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Classification of Metro Station Areas Using Multi-Source Big Data: Case Studies in Beijing

  • Shuo Chen (Faculty of Architecture, Civil And Transportation Engineering, Beijing University of Technology) ;
  • Xiangyu Li (Faculty of Architecture, Civil And Transportation Engineering, Beijing University of Technology)
  • Published : 2023.03.01

Abstract

Large-capacity public transportation systems, represented by urban metro lines, are the key to alleviating the significant increase in urbanization and motorization in China. But to improve the agglomeration effect of metro stations in a more accurate and targeted way requires scientific evaluation and classification of the surrounding areas of metro stations. As spatial and functional design are the core factors for urban renewal design, this study took Beijing as an example, using multi-source data to evaluate the morphology and functional composition surrounding areas of metro stations, and the Boston Consulting Group (BCG) matrix was used to classify and characterize each type of surrounding areas from morphological-functional dimensions. It shows a negative correlation of the mix-use index with the floor area ratio, and only about 20% of the areas achieve the ideal situation of high construction intensity with high mix-use diversity. Hoping to provide a reference for city managers and designers in dealing with the surrounding metro stations with different construction intensities in a more precise way.

Keywords

Acknowledgement

This research is supported by the National Natural Science Foundation (52178001); Beijing Natural Science Foundation (8222006); Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX1545); Beijing Postdoctoral Work Foundation (2021-ZZ-107); The International Postdoctoral Exchange Fellowship Program(PC2021007).

References

  1. Baldwin J. (1993). The Next American Metropolis: Ecology, Community, and the American Dream. - book reviews. Whole Earth Review, (12):34-35.
  2. Bao, Y. (2016). Research on the measurement of mixed urban land use-Shenzhen city as an example. Hubei Agricultural Science, 55(22):5794-5797+5801.
  3. C. J. Keylock.(2005). Simpson diversity and the Shannon-Wiener index as special cases of a generalized entropy[J]. Oikos,109(1).
  4. Chen Yilin,Qian Jiahuan. (2020). Study on Urban Form and Slow Travel Adaptability in Rail Transit Station Area: A Case Study of Huangpu Road Subway Station in Wuhan City. Traffic Governance and Spatial Remodeling - Proceedings of the 2020 China Urban Transportation Planning Annual Conference.
  5. Dag Oivind Madsen (2017). Not dead yet: the rise, fall and persistence of the BCG Matrix. Problems and Perspectives in Management, 15(1), 19-34. https://doi.org/10.21511/ppm.15(1).2017.02
  6. Duan Degang, Zhang Fan. (2013). Study on the classification of urban rail stations from the perspective of land use optimization - Taking Xi'an Metro Line 2 as an example. Urban Planning, (9): 39-45.
  7. Gutierrez J , Cardozo O D, Garcia-Palomares J C. (2021) . Transit ridership forecasting at station level: an approach based on distance-decay weighted regression[J]. Journal of Transport Geography, 19(6):p.1081-1092.
  8. Jian Rong, Yang Bian, Yi Wang. (2022). Concordance between Regional Functions and Mobility Features Using Bikesharing and Land-use Data near Metro Stations,Sustainable Cities and Society,Volume 84,2022,104010,
  9. K.C.L. K. (1995). Price Impact from the Subway Construction. Seoul: Graduate School of Public Administration, 44-47.
  10. Li Zuyu. (2016) Development and Design Study of Urban Rail Transit Station "Quasi Complex" Based on the Phenomenon and Evaluation of Urban Rail Transit Station & Residence Jonit-built. Chongqing University
  11. Li, GD. (2021) Research on land use mix analysis and layout optimization around rail transit stations based on POI data. Chang'an University.
  12. Lin, Yan-Yu. (2015) Adaptation evaluation and optimization research of rail transit station impact domain based on pedestrian microsimulation. Chongqing University, 2015.
  13. Mercado R, Paez A. (2009). Determinants of distance traveled with a focus on the elderly: a multilevel analysis in the Hamilton CMA, Canada. Journal of Transport Geography, 17(1):65-76. https://doi.org/10.1016/j.jtrangeo.2008.04.012
  14. Pan, H., Liu, H. T., John Zacharias, et al. (2003). Neighborhood design features and green transportation choices: A case study of four neighborhoods in Shanghai: Kangjian, Luwan, Zhongyuan, and Yaohan. Journal of Urban Planning, 000(006):42-48.
  15. Pan, H., Ren, C., Yang, G. (2007). An empirical study on the impact of rail transit on land use in station areas in Shanghai. Journal of Urban Planning, (004): 92-97.
  16. Suo Wenwen. (2019) A study on the mixed functions of the waterfront in the middle section of Shanghai Huangpu River based on POI. Beijing Forestry University.
  17. Tilles, S. (1966). Strategies for allocating funds, Harvard Business Review, 44 (1), pp. 72-80. Volume 84,104010
  18. Wu PY, YU CM. (2020). Using the Walkability Environment Analysis Method to Shape the Basic Unit of Walkable City: The Case Study from Beijing Huilongguan Residential Area. Journal of Human Settlements in West China, 35(5): 91-99.
  19. XY Gao. (2013). Research on the fusion problem of multisource POI data based on spatial location information. Ocean University of China.
  20. Yi Wang. (2022). Concordance between Regional Functions and Mobility Features Using Bike-sharing and Land-use Data near Metro Stations,Sustainable Cities and Society,
  21. Yin Chaoying, Shao Chunfu, Wang Xiaoquan. (2018). Impact of land use mix on travel mode choice. Journal of Beijing University of Technology, v.44(09):67-72.
  22. Yu, Tianshu. (2012) Research on Integrated Development Model of Metro Station Domain. Tianjin University.
  23. Zeng Ruth, Shen Zhongwei, Luo Keqian. (2020). Study on the characteristics and evolution of commercial agglomeration in rail transit station domain - an empirical analysis based on POI data. Southern Architecture, (006): 000.
  24. Zhang Lingzhu,Alain Chiaradia. (2020). Study on the evaluation of TOD effectiveness of rail transit station area in the context of big data - taking Hong Kong as an example. Architectural Techniques,26(09):85-89.
  25. Zhang Zhening, Wang Shuling, Sun Fuliang,et al. (2019). Study on the sphere of influence of urban rail transit stations in the context of refined data - A case study of Beijing City 2019 Annual Conference on Urban Transportation Planning in China.
  26. Zhao, Guihua, Zhang, R. L. (2016). Discussion on the integrated development mode of subway station and surrounding underground space. Architectural Techniques, (08): 111-115.