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Visual Analytics of Air Pollution Transmission Among Urban Agglomerations

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14497))

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Abstract

In field of air quality research, it is essential to scientifically reflect the internal structure of air quality distribution and reveal the dynamic evolution of air pollution. In this study, a novel visual analytics method is proposed to address these challenges. Initially, the spatio-temporal features of air quality data are mined to complete urban agglomeration division based on dimensionality reduction and clustering. Subsequently, the air pollution transmission network (APTN) is constructed through particle transport and correlation analysis. A progressive exploration analysis method based on multidimensional space transformation is then employed to explore the process of air pollution transmission. Furthermore, a visual analytics system is developed to facilitate the interpretation of the results. Finally, we demonstrate the effectiveness of our proposed methodology using real data sets and case studies, and receive positive feedback from domain experts.

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Acknowledgements

This study was supported by the Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0961), the Doctoral Foundation of Southwest University of Science and Technology (Grant No. 19zx7144), and the Special Research Fund of the Research Centre for Network Emergency Management in China (Mianyang) Science and Technology City (Grant No. WLYJGL2023ZD04).

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Authors and Affiliations

  1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, Sichuan, China

    Shijie Chen, Song Wang, Yipan Liu, Dongliang Ma & Hao Hu

Authors
  1. Shijie Chen
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  2. Song Wang
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  3. Yipan Liu
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  4. Dongliang Ma
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  5. Hao Hu
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Corresponding author

Correspondence to Song Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneva, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Chen, S., Wang, S., Liu, Y., Ma, D., Hu, H. (2024). Visual Analytics of Air Pollution Transmission Among Urban Agglomerations. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-50075-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50074-9

  • Online ISBN: 978-3-031-50075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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