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Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Author

Listed:
  • Zhou Huang
  • Ganmin Yin
  • Xia Peng
  • Xiao Zhou
  • Quanhua Dong

Abstract

Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R 2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning.

Suggested Citation

  • Zhou Huang & Ganmin Yin & Xia Peng & Xiao Zhou & Quanhua Dong, 2023. "Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data," Environment and Planning B, , vol. 50(9), pages 2470-2490, November.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:9:p:2470-2490
    DOI: 10.1177/23998083231158370
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    1. C. von Ferber & T. Holovatch & Yu. Holovatch & V. Palchykov, 2009. "Public transport networks: empirical analysis and modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 68(2), pages 261-275, March.
    2. Arbués, Pelayo & Baños, José F. & Mayor, Matías & Suárez, Patricia, 2016. "Determinants of ground transport modal choice in long-distance trips in Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 131-143.
    3. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    4. Jasper Grashuis & Theodoros Skevas & Michelle S. Segovia, 2020. "Grocery Shopping Preferences during the COVID-19 Pandemic," Sustainability, MDPI, vol. 12(13), pages 1-10, July.
    5. Felipa de Mello-Sampayo, 2017. "Competing-destinations gravity model applied to trade in intermediate goods," Applied Economics Letters, Taylor & Francis Journals, vol. 24(19), pages 1378-1384, November.
    6. Andres Gomez-Lievano & Oscar Patterson-Lomba & Ricardo Hausmann, 2016. "Explaining the Prevalence, Scaling and Variance of Urban Phenomena," CID Working Papers 329, Center for International Development at Harvard University.
    7. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
    8. Teller, Christoph & Reutterer, Thomas, 2008. "The evolving concept of retail attractiveness: What makes retail agglomerations attractive when customers shop at them?," Journal of Retailing and Consumer Services, Elsevier, vol. 15(3), pages 127-143.
    9. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    10. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    11. Abhijit Chakraborty & Hazem Krichene & Hiroyasu Inoue & Yoshi Fujiwara, 2019. "Exponential random graph models for the Japanese bipartite network of banks and firms," Journal of Computational Social Science, Springer, vol. 2(1), pages 3-13, January.
    12. Kang, Yuhao & Zhang, Fan & Peng, Wenzhe & Gao, Song & Rao, Jinmeng & Duarte, Fabio & Ratti, Carlo, 2021. "Understanding house price appreciation using multi-source big geo-data and machine learning," Land Use Policy, Elsevier, vol. 111(C).
    13. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
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