IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v50y2023i9p2470-2490.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/23998083231158370
    Download Restriction: no
    File URL: https://libkey.io/10.1177/23998083231158370?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Andres Gomez-Lievano & Oscar Patterson-Lomba & Ricardo Hausmann, 2017. "Explaining the prevalence, scaling and variance of urban phenomena," Nature Human Behaviour, Nature, vol. 1(1), pages 1-6, January.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    2. Selima Sultana & Nastaran Pourebrahim & Hyojin Kim, 2018. "Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    3. Paul Harris & Bruno Lanfranco & Binbin Lu & Alexis Comber, 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    4. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Teller, David, 2020. "Study design impacts on built environment and transit use research," Journal of Transport Geography, Elsevier, vol. 82(C).
    5. Gong, Wenjing & Rui, Jin & Li, Tianyu, 2024. "Deciphering urban bike-sharing patterns: An in-depth analysis of natural environment and visual quality in New York's Citi bike system," Journal of Transport Geography, Elsevier, vol. 115(C).
    6. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    7. Giron Cima, Elizabeth & Freire da Rocha-Junior, Weimar & Angel Uribe-Opazo, Miguel & Henrique Dalposso, Gustavo, 2023. "An Analysis of the Gross Domestic Product of Municipalities: a Spatial Glance into the State of Paraná-Brazil," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(2), June.
    8. Peter Congdon, 2020. "Geographical Aspects of Recent Trends in Drug-Related Deaths, with a Focus on Intra-National Contextual Variation," IJERPH, MDPI, vol. 17(21), pages 1-18, November.
    9. Yujiao Chen & Zhengbo Luo, 2022. "Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    10. Mehmet Burak Kaya & Onur Alisan & Alican Karaer & Eren Erman Ozguven, 2024. "Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach," Sustainability, MDPI, vol. 16(3), pages 1-27, January.
    11. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    12. Alexis Comber & Khanh Chi & Man Q Huy & Quan Nguyen & Binbin Lu & Hoang H Phe & Paul Harris, 2020. "Distance metric choice can both reduce and induce collinearity in geographically weighted regression," Environment and Planning B, , vol. 47(3), pages 489-507, March.
    13. Cynthia Sin Tian Ho & Mats Wilhelmsson, 2022. "Geographical accessibility to bank branches and its relationship to new firm formation in Sweden via multiscale geographically weighted regression," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(2), pages 191-218, August.
    14. Mühlematter, Dominik J. & Wiedemann, Nina & Xin, Yanan & Raubal, Martin, 2024. "Spatially-aware station based car-sharing demand prediction," Journal of Transport Geography, Elsevier, vol. 114(C).
    15. Cui, Wencong & Li, Jianyi & Xu, Wangtu & Güneralp, Burak, 2021. "Industrial electricity consumption and economic growth: A spatio-temporal analysis across prefecture-level cities in China from 1999 to 2014," Energy, Elsevier, vol. 222(C).
    16. Guanwei Zhao & Zhitao Li & Yuzhen Shang & Muzhuang Yang, 2022. "How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales?," IJERPH, MDPI, vol. 19(9), pages 1-25, April.
    17. Batac, Rene C. & Cirunay, Michelle T., 2022. "Shortest paths along urban road network peripheries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    18. Liu, Xueli & Jiang, Chunxia & Wang, Feng & Yao, Shujie, 2021. "The impact of high-speed railway on urban housing prices in China: A network accessibility perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 84-99.
    19. Yanjie Zhang & Bilal M. Ayyub & Dongming Zhang & Hongwei Huang & Yalda Saadat, 2019. "Impact of Water Level Rise on Urban Infrastructures: Washington, DC, and Shanghai as Case Studies," Risk Analysis, John Wiley & Sons, vol. 39(12), pages 2718-2731, December.
    20. Chih-Hao Wang & Na Chen, 2021. "A multi-objective optimization approach to balancing economic efficiency and equity in accessibility to multi-use paths," Transportation, Springer, vol. 48(4), pages 1967-1986, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:50:y:2023:i:9:p:2470-2490. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.