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Exploring the tidal effect of urban business district with large-scale human mobility data

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Abstract

Business districts are urban areas that have various functions for gathering people, such as work, consumption, leisure and entertainment. Due to the dynamic nature of business activities, there exists significant tidal effect on the boundary and functionality of business districts. Indeed, effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city. However, with the implicit and complex nature of business district evolution, it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts. To this end, we propose a data-driven and multi-dimensional framework for dynamic business district analysis. Specifically, we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods. Then, we detect and forecast the functional changes in business districts. Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts. Moreover, the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts. For example, the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

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Acknowledgements

The research work was supported by State Key Laboratory of Software Development Environment (SKLSDE-2021ZX-19, SKLSDE-2020ZX-02).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Hongting Niu, Cong Geng & Bo Lang

  2. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100086, China

    Ying Sun

  3. Baidu Talent Intelligence Center, Baidu Inc., Beijing, 100085, China

    Ying Sun & Hengshu Zhu

  4. Business School, Imperial College London, London, SW72AZ, UK

    Jiuchun Yang

  5. Artificial Intelligence Thrust, The Hong Kong University of Science and Technology, Guangzhou, 510030, China

    Hui Xiong

Authors
  1. Hongting Niu
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  2. Ying Sun
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  3. Hengshu Zhu
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  4. Cong Geng
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  5. Jiuchun Yang
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  6. Hui Xiong
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  7. Bo Lang
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Corresponding author

Correspondence to Hongting Niu.

Additional information

Hongting Niu is currently a teacher in School of Computer Science and Engineering, Beihang University (BUAA), China. She received her Master degree in computer science and technology from Beihang University and is currently a PhD Candidate in School of Computer Science and Engineering, Beihang University, China. Her research interests include artificial intelligence, data mining, and machine learning, with a focus on human mobility and spatio-temporal data analysis in the application of urban computing. As a core member, she has engaged in many national and provincial research projects founded by the National Science and Technology Major Project and the Major project of Beijing Science and Technology Plan, etc.

Ying Sun is currently a PhD candidate from Institute of Computing Technology, Chinese Academy of Sciences. Prior to that, she received her BEng from Beijing Institute of Technology, China in 2017. Her general areas of research have been artificial intelligence, data mining, and machine learning, with a focus on developing effective, explainable, and efficient algorithms and models for talent-centered business analytics and applications.

Hengshu Zhu is currently a principal architect & scientist at Baidu Inc. He received the PhD degree in 2014 and BE degree in 2009, both in Computer Science from University of Science and Technology of China (USTC), China. His general area of research is data mining and machine learning, with a focus on developing advanced data analysis techniques for innovative business applications. He has published prolifically in refereed journals and conference proceedings, and served regularly on the organization and program committees of numerous conferences. He was the recipient of the Distinguished Dissertation Award of CAS (2016), the Distinguished Dissertation Award of CAAI (2016), the Special Prize of President Scholarship for Postgraduate Students of CAS (2014), the Best Student Paper Award of KSEM-2011, WAIM-2013, CCDM-2014, and the Best Paper Nomination of ICDM-2014. He is the senior member of IEEE, ACM, and CCF.

Cong Geng received his Bachelor degree in computer science and technology from Beihang University, China in 2021. He has been studying for his Master degree in computer science and technology from Beihang University, China since 2021. His research interests include service computing and information extraction.

Jiuchun Yang received his Bachelor degree in computer science and technology at Beihang University, China in 2021. He is currently pursuing his Master Program in Financial Technology at Imperial College London, UK. His research interest includes big data, machine learning, and quantitative finance.

Hui Xiong is currently a Chair Professor at the Hong Kong University of Science and Technology, China. Dr. Xiong’s research interests include data mining, mobile computing, and their applications in business. Dr. Xiong received his PhD in Computer Science from University of Minnesota, USA. He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM), a General Co-Chair for the 2015 IEEE International Conference on Data Mining (ICDM), and a Program Co-Chair of the Research Track for the 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Bo Lang received the PhD degree from Beihang University (BUAA), China in 2004. She has been a visiting scholar at Argonne National Lab/University of Chicago, USA for one year. She is a Professor in School of Computer Science and Engineering, Beihang University (BUAA), China. Her current research interests include big data analytics and information security. As a principle investigator or core member, she has engaged in many national research projects founded by the National Natural Science Foundation, National High Technology Research and Development (863) Program, etc.

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Niu, H., Sun, Y., Zhu, H. et al. Exploring the tidal effect of urban business district with large-scale human mobility data. Front. Comput. Sci. 17, 173319 (2023). https://doi.org/10.1007/s11704-022-1623-6

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  • DOI: https://doi.org/10.1007/s11704-022-1623-6

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