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Discovering Geographical Flock Patterns of CO 2 Emissions in China Using Trajectory Mining Techniques

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  • Pengdong Zhang

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
    Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China)

  • Lizhi Miao

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China
    Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China)

  • Fei Wang

    (East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China)

  • Xinting Li

    (School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Wenyuan Road 9, Nanjing 210023, China)

Abstract

Carbon dioxide (CO 2 ) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO 2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO 2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO 2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high–low attribute values criterion and the extreme number–duration values criterion. A case study is conducted based on the CO 2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO 2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

Suggested Citation

  • Pengdong Zhang & Lizhi Miao & Fei Wang & Xinting Li, 2023. "Discovering Geographical Flock Patterns of CO 2 Emissions in China Using Trajectory Mining Techniques," IJERPH, MDPI, vol. 20(5), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4265-:d:1082669
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    References listed on IDEAS

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