Author(s)

Jinghan Zhang ¹, Luqi Shi ²

Affiliation(s)

¹ Greentown Architecture & Technology Group Co., LTD, Hangzhou, China
² Hangzhou Future City Underground Space Planning & Design Institute Co., LTD, Hangzhou, China

Corresponding Author

Jinghan Zhang

ABSTRACT

When carrying out urban renewal and related development projects, large-scale demolition and reconstruction in city centers inevitably present special challenges. Spatial quantitative analysis tools such as ArcGIS, Python, and depthMapX offer the opportunity to assess various urban development-related factors, including identifying which streets are most in need of renovation. In this paper, we employ space syntax analysis to design spatial intersections between low-choice streets and multipurpose land-use areas, as well as between high-choice streets and areas with low green visibility rates. Firstly, we use this approach to identify the low- and high-choice streets. Secondly, we use space information entropy to measure the degree of functionality mixing of land use, which is calculated based on POIs (Points of Interest) data. Thirdly, with the aid of semantic segmentation, this study uses street views to visualize the green visibility rates of specific regions. After intersecting spatially by ArcGIS, we are able to determine which areas should be given priority consideration for urban street renewal.

KEYWORDS

Space syntax, POIs, space information entropy, semantic segmentation, green visibility rate

CITE THIS PAPER

Jinghan Zhang, Luqi Shi, Spatial Intersection Analysis Based on the Choice of Streets, the Functionality Mixing Degree of Land Use and the Green Visibility Rate by Street Views—Taking the City Center in Shanghai as the Research Area. Journal of Civil Engineering and Urban Planning (2023) Vol. 5: 52-58. DOI: http://dx.doi.org/10.23977/jceup.2023.050507.

REFERENCES

[1] Yang, P. (2023) Connotation logic and practice orientation of urban renewal from the perspective of high quality development [J/OL]. Modern Economic Science. https://kns.cnki.net/kcms/detail/61.1400.F.20230413.2105.004.html
[2] Duan, J., Hillier, B., Shao, R., Dai, X., Read, S., Ye, M., Jiang, B., Hong, L., Sheng, Q. (2007) Urban space 3 --space syntax and urban planning. Nanjing: Southeast University Press
[3] Hillier, B., Burdett, R., Peponis, J., Penn, A. (1987) Creating Life: Or, Does Architecture Determine Anything? Architecture & Comportment/Architecture and Behaviour. 3(3): 233-250
[4] Griffiths, S., Quick, T. (2005) How the individual, society and space become structurally coupled over time. van Nes, A., (ed.) Proceedings of the 5th Space Syntax Symposium, June 2005: Volume 2. Amsterdam, The Netherlands: Techne Press, pp. 447-458. ISBN 9085940028. https://www.researchgate.net/publication/32897365
[5] Long, Y., Liu, X. (2013) How Mixed is Beijing, China? A Visual Exploration of Mixed Land Use. Environment and Planning A, 2013, 45:2797–2798.
[6] Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, p. 379-423, 623-656.
[7] Li, M., Ma, Y., Sun, X., Wang, J., Dang, A. (2018) Application of spatial and temporal entropy based on multi-source data for measuring the mix degree of urban functions. City Planning Review.
[8] Yang, J., Zhao, L., Mcbride, J., Gong, P. (2009) Can you see green? Assessing the visibility of urban forests in cities. Landscape and Urban Planning. https://www.sciencedirect.com/science/article/pii/S0169204608002314
[9] Li, X., Zhang, C., Li, W., Ricard, R., Meng, Q., Zhang, W. (2015) Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry & Urban Greening. https: // www. sciencedirect. com/ science/ article/pii/S1618866715000874
[10] Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H.H., Lin, H., Ratti, C. (2018) Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning. https: // doi. org/ 10. 1016/ j. landurbplan. 2018. 08.020
[11] Aikoh, T., Homma T., Abe Y. (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 
[12] Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., Torralba, A. (2018) Semantic Understanding of Scenes through ADE20K Dataset. International Journal on Computer Vision (IJCV). https://arxiv.org/pdf/1608.05442.pdf
[13] Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., MartinezGonzalez, P., Garcia-Rodriguez, J. (2018) A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing.
[14] Hansen, W.G. (1959) How accessibility shapes land use. Journal of the American Institute of Planners, 25, 73-76
[15] Dai, D. (2011) Racial/ethnic and socioeconomic disparities in urban green space accessibility: Where to intervene? Landscape and Urban Planning. Volume 102, Issue 4, 30 September 2011, Pages 234-244
[16] Chen, W., Cheng, L., Chen, X., Chen, J., Cao, M. (2021) Measuring accessibility to health care services for older bus passengers: A finer spatial resolution. Journal of Transport Geography. Volume 93, May 2021, 103068
[17] Tang, J., 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. https: // doi. org/ 10. 1016/j. landurbplan. 2018. 09.015

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