IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4205-d1080885.html

Big Data Application in Urban Commercial Center System Evaluation

Author

Listed:
  • Xinyu Liu

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
    Jiangsu Collaborative Innovation Center for Building Energy Saving and Construction Technology, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221116, China)

  • Yibing Guan

    (Jiangsu Provincial Planning and Design Group, Nanjing 213004, China)

  • Zihan Wu

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Lufeng Nie

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Xiang Ji

    (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
    Jiangsu Collaborative Innovation Center for Building Energy Saving and Construction Technology, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221116, China)

Abstract

Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the spatial structure system of “one main core, five secondary cores, multiple district cores, three horizontal axes, and one vertical axis.” Meanwhile, a public questionnaire was used to evaluate the public’s perception of the commercialization level in Nanjing. Finally, the results obtained were used for comparison with the structure of the commercial center system of Nanjing commercial network planning. We discovered that the results of the public’s perception of the commercialization level in Nanjing were similar to those of the big data analysis, which confirmed the credibility of big data analysis results. In conclusion, the findings of this study provide a basis for developing urban commercial center-level positioning and propose a method for data-assisted planning research.

Suggested Citation

  • Xinyu Liu & Yibing Guan & Zihan Wu & Lufeng Nie & Xiang Ji, 2023. "Big Data Application in Urban Commercial Center System Evaluation," Sustainability, MDPI, vol. 15(5), pages 1-25, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4205-:d:1080885
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4205/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4205/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang Wang & Zhao Liu & Shanshan Shang & Yuelei Qin & Bihu Wu, 2019. "Vitality continuation or over-commercialization? Spatial structure characteristics of commercial services and population agglomeration in historic and cultural areas," Tourism Economics, , vol. 25(8), pages 1302-1326, December.
    2. Changhui Hu & Weidong Liu & Yuqiu Jia & Yaya Jin, 2019. "Characterization of Territorial Spatial Agglomeration Based on POI Data: A Case Study of Ningbo City, China," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    3. Fang Wang & Fang-qu Niu, 2019. "Urban Commercial Spatial Structure Optimization in the Metropolitan Area of Beijing: A Microscopic Perspective," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    4. Wang, Yang & Zhang, Hong & Liu, Libing, 2022. "Does city construction improve life quality?-evidence from POI data of China," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 643-653.
    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. Peng Zeng & Zongyao Sun & Yuqi Chen & Zhi Qiao & Liangwa Cai, 2021. "COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    2. Bahram Zikirya & Chunshan Zhou, 2023. "Spatial Distribution and Influencing Factors of High-Level Tourist Attractions in China: A Case Study of 9296 A-Level Tourist Attractions," Sustainability, MDPI, vol. 15(19), pages 1-18, September.
    3. Gangmin Weng & Hongyan Li & Yan Li, 2023. "The temporal and spatial distribution characteristics and influencing factors of tourist attractions in Chengdu-Chongqing economic circle," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8677-8698, August.
    4. Chunyu Jiang & Seuk Wai Phoong, 2023. "A ten-year review analysis of the impact of digitization on tourism development (2012–2022)," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    5. Yang Lin & William Thackway & Balamurugan Soundararaj & Serryn Eagleson & Hoon Han & Christopher Pettit, 2026. "Transforming urban planning through machine learning: A study on planning application classification using natural language processing," Environment and Planning B, , vol. 53(1), pages 11-31, January.
    6. Feng, Guoqiang & Wang, Tianle & Huang, Zihong, 2025. "Measuring the impacts of subway openings on location choice: Systematic evidence from service enterprises, Beijing," Journal of Transport Geography, Elsevier, vol. 123(C).
    7. Jiangang Li & Songhong Li & Jun Lei & Xiaolei Zhang & Jianwei Qi & Buayxam Tohti & Zuliang Duan, 2022. "Analysis of Spatial Structure in the Kashgar Metropolitan Area, China," Land, MDPI, vol. 11(6), pages 1-22, May.
    8. Wanjau Christopher Kinyua & Gichuhi D. M & K. Kiambati, 2023. "Motorcycles delivery innovation and performance of wholesale retail sector in Kenya," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 12(5), pages 456-462, July.
    9. Fangqu Niu & Fang Wang, 2021. "How Does China’s New Consumption Era Reshape Residents’ Shopping Behaviors from the Perspective of Community in Hohhot, China," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    10. Yuyu Wu & Jia Chen, 2023. "Spatial Distribution Heterogeneity and Influencing Factors of Different Leisure Agriculture Types in the City," Agriculture, MDPI, vol. 13(9), pages 1-20, August.
    11. Pengfei Ban & Wei Zhan & Qifeng Yuan & Xiaojian Li, 2021. "Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
    12. Sun, Yong & Tang, Lixia & Jiang, Xingling, 2025. "Combating over-commercialization in ecotourism through collaborative governance: A game-theoretic modeling analysis," Ecological Modelling, Elsevier, vol. 508(C).
    13. Wijesinghe, Dilnu Chanuwan & Sun, Pingjun & Liu, Zhigao, 2026. "Spatial evolution characteristics and driving factors of urban forms in the Colombo Metropolitan Area (CMA), Sri Lanka: A geo-informatics approach," Land Use Policy, Elsevier, vol. 162(C).
    14. Gang Xu & Yuxin Liao & Yixin Jiang & Peiyao Xu & Lilin Yang & Wenhua Huang & Manru Zhang & Rong Wu, 2022. "The Impacts of Urban Environments on Community Trust of the Low-Income Group: A Case Study for the Pearl River Delta Region," Land, MDPI, vol. 12(1), pages 1-14, December.
    15. Li Yang & Yung‐ho Chiu & Tzu‐Han Chang, 2023. "The impact of quality of life on industrial and agricultural production and environmental efficiency in China's provinces," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 1054-1072, March.
    16. Liangkai Song & Rosniza Aznie CR & Rosmiza MZ & Sisi Chen, 2025. "Reconstructing high-quality tourism spatial structure: an empirical study of Shanxi Province from the perspective of supply and demand," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 74(3), pages 1-28, September.
    17. Guoping Xiong & Xin Cao & Nicholas A. S. Hamm & Tao Lin & Guoqin Zhang & Binghong Chen, 2021. "Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial Form: A Case Study of Jiangsu Province, China," Sustainability, MDPI, vol. 13(6), pages 1-39, March.
    18. Dani Broitman & Eric Koomen, 2020. "The attraction of urban cores: Densification in Dutch city centres," Urban Studies, Urban Studies Journal Limited, vol. 57(9), pages 1920-1939, July.
    19. Mingrui Miao & Lyuhang Feng & Yao Wu & Rong Zhu & Dawei Xu, 2024. "Color Authenticity for the Sustainable Development of Historical Areas: A Case Study of Shiquan," Sustainability, MDPI, vol. 16(6), pages 1-24, March.
    20. Anqi Lei & Li Yang & Yung-ho Chiu & Yuan-yuan Niu & Tzu-Han Chang, 2025. "Reassessment of the efficiency of China's industrial circular economy considering quality of life: a meta recycle SBM dynamic under exogenous model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(8), pages 19435-19466, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:15:y:2023:i:5:p:4205-:d:1080885. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.