemic justice in flood-adaptive green infrastructure planning: The recognition of local experiential knowledge in Thorncliffe Park, Toronto. Landscape and Urban Planning 238: 104834. 
  25. Orihara, k.(2006) A study on the evaluation of green landscape: A study on the evaluation method of green landscape for good landscape formation. Ibec: Information on Building Environment and Energy Conservation 27(3): 32-35. 
  26. Peng, L. L., Z. Jiang, X. Yang, Y. He, T. Xu, and S. S. Chen(2020) Cooling effects of block-scale facade greening and their relationship with urban form. Building and Environment 169: 106552. 
  27. Shen, Y., F. Sun and Y. Che(2017) Public green spaces and human wellbeing: Mapping the spatial inequity and mismatching status of public green space in the Central City of Shanghai. Urban Forestry & Urban Greening 27: 59-68.  https://doi.org/10.1016/j.ufug.2017.06.018
  28. Sprengel, L., A. Hamann, S. Wu and H. Spiecker(2023) Carbon sequestration potential of eight economically important tree species in Northeast China under climate change. Forest Ecology and Management 545: 121299. 
  29. Sung, J. S.(2012) Constructing landscape as an operational multi-environ-mental control utility and green infrastructure: Landscape design for national marine biology resource institute. Journal of Korean Environmental Restoration Technology 15(2): 41-50.  https://doi.org/10.13087/kosert.2012.15.2.041
  30. Tomson, M., P. Kumar, Y. Barwise, P. Perez, H. Forehead, K. French, L. Morawska and J. F. Watts(2021) Green infrastructure for air quality improvement in street canyons. Environment International 146: 106288. 
  31. Tonosaki, K.(2010) Research on the new measurement method of the ratio of vertical green coverage with leaf colors. Landscape Research Japan Online 3: 26-31.  https://doi.org/10.5632/jilaonline.3.26
  32. Walmsley, A.(2006) Greenways: Multiplying and diversifying in the 21st century. Landscape and Urban Planning 76(1-4): 252-290.  https://doi.org/10.1016/j.landurbplan.2004.09.036
  33. Wang, L., X. Han, J. He and T. Jung(2022) Measuring residents' perceptions of city streets to inform better street planning through deep learning and space syntax. ISPRS Journal of Photogrammetry and Remote Sensing 190: 215-230.  https://doi.org/10.1016/j.isprsjprs.2022.06.011
  34. Wong, N. H., A. Y. K. Tan, P. Y. Tan, A. Sia and N. C. Wong(2010) Perception studies of vertical greenery systems in Singapore. Journal of Urban Planning and Development 136(4): 330-338.  https://doi.org/10.1061/(ASCE)UP.1943-5444.0000034
  35. Wu, Y., Y. D. Wei, M. Liu and I. Garcia(2023) Green infrastructure inequality in the context of COVID-19: Taking parks and trails as examples. Urban Forestry & Urban Greening 86: 128027. 
  36. Ye, Y., D. Richards, Y. Lu, X. Song, Y. Zhuang, W. Zeng and T. Zhong(2019) Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landscape and Urban Planning 191: 103434. 
  37. Yen, B. T., C. Mulley and H. Shearer(2023) The value of green infrastructure to property prices: Evidence from the Gold Coast, Queensland, Australia. Land Use Policy 134: 106890. 
  38. Yeow, L. W., R. Low, Y. X. Tan and L. Cheah(2021) Point-of-Interest (POI) data validation methods: an urban case study. ISPRS International Journal of Geo-Information 10(11): 735. 
  39. Yu, H., Y. Zhou, R. Wang, Z. Qian, L. D. Knibbs, B. Jalaludin, M. Schootman, P. Zhou, G. Chen, W. Feng, M. Xiang and G. H. Dong(2021) Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index. Environmental Pollution 286: 117582. 
  40. https://baike.baidu.com/item/成都[Accessed September 26, 2023] 
  41. http://api.map.baidu.com/panorama/v2?ak[Accessed August 16, 2023]