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Identifying Urban Functional Regions by LDA Topic Model with POI Data

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Big Data (BigData 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1709))

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Abstract

Identifying Urban Functional Regions (UFR) can achieve the rational aggregation of social resource space, realize urban economic and social functions, promote the deployment of urban infrastructure, radiate and drive the development of surrounding regions, so the identification of urban functional regions can promote the efficient development of cities. However, the traditional functional region identification method is mainly based on remote sensing mapping, which relies more on the natural geographical characteristics of the region to describe and identify the region, while the urban functional region is closely related to human activities, and the traditional functional region identification results are not ideal. Social data includes a series of data that reflect people’s activities and behaviors, such as trajectory data, social media data, and travel data, thus the analysis of social data can more effectively solve the difficulties of traditional mapping and identification. POI (Point of Interest) data, as a typical type of social data, can be used to identify urban functional regions. We apply the LDA topic model to the POI data, and propose a new urban functional region identification method, which makes full use of the POI data to reflect the activity categories of urban populations to characterize the features of regional functions and achieve a high degree of identification of urban functional regions. Through experimental verification on real data, the experimental results show that the proposed method can more accurately identify urban functions, which proves the method reliable.

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Acknowledgement

The work is supported by the National Natural Science Foundation of China under Grant No. 61972317, No. 61972318, the Natural Science Foundation of Shaanxi Province of China under Grant No. 2021JM068, the Shaanxi Province Training Program of Innovation and Entrepreneurship for Undergraduates under Grant No. S202110699625.

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Yuhao Huang, Lijun Zhang, Haijun Wang & Siqi Wang

  2. Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, China

    Yuhao Huang, Lijun Zhang, Haijun Wang & Siqi Wang

Authors
  1. Yuhao Huang
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  2. Lijun Zhang
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  3. Haijun Wang
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  4. Siqi Wang
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Corresponding author

Correspondence to Lijun Zhang .

Editor information

Editors and Affiliations

  1. Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  2. Shenzhen University, Shenzhen, China

    Rui Mao

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  4. Southwest Jiaotong University, Chengdu, China

    Fei Teng

  5. Sichuan University, Chengdu, China

    Lei Zhang

  6. Taiyuan University of Technology, Taiyuan, China

    Li Wang

  7. Shenzhen University, Shenzhen, China

    Laizhong Cui

  8. Southwest Jiaotong University, Chengdu, China

    Jie Hu

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Huang, Y., Zhang, L., Wang, H., Wang, S. (2022). Identifying Urban Functional Regions by LDA Topic Model with POI Data. In: Li, T., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1709. Springer, Singapore. https://doi.org/10.1007/978-981-19-8331-3_5

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  • DOI: https://doi.org/10.1007/978-981-19-8331-3_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8330-6

  • Online ISBN: 978-981-19-8331-3

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