IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p647-d478689.html
   My bibliography  Save this article

Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing

Author

Listed:
  • Ruomu Miao

    (School of Media and Communication, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yuxia Wang

    (School of Geographic Sciences, Key Lab of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China)

  • Shuang Li

    (Institute of Chinese Historical Geography, Fudan University, Shanghai 200433, China)

Abstract

With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

Suggested Citation

  • Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:647-:d:478689
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/647/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/2/647/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Younghoon Seo & Donghyun Lim & Woongbee Son & Yeongmin Kwon & Junghwa Kim & Hyungjoo Kim, 2020. "Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    2. Guang Yang & Yan Han & Hao Gong & Tiantian Zhang, 2020. "Spatial-Temporal Response Patterns of Tourist Flow under Real-Time Tourist Flow Diversion Scheme," Sustainability, MDPI, vol. 12(8), pages 1-28, April.
    3. Yu Liu & Zhengwei Sui & Chaogui Kang & Yong Gao, 2014. "Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    4. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    5. Wangbao Liu & Quan Hou & Zhihao Xie & Xin Mai, 2020. "Urban Network and Regions in China: An Analysis of Daily Migration with Complex Networks Model," Sustainability, MDPI, vol. 12(8), pages 1-12, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ya Li & Chunxia Liu & Yuechen Li, 2022. "Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses," Land, MDPI, vol. 11(7), pages 1-17, June.
    2. Mingyan Ni & Yindi Zhao & Caihong Ma & Xiaolin Hou & Yanmei Xie, 2023. "Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    3. Jiaxin Zhang & Zhilin Yu & Yunqin Li & Xueqiang Wang, 2023. "Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception," Land, MDPI, vol. 12(7), pages 1-20, June.
    4. I-Cheng Chang & Tai-Kuei Yu & Yu-Jie Chang & Tai-Yi Yu, 2021. "Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    5. Yong Huang & Qinjun Kang & Qi Wang & Lili Luo & Tingting Wang & Qingrui Chang, 2022. "Multiscale Spatial Distribution Pattern and Influencing Factors on Inland Fishing Gardens in China," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    6. Guowei Luo & Jiayuan Ye & Jinfeng Wang & Yi Wei, 2023. "Urban Functional Zone Classification Based on POI Data and Machine Learning," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    7. Yao Chang & Dongbing Li & Zibibula Simayi & Shengtian Yang & Maliyamuguli Abulimiti & Yiwei Ren, 2022. "Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest," IJERPH, MDPI, vol. 19(13), pages 1-18, June.
    8. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    9. Fangjie Cao & Yun Qiu & Qianxin Wang & Yan Zou, 2022. "Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    10. Zhenwei Wang & Xiaochun Wang & Zijin Dong & Lisan Li & Wangjun Li & Shicheng Li, 2023. "More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China," Land, MDPI, vol. 12(1), pages 1-13, January.
    11. Oliveira, Renata Lúcia Magalhães de & Dablanc, Laetitia & Schorung, Matthieu, 2022. "Changes in warehouse spatial patterns and rental prices: Are they related? Exploring the case of US metropolitan areas," Journal of Transport Geography, Elsevier, vol. 104(C).
    12. El Bachir Diop & Jérôme Chenal & Stéphane Cédric Koumetio Tekouabou & Rida Azmi, 2022. "Crowdsourcing Public Engagement for Urban Planning in the Global South: Methods, Challenges and Suggestions for Future Research," Sustainability, MDPI, vol. 14(18), pages 1-21, September.

    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. Ji Han & Jiabin Liu, 2018. "Urban Spatial Interaction Analysis Using Inter-City Transport Big Data: A Case Study of the Yangtze River Delta Urban Agglomeration of China," Sustainability, MDPI, vol. 10(12), pages 1-16, November.
    2. Bilong Shen & Weimin Zheng & Kathleen M. Carley, 2018. "Urban Activity Mining Framework for Ride Sharing Systems Based on Vehicular Social Networks," Networks and Spatial Economics, Springer, vol. 18(3), pages 705-734, September.
    3. Yaxin Fan & Xinyan Zhu & Bing She & Wei Guo & Tao Guo, 2018. "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    4. Hongwen Xia & Rengkui Liu & Wei Zhou & Wenhui Luo, 2024. "Modeling the Causes of Urban Traffic Crashes: Accounting for Spatiotemporal Instability in Cities," Sustainability, MDPI, vol. 16(20), pages 1-16, October.
    5. Wang, Cheng & Wang, Gang & Guo, Ziru & Dai, Lingjun & Liu, Hongyu & Li, Yufeng & Chen, Hao & Zhao, Yongxiang & Zhang, Yanan & Cheng, Hai, 2020. "Effects of land-use change on the distribution of the wintering red-crowned crane (Grus japonensis) in the coastal area of northern Jiangsu Province, China," Land Use Policy, Elsevier, vol. 90(C).
    6. Mert Ersen & Ali Hakan Büyüklü & Semra Taşabat Erpolat, 2021. "Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods," Sustainability, MDPI, vol. 13(19), pages 1-39, October.
    7. Liu, Zhaoyang & Hanley, Nick & Campbell, Danny, 2020. "Linking urban air pollution with residents’ willingness to pay for greenspace: A choice experiment study in Beijing," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    8. Karolina Dudzic-Gyurkovich, 2023. "Study of Centrality Measures in the Network of Green Spaces in the City of Krakow," Sustainability, MDPI, vol. 15(18), pages 1-30, September.
    9. Qing Ye & Yi Li & Wenzhe Shen & Zhaoze Xuan, 2023. "Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    10. Younes, Hannah & Nasri, Arefeh & Baiocchi, Giovanni & Zhang, Lei, 2019. "How transit service closures influence bikesharing demand; lessons learned from SafeTrack project in Washington, D.C. metropolitan area," Journal of Transport Geography, Elsevier, vol. 76(C), pages 83-92.
    11. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    12. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    13. Tianzheng Xiao & Huapu Lu & Jianyu Wang & Katrina Wang, 2021. "Predicting and Interpreting Spatial Accidents through MDLSTM," IJERPH, MDPI, vol. 18(4), pages 1-18, February.
    14. Delso, Javier & Martín, Belén & Ortega, Emilio, 2018. "A new procedure using network analysis and kernel density estimations to evaluate the effect of urban configurations on pedestrian mobility. The case study of Vitoria –Gasteiz," Journal of Transport Geography, Elsevier, vol. 67(C), pages 61-72.
    15. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    16. Rishuang Sun & Chi Zhang & Yujie Xiang & Lei Hou & Bo Wang, 2022. "Identification Method for Crash-Prone Sections of Mountain Highway under Complex Weather Conditions," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    17. Chen, Yanguang, 2015. "The distance-decay function of geographical gravity model: Power law or exponential law?," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 174-189.
    18. Yeran Sun & Hongchao Fan & Ming Li & Alexander Zipf, 2016. "Identifying the city center using human travel flows generated from location-based social networking data," Environment and Planning B, , vol. 43(3), pages 480-498, May.
    19. Loidl, Martin & Traun, Christoph & Wallentin, Gudrun, 2016. "Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)," Journal of Transport Geography, Elsevier, vol. 52(C), pages 38-50.
    20. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.

    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:13:y:2021:i:2:p:647-:d:478689. 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.