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Identification of Urban Functional Areas Based on Point of Interest data and Thiessen Polygons for a Sustainable Urban Management

Author

Listed:
  • Mehmet Fatih Döker

    (Sakarya University)

  • Ahmet Gül

    (Faculty of Humanities and Social Sciences, Kilis 7 Aralık University)

  • Cem Kırlangıçoğlu

    (Sakarya University)

  • Fatih Ocak

    (Samsun University)

  • Masoud Minaei

    (Ferdowsi University of Mashhad)

Abstract

In rapidly developing urban areas, land use changes frequently occur in conjunction with population growth. The determination of the existing land use structure via traditional methods, including field studies and remote sensing techniques, is an exceedingly time-consuming, costly, and labor-intensive process. To address this issue, this paper proposes an innovative model based on Point of Interest (POI) data, Thiessen polygons and Geographic Information Systems (GIS) in the process of identifying land use functions. The model’s workflow comprises data collection, spatial geodatabase design, data pre-processing, the construction of geoprocessing workflows with ModelBuilder, analysis, urban function identification, and model verification steps. This study employs Thiessen polygons to analyze the topological and spatial relationships among 127,265 geotagged POIs in Ankara, the capital of Türkiye. The model achieved a Kappa value of 0.82 and an overall accuracy of 84.5%, demonstrating a high level of reliability. Following this analysis, functional density ratios were calculated based on the distribution of POIs to identify areas characterized by either dominant or mixed land use. The proposed methodology is expected to contribute significantly to urban planning efforts by providing insights into the utilization patterns of urban land. Furthermore, this model has the potential to function as a decision support system, aiding city planners in the effective management and development of urban spaces by delineating existing functional areas.

Suggested Citation

  • Mehmet Fatih Döker & Ahmet Gül & Cem Kırlangıçoğlu & Fatih Ocak & Masoud Minaei, 2025. "Identification of Urban Functional Areas Based on Point of Interest data and Thiessen Polygons for a Sustainable Urban Management," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 176(3), pages 1071-1092, February.
    • Handle: RePEc:spr:soinre:v:176:y:2025:i:3:d:10.1007_s11205-024-03502-9
    DOI: 10.1007/s11205-024-03502-9
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    References listed on IDEAS

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    1. Shuwei Wang & Lishan Sun & Jian Rong & Zifan Yang, 2014. "Transit Traffic Analysis Zone Delineating Method Based on Thiessen Polygon," Sustainability, MDPI, vol. 6(4), pages 1-12, April.
    2. Zakaria A. Mani & Krzysztof Goniewicz, 2023. "Adapting Disaster Preparedness Strategies to Changing Climate Patterns in Saudi Arabia: A Rapid Review," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    3. 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.
    4. 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.
    5. Mohammad Amerian, 2024. "Identifying the Impact of Social Capital on Quality of Urban Life (Evidence from Iran)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 171(3), pages 921-936, February.
    6. Yunfeng Hu & Yueqi Han, 2019. "Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone," Sustainability, MDPI, vol. 11(5), pages 1-15, March.
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    Cited by:

    1. Minli Huang & Junlin Huang & Wanqing Zhang & Qiao Chen, 2025. "Multi-Pattern Characteristics and Driving Mechanisms of Mixed Land Use: A Case Study of Changsha’s Built-Up Areas, China," Land, MDPI, vol. 14(4), pages 1-26, April.

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