Assessing Inequity in Green Space Exposure toward a “15-Minute City” in Zhengzhou, China: Using Deep Learning and Urban Big Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. Street View Images Data
2.2.2. Housing Price Data
2.3. Data Analysis
2.3.1. Framework Design
2.3.2. Image Segmentation Based on Machine Learning
2.3.3. Location Entropy
2.3.4. Spatial Statistical Analysis
3. Results
3.1. Spatial Inequity of Green Space Exposure
3.2. The Association between Green Space Exposure and Rental Prices
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Cutter, S.L. Race, class and environmental justice. Progress Hum. Geogr. 1995, 19, 111–122. [Google Scholar] [CrossRef]
- Agyeman, J.; Evans, B. ‘just sustainability’: The emerging discourse of environmental justice in britain? Geogr. J. 2004, 170, 155–164. [Google Scholar] [CrossRef]
- Pellow, D. Environmental inequality formation-Toward a theory of environmental injustice. Am. Behav. Sci. 2000, 43, 581–601. [Google Scholar] [CrossRef]
- Fernández-Somoano, A.; Tardon, A. Socioeconomic status and exposure to outdoor NO2 and benzene in the Asturias INMA birth cohort, Spain. J. Epidemiol. Community Health 2014, 68, 29–36. [Google Scholar] [CrossRef] [Green Version]
- Zou, B.; Peng, F.; Wan, N.; Mamady, K.; Wilson, G.J. Spatial cluster detection of air pollution exposure inequities across the united states. PLoS ONE 2014, 9, e91917. [Google Scholar] [CrossRef] [Green Version]
- Xiao, Y.; Wang, D.; Fang, J. Exploring the disparities in park access through mobile phone data: Evidence from Shanghai, China. Landsc. Urban Plan. 2019, 181, 80–91. [Google Scholar] [CrossRef]
- Chen, Y.; Yue, W.; Rosa, D.L. Which communities have better accessibility to green space? An investigation into environmental inequality using big data. Landsc. Urban Plan. 2020, 204, 103919. [Google Scholar] [CrossRef]
- Zheng, Z.; Shen, W.; Li, Y.; Qin, Y.; Wang, L. Spatial equity of park green space using kd2sfca and web map api: A case study of Zhengzhou, China. Appl. Geogr. 2020, 123, 102310. [Google Scholar] [CrossRef]
- Matthew, M.; Connachie, M.; Shackleton, C.M. Public green space inequality in small towns in South Africa. Habitat Int. 2010, 34, 244–248. [Google Scholar] [CrossRef] [Green Version]
- Nesbitt, L.; Meitner, M.J.; Girling, C.; Sheppard, S.R.J.; Lu, Y. Who has access to urban vegetation? A spatial analysis of distributional green equity in 10 US cities. Landsc. Urban Plan. 2019, 181, 51–79. [Google Scholar] [CrossRef]
- Baoxing, Q. Challenges Faced by China in Its Rapid Urbanization Process in the Near Future. Urban Dev. Stud. 2003, 10, 1–15. [Google Scholar]
- Wolf, K.L. Business district streetscapes, trees, and consumer response. J. For. 2005, 103, 396–400. [Google Scholar] [CrossRef]
- Bain, L.; Gray, B.; Rodgers, D. Living Streets: Strategies for Crafting Public Space. Landsc. Archit. 2012, 102, 1. [Google Scholar]
- Dadvand, P.; Rivas, I.; Basagaña, X.; Alvarez-Pedrerol, M.; Su, J.; De Castro, P.M.; Nieuwenhuijsen, M.J. The association between greenness and traffic-related air pollution at schools. Sci. Total Environ. 2015, 523, 59–63. [Google Scholar] [CrossRef] [PubMed]
- Su, S.; Zhang, Q.; Pi, J.; Wan, C.; Weng, M. Public health in linkage to land use: Theoretical framework, empirical evidence, and critical implications for reconnecting health promotion to land use policy. Land Use Policy 2016, 57, 605–618. [Google Scholar] [CrossRef]
- You, H. Characterizing the inequalities in urban public green space provision in Shenzhen, China. Habitat Int. 2016, 56, 176–180. [Google Scholar] [CrossRef]
- Krekel, C.; Kolbe, J.; Wüstemann, H. The greener, the happier? The effect of urban land use on residential well-being. Ecol. Econ. 2016, 21, 117–127. [Google Scholar] [CrossRef] [Green Version]
- Zhou, X.; Parves Rana, M. Social benefits of urban green space: A conceptual framework of valuation and accessibility measurements. Manag. Environ. Qual. Int. J. 2012, 23, 173–189. [Google Scholar] [CrossRef]
- Kabisch, N.; Haase, D. Green justice or just green? provision of urban green spaces in Berlin, Germany. Landsc. Urban Plan. 2014, 122, 129–139. [Google Scholar] [CrossRef]
- Wang, R.; Feng, Z.; Pearce, J.; Yao, Y.; Li, X.; Liu, Y. The distribution of greenspace quantity and quality and their association with neighbourhood socioeconomic conditions in Guangzhou, China: A new approach using deep learning method and street view images. Sustain. Cities Soc. 2021, 66, 102664. [Google Scholar] [CrossRef]
- Li, X.; Zhang, C.; Li, W.; Kuzovkina, Y.A. Environmental inequities in terms of different types of urban greenery in Hartford, Connecticut. Urban For. Urban Green. 2016, 18, 163–172. [Google Scholar] [CrossRef]
- Xu, C.; Haase, D.; Pribadi, D.O.; Pauleit, S. Spatial variation of green space equity and its relation with urban dynamics: A case study in the region of Munich. Ecol. Indic. 2018, 93, 512–523. [Google Scholar] [CrossRef]
- Zhou, X.; Kim, J. Social disparities in tree canopy and park accessibility: A case study of six cities in Illinois using GIS and remote sensing. Urban For. Urban Green. 2013, 12, 88–97. [Google Scholar] [CrossRef]
- Li, X.; Zhang, C.; Li, W.; Kuzovkina, Y.A.; Weiner, D. Who lives in greener neighborhoods? The distribution of street greenery and its association with residents’ socioeconomic conditions in Hartford, Connecticut, USA. Urban For. Urban Green. 2015, 14, 751–759. [Google Scholar] [CrossRef]
- Jennifer, R.; Wolch, J.B.; Newell, J.P. Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landsc. Urban Plan. 2014, 125, 234–244. [Google Scholar] [CrossRef] [Green Version]
- Joassart-Marcell, P. Leveling the Playing Field? Urban Disparities in Funding for Local Parks and Recreation in the Los Angeles Region. Environ. Plan. A 2010, 42, 1174–1192. [Google Scholar] [CrossRef]
- Shen, Y. Study on the Spatial Layout of Public Green Space in Central Shanghai under the Concept of Environmental Justice. Master’s Thesis, East China Normal University, Shanghai, China, 2017. [Google Scholar]
- Astell-Burt, T.; Feng, X.; Mavoa, S.; Badland, H.M.; Giles-Corti, B. Do low-income neighbourhoods have the least green space? a cross-sectional study of Australia’s most populous cities. BMC Public Health 2014, 14, 292. [Google Scholar] [CrossRef]
- Apparicio, P.; Thi-Thanh-Hien, P.; Seguin, A.M.; Landry, S.; Gagnon, M. Spatial distribution of vegetation in Montreal: An uneven distribution or environmental inequity? Landsc. Urban Plan. 2012, 107, 214–224. [Google Scholar] [CrossRef]
- Shen, Y.; Sun, F.; Che, Y. Public green spaces and human wellbeing: Mapping the spatial inequity and mismatching status of public green space in the Central City of Shanghai. Urban For. Urban Green. 2017, 27, 59–68. [Google Scholar] [CrossRef]
- Jiang, H.; Zhou, C.; Xiao, R. Spatial differentiation and social equity of public parks in Guangzhou. City Plan. Rev. 2010, 34, 43–48. [Google Scholar]
- Rigolon, A.; Browning, M.M.H.E.; Lee, K.; Shin, S. Access to urban green space in cities of the Global South: A systematic literature review. Urban Sci. 2018, 2, 67. [Google Scholar] [CrossRef] [Green Version]
- Wüstemann, H.; Kalisch, D.; Kolbe, J. Access to urban green space and environmental inequalities in Germany. Landsc. Urban Plan. 2017, 164, 124–131. [Google Scholar] [CrossRef]
- Yao, L.; Liu, J.; Wang, R.; Yin, K.; Han, B. Effective green equivalent—A measure of public green spaces for cities. Ecol. Indic. 2014, 47, 123–127. [Google Scholar] [CrossRef]
- Jensen, R.; Gatrell, J.; Boulton, J.; Harper, B. Using remote sensing and geographic information systems to study urban quality of life and urban forest amenities. Ecol. Soc. 2004, 9, 5. [Google Scholar] [CrossRef]
- Jennings, V.; Johnson Gaither, C.; Gragg, R.S. Promoting Environmental Justice Through Urban Green Space Access: A Synopsis. Environ. Justice 2012, 5, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Van Dillen, S.M.; de Vries, S.; Groenewegen, P.P.; Spreeuwenberg, P. Greenspace in urban neighbourhoods and residents’ health: Adding quality to quantity. J. Epidemiol. Community Health 2012, 66, e8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Qian, Y.; Zhou, W.; Li, W.; Han, L. Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite images. Urban For. Urban Green. 2015, 14, 39–47. [Google Scholar] [CrossRef]
- Dong, R.; Zhang, Y.; Zhao, J. How green are the streets within the sixth ring road of Beijing? An analysis based on Tencent Street View pictures and the green view index. Int. J. Environ. Res Public Health 2018, 15, 1367. [Google Scholar] [CrossRef] [Green Version]
- Leslie, E.; Sugiyama, T.; Ierodiaconou, D.; Kremer, P. Perceived and objectively measured greenness of neighbourhoods: Are they measuring the same thing? Landsc. Urban Plan. 2010, 95, 28–33. [Google Scholar] [CrossRef]
- Landry, S.M.; Chakraborty, J. Street Trees and Equity: Evaluating the Spatial Distribution of an Urban Amenity. Environ. Plan. A Econ. Space 2009, 41, 2651–2670. [Google Scholar] [CrossRef]
- Yang, J.; Zhao, L.; McBride, J.; Gong, P. Can you see green? Assessing the visibility of urban forests in cities. Landsc. Urban Plan. 2009, 91, 97–104. [Google Scholar] [CrossRef]
- Li, X.; Zhang, C.; Li, W.; Ricard, R.; Meng, Q.; Zhang, W. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban For. Urban Green. 2015, 14, 675–685. [Google Scholar] [CrossRef]
- Helbich, M.; Yao, Y.; Liu, Y.; Zhang, J.; Liu, P.; Wang, R. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environ Int. 2019, 126, 107–117. [Google Scholar] [CrossRef] [PubMed]
- Li, X.; Santi, P.; Courtney, T.K.; Verma, S.K.; Ratti, C. Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data. Trans. GIS 2018, 22, 1029–1044. [Google Scholar] [CrossRef]
- Yin, L.; Cheng, Q.; Wang, Z.; Shao, Z. ‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts. Appl. Geogr. 2015, 63, 337–345. [Google Scholar] [CrossRef]
- Seiferling, I.; Naik, N.; Ratti, C.; Proulx, R. Green streets−Quantifying and mapping urban trees with street-level imagery and computer vision. Landsc. Urban Plan. 2017, 165, 93–101. [Google Scholar] [CrossRef]
- Liu, L.; Silva, E.A.; Wu, C.; Wang, H. A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Comput. Environ. Urban Syst. 2017, 65, 113–125. [Google Scholar] [CrossRef]
- Rong, P.; Zheng, Z.; Kwan, M.P.; Qin, Y. Evaluation of the spatial equity of medical facilities based on improved potential model and map service API: A case study in Zhengzhou, China. Appl. Geogr. 2020, 119, 102192. [Google Scholar] [CrossRef]
- Li, b.; Song, Y.; Yu, K. Evaluation Method for Measurement of Accessibility in Urban Public Green Space Planning. Acta Sci. Nat. Univ. Pekin. 2008, 44, 618–624. [Google Scholar] [CrossRef]
- Chen, Y.; Yu, P.; Li, Z.G.; Wang, J.; Chen, Y. Environment Equity Measurement of Urban Green Space from the Perspective of SDG11:A Case Study of the Central Urban Area of Wuhan. Geogr. Geo-Inf. Sci. 2021, 37, 81–89. [Google Scholar] [CrossRef]
- Guo, S.; Song, C.; Pei, T.; Liu, Y.X.; Ma, T.; Dua, Y.; Chen, J.; Fan, Z.D.; Tang, X.L.; Peng, Y.; et al. Accessibility to urban parks for elderly residents: Perspectives from mobile phone data. Landsc. Urban Plan. 2019, 191, 103642. [Google Scholar] [CrossRef]
- Long, J.; Shelhamer, E.; Darrell, T. Fully Convolutional Networks for Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2015, 39, 640–651. [Google Scholar] [CrossRef] [Green Version]
- Verbyla, D.L.; Hammond, T.O. Conservative bias in classification accuracy assessment due to pixel-by-pixel comparison of classified images with reference grids. Int. J. Remote Sens. 1995, 16, 581–587. [Google Scholar] [CrossRef]
- Tang, Z.L.; Gu, S. An Evaluation of Social Performance in the Distribution of Urban Parks in the Central City of Shanghai: From Spatial Equity to Social Equity. J. Urban Plan. 2015, 2, 48–56. [Google Scholar] [CrossRef]
- Moran, P.A.P. Notes on continuous stochastic phenomena. Biometrika 1950, 37, 17–23. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, Y.; Zhang, Y.; Liu, Y.; Zhang, G.; Chen, Y. On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China. Sci. Total Environ. 2018, 637–638, 780–790. [Google Scholar] [CrossRef] [PubMed]
- Ye, Y.; Richards, D.; Lu, Y.; Song, X.; Zhuang, Y.; Zeng, W.; Zhong, T. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices. Landsc. Urban Plan. 2018, 191, 103434. [Google Scholar] [CrossRef]
- Sun, C.; Lin, T.; Zhao, Q.; Li, X.; Ye, H.; Zhang, G.; Zhao, Y. Spatial pattern of urban green spaces in a long-term compact urbanization process—A case study in China. Ecol. Indic. 2019, 96, 111–119. [Google Scholar] [CrossRef]
- Nagata, S.; Nakaya, T.; Hanibuchi, T.; Amagasa, S.; Kikuchi, H.; Inoue, S. Objective scoring of streetscape walkability related to leisure walking: Statistical modeling approach with semantic segmentation of Google Street View images. Health Place 2020, 66, 102428. [Google Scholar] [CrossRef]
- La Rosa, D.; Takatori, C.; Shimizu, H.; Privitera, R. A planning framework to evaluate demands and preferences by different social groups for accessibility to urban greenspaces. Sustain. Cities Soc. 2018, 36, 346–362. [Google Scholar] [CrossRef]
Buffer Distance | LQ | Count (Percentage) |
---|---|---|
5 min (360 m) | <0.2 | 3 (0.60) |
0.2–0.5 | 52 (10.42) | |
0.5–1.0 | 205 (41.08) | |
1.0–1.5 | 122 (24.45) | |
1.5–2.0 | 76 (15.23) | |
2.0–5.0 | 37 (7.42) | |
>5.0 | 4 (0.80) | |
10 min (720 m) | <0.2 | 1 (0.20) |
0.2–0.5 | 41 (8.22) | |
0.5–1.0 | 223 (44.69) | |
1.0–1.5 | 124 (24.85) | |
1.5–2.0 | 69 (13.83) | |
2.0–5.0 | 37 (7.41) | |
>5.0 | 4 (0.80) | |
15 min (1080 m) | <0.2 | 0 (0.00) |
0.2–0.5 | 41 (8.22) | |
0.5–1.0 | 220 (44.09) | |
1.0–1.5 | 135 (27.05) | |
1.5–2.0 | 66 (13.23) | |
2.0–5.0 | 33 (6.61) | |
>5.0 | 4 (0.80) | |
30 min (2160 m) | <0.2 | 0 (0.00) |
0.2–0.5 | 31 (6.21) | |
0.5–1.0 | 204 (40.88) | |
1.0–1.5 | 164 (32.87) | |
1.5–2.0 | 72 (14.43) | |
2.0–5.0 | 24 (4.81) | |
>5.0 | 4 (0.80) |
Buffer Distance | Moran’s I |
---|---|
5 min (360 m) | −0.057 *** |
10 min (720 m) | −0.080 *** |
15 min (1080 m) | −0.083 *** |
30 min (2160 m) | −0.096 *** |
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Luo, J.; Zhai, S.; Song, G.; He, X.; Song, H.; Chen, J.; Liu, H.; Feng, Y. Assessing Inequity in Green Space Exposure toward a “15-Minute City” in Zhengzhou, China: Using Deep Learning and Urban Big Data. Int. J. Environ. Res. Public Health 2022, 19, 5798. https://doi.org/10.3390/ijerph19105798
Luo J, Zhai S, Song G, He X, Song H, Chen J, Liu H, Feng Y. Assessing Inequity in Green Space Exposure toward a “15-Minute City” in Zhengzhou, China: Using Deep Learning and Urban Big Data. International Journal of Environmental Research and Public Health. 2022; 19(10):5798. https://doi.org/10.3390/ijerph19105798
Chicago/Turabian StyleLuo, Jingjing, Shiyan Zhai, Genxin Song, Xinxin He, Hongquan Song, Jing Chen, Huan Liu, and Yuke Feng. 2022. "Assessing Inequity in Green Space Exposure toward a “15-Minute City” in Zhengzhou, China: Using Deep Learning and Urban Big Data" International Journal of Environmental Research and Public Health 19, no. 10: 5798. https://doi.org/10.3390/ijerph19105798