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AirPollutionViz: visual analytics for understanding the spatio-temporal evolution of air pollution

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Abstract

Spatio-temporal evolution analysis has been a critical topic of air pollution research. However, there are still several difficulties caused by the large scale and dimensionality of the data. Specifically, First, traditional methods deal with such data by simplifying and abstracting, resulting in information loss. Second, most existing visualizations, generally focusing on overall evolution, ignore the exploration of multiple time scales and pattern transitions between subsequences. This paper presents AirPollutionViz, a visual analytics system that enables to analyze the spatio-temporal evolution in two manners: sequence mining and clustering analysis. Concretely, we propose sequence merging to shorten the sequence length and construct a weighted directed graph structure, which promotes efficient querying of sequence patterns by combination with dynamic time warping. We design a novel summary view to display the overview of pollution level changes, together with the improved node-link chart, to support the analysis of air pollution spatio-temporal evolution patterns. We also apply K-means clustering to pollutants, and a scatter plot and map reflect the spatial distribution aggregation. The system supports users’ free exploration across multiple time scales with rich interactions. Case studies with three domain experts and a user study with ten users demonstrate the usefulness and effectiveness of AirPollutionViz.

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References

  • Aljawarneh S, Radhakrishna V, Cheruvu A (2018) VRKSHA: a novel multi-tree based sequential approach for seasonal pattern mining. In: Proceedings of the fourth international conference on engineering & MIS 2018. pp 1–10

  • Andrienko G, Andrienko N, Fuchs G, Wood J (2016) Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Trans Vis Comput Graph 23(9):2120–2136

    Article  PubMed  Google Scholar 

  • Bai Y-T, Jin X-B, Wang X-Y, Wang X-K, Xu J-P (2020) Dynamic correlation analysis method of air pollutants in spatio-temporal analysis. Int J Environ Res Public Health 17(1):360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L (2022) Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19. Chemosphere 288:132569

    Article  CAS  PubMed  Google Scholar 

  • Büschel W, Lehmann A, Dachselt R (2021) Miria: a mixed reality toolkit for the in-situ visualization and analysis of spatio-temporal interaction data. In: Proceedings of the 2021 CHI conference on human factors in computing systems. pp 1–15

  • Cao N, Shi C, Lin S, Lu J, Lin Y-R, Lin C-Y (2015) Targetvue: visual analysis of anomalous user behaviors in online communication systems. IEEE Trans Vis Comput Graph 22(1):280–289

    Article  Google Scholar 

  • Chen R, Shu X, Chen J, Weng D, Tang J, Fu S, Wu Y (2021) Nebula: a coordinating grammar of graphics. IEEE Trans Vis Comput Graph 28(12):4127–4140

    Article  Google Scholar 

  • Chen Z (2021) The application of artificial intelligence on the traceability and dispersion of air pollution. In: 2021 6th international conference on image, vision and computing (ICIVC). IEEE, pp 404–407

  • Cui M et al (2020) Introduction to the k-means clustering algorithm based on the elbow method. Account Audit Finance 1(1):5–8

    Google Scholar 

  • de Amo S, Furtado DA, Giacometti A, Laurent D (2004) An apriori-based approach for first-order temporal pattern mining. In: SBBD. pp 48–62

  • Deng Z, Weng D, Chen J, Liu R, Wang Z, Bao J, Zheng Y, Wu Y (2019) AirVis: visual analytics of air pollution propagation. IEEE Trans Vis Comput Graph 26(1):800–810

    PubMed  Google Scholar 

  • Dunne C, Shneiderman B (2013) Motif simplification: improving network visualization readability with fan, connector, and clique glyphs. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp 3247–3256

  • Elbir T (2004) A GIS based decision support system for estimation, visualization and analysis of air pollution for large Turkish cities. Atmos Environ 38(27):4509–4517

    Article  ADS  CAS  Google Scholar 

  • Fournier-Viger P, Lin JC-W, Kiran RU, Koh YS, Thomas R (2017) A survey of sequential pattern mining. Data Sci Pattern Recognit 1(1):54–77

    Google Scholar 

  • Guo F, Gu T, Chen W, Wu F, Wang Q, Shi L, Qu H (2019) Visual exploration of air quality data with a time-correlation-partitioning tree based on information theory. ACM Trans Interact Intell Syst (TiiS) 9(1):1–23

    Article  Google Scholar 

  • Harding L, Wernli F, Suter F (2021) Sequence-RTG: efficient and production-ready pattern mining in system log messages. In: 2021 IEEE international conference on cluster computing (CLUSTER). IEEE, pp 623–631

  • Huang W, Li T, Liu J, Xie P, Du S, Teng F (2021) An overview of air quality analysis by big data techniques: monitoring, forecasting, and traceability. Inf Fusion 75:28–40

    Article  Google Scholar 

  • Kang GK, Gao JZ, Chiao S, Lu S, Xie G (2018) Air quality prediction: big data and machine learning approaches. Int J Environ Sci Dev 9(1):8–16

    Article  Google Scholar 

  • Kappe CP, Böttinger M, Leitte H (2018) Exploring variability within ensembles of decadal climate predictions. IEEE Trans Vis Comput Graph 25(3):1499–1512

    Article  PubMed  Google Scholar 

  • Kern M, Hewson T, Schätler A, Westermann R, Rautenhaus M (2018) Interactive 3d visual analysis of atmospheric fronts. IEEE Trans Vis Comput Graph 25(1):1080–1090

    Article  Google Scholar 

  • Kong L, Tang X, Zhu J, Wang Z, Li J, Wu H, Wu Q, Chen H, Zhu L, Wang W et al (2021) A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in china based on the assimilation of surface observations from CNEMC. Earth Syst Sci Data 13(2):529–570

    Article  ADS  Google Scholar 

  • Kuerban M, Waili Y, Fan F, Liu Y, Qin W, Dore AJ, Peng J, Xu W, Zhang F (2020) Spatio-temporal patterns of air pollution in china from 2015 to 2018 and implications for health risks. Environ Pollut 258:113659

    Article  CAS  PubMed  Google Scholar 

  • Li J, Xiao Z, Zhao H-Q, Meng Z-P, Zhang K (2016) Visual analytics of smogs in China. J Vis 19:461–474

    Article  Google Scholar 

  • Lin G, Fu J, Jiang D, Hu W, Dong D, Huang Y, Zhao M (2014) Spatio-temporal variation of PM2. 5 concentrations and their relationship with geographic and socioeconomic factors in China. Int J Environ Res Public Health 11(1):173–186

    Article  Google Scholar 

  • Liu H, Liu J, Li M, Gou P, Cheng Y (2022) Assessing the evolution of PM2. 5 and related health impacts resulting from air quality policies in China. Environ Impact Assess Rev 93:106727

    Article  Google Scholar 

  • Lundblad P, Eurenius O, Heldring T (2009) Interactive visualization of weather and ship data. In: 2009 13th international conference information visualisation. IEEE, pp 379–386

  • Luo Z, Chen Y, Li H, Li Y, Guo Y (2022) TreeMerge: a visual comparative analysis method for food classification tree in pesticide residue maximum limit standards. Agronomy 12(12):3148

    Article  Google Scholar 

  • Mooney CH, Roddick JF (2013) Sequential pattern mining-approaches and algorithms. ACM Comput Surv (CSUR) 45(2):1–39

    Article  Google Scholar 

  • Naulaerts S, Meysman P, Bittremieux W, Vu TN, Vanden Berghe W, Goethals B, Laukens K (2015) A primer to frequent itemset mining for bioinformatics. Brief Bioinform 16(2):216–231

    Article  CAS  PubMed  Google Scholar 

  • Nocke T, Schumann H, Böhm U (2004) Methods for the visualization of clustered climate data. Comput Stat 19:75–94

    Article  MathSciNet  Google Scholar 

  • Orru H, Ebi K, Forsberg B (2017) The interplay of climate change and air pollution on health. Curr Environ Health Rep 4:504–513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Park JW, Yun CH, Jung HS, Lee YW (2011) Visualization of urban air pollution with cloud computing. In: 2011 IEEE world congress on services. IEEE, pp 578–583

  • Peng Y, Fan X, Chen R, Yu Z, Liu S, Chen Y, Zhao Y, Zhou F (2023) Visual abstraction of dynamic network via improved multi-class blue noise sampling. Front Comput Sci 17(1):171701

    Article  Google Scholar 

  • Qu D, Lin X, Ren K, Liu Q, Zhang H (2020) AirExplorer: visual exploration of air quality data based on time-series querying. J Vis 23:1129–1145

    Article  Google Scholar 

  • Qu D, Lv C, Lin Y, Zhang H, Wang R (2022) AirLens: multi-level visual exploration of air quality evolution in urban agglomerations. In: Computer graphics forum, vol 41. Wiley Online Library, pp 223–234

  • Qu H, Chan W-Y, Xu A, Chung K-L, Lau K-H, Guo P (2007) Visual analysis of the air pollution problem in Hong Kong. IEEE Trans Vis Comput Graph 13(6):1408–1415

    Article  PubMed  Google Scholar 

  • Rastogi V, Khare VK (2012) Apriori based: mining positive and negative frequent sequential patterns. Int J Latest Trends Eng Technol 1(3):24–33

    Google Scholar 

  • Raza K (2012) Application of data mining in bioinformatics. arXiv preprint arXiv:1205.1125

  • Rebolj D, Sturm PJ (1999) A GIS based component-oriented integrated system for estimation, visualization and analysis of road traffic air pollution. Environ Model Softw 14(6):531–539

    Article  Google Scholar 

  • Ren R, Li J, Yin Y, Tian S (2021) Failure prediction for large-scale clusters logs via mining frequent patterns. In: Intelligent computing and block chain: first benchcouncil international federated conferences, FICC 2020, Qingdao, China, October 30–November 3, 2020, Revised Selected Papers 1. Springer, pp 147–165

  • Samet H, Han Y, Kastner J, Wei H (2020) Using animation to visualize spatio-temporal varying COVID-19 data. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on modeling and understanding the spread of COVID-19. pp 53–62

  • Shen Y, Zhao L, Fan J (2015) Analysis and visualization for hot spot based route recommendation using short-dated taxi GPS traces. Information 6(2):134–151

    Article  Google Scholar 

  • Tavard F, Simon A, Hernandez AI, Betancur J, Donal E, Garreau M (2012) Dynamic time warping. In: Information retrieval for music and motion. Springer, Berlin, pp 198–203

  • Tian D, Fan J, Jin H, Mao H, Geng D, Hou S, Zhang P, Zhang Y (2020) Characteristic and spatiotemporal variation of air pollution in northern china based on correlation analysis and clustering analysis of five air pollutants. J Geophys Res Atmos 125(8):e2019JD031931

    Article  ADS  CAS  Google Scholar 

  • Van HTH, Chau VTN, Phung NH (2017) An expanded prefix tree-based mining algorithm for sequential pattern maintenance with deletions. In: 2017 2nd international conferences on information technology, information systems and electrical engineering (ICITISEE). IEEE, pp 11–16

  • Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579–2605

    Google Scholar 

  • Wu C, Chen Y, Dong Y, Zhou F, Zhao Y, Liang CJ (2023) VizOPTICS: getting insights into OPTICS via interactive visual analysis. Comput Electr Eng 107:108624

    Article  Google Scholar 

  • Yang A, Zhang W, Wang J, Yang K, Han Y, Zhang L (2020) Review on the application of machine learning algorithms in the sequence data mining of DNA. Front Bioeng Biotechnol 8:1032

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang T, Liu W (2018) Does air pollution affect public health and health inequality? Empirical evidence from China. J Clean Prod 203:43–52

    Article  Google Scholar 

  • Yi X, Zhang J, Wang Z, Li T, Zheng Y (2018) Deep distributed fusion network for air quality prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. pp 965–973

  • Yoshizumi A, Coffer MM, Collins EL, Gaines MD, Gao X, Jones K, McGregor IR, McQuillan KA, Perin V, Tomkins LM, et al (2020) A review of geospatial content in IEEE visualization publications. In: 2020 IEEE visualization conference (VIS). IEEE, pp 51–55

  • Yu D, Ian O, Jie L, Xiaoru Y, Vinh NQ (2023) User-centered visual explorer of in-process comparison in spatiotemporal space. J Vis 26(2):403–421

    Article  Google Scholar 

  • Jianguo Y, Ruifang D (2018) Understanding the patterns and drivers of air pollution on multiple time scales: the case of northern China

  • Zhang D, Lee K, Lee I (2018) Hierarchical trajectory clustering for spatio-temporal periodic pattern mining. Expert Syst Appl 92:1–11

    Article  CAS  Google Scholar 

  • Zhang D, Liu J, Li B (2014) Tackling air pollution in China—what do we learn from the great smog of 1950s in London. Sustainability 6(8):5322–5338

    Article  Google Scholar 

  • Zhao Y, Ge L, Xie H, Bai G, Zhang Z, Wei Q, Lin Y, Liu Y, Zhou F (2022) Astf: visual abstractions of time-varying patterns in radio signals. IEEE Trans Vis Comput Graph 29(1):214–224

    PubMed  Google Scholar 

  • Zhao Y, Lv S, Long W, Fan Y, Yuan J, Jiang H, Zhou F (2023) Malicious webshell family dataset for webshell multi-classification research. Vis Inform

  • Zhong S, Zhou L, Wang Z (2011) Software for environmental impact assessment of air pollution dispersion based on ArcGIS. Procedia Environ Sci 10:2792–2797

    Article  Google Scholar 

  • Zhou B, Hu W, Chen T (2022) Pattern extraction from industrial alarm flood sequences by a modified CloFAST algorithm. IEEE Trans Ind Inf 18(1):288–296. https://doi.org/10.1109/TII.2021.3071361

    Article  ADS  Google Scholar 

  • Zhou F, Mi J, Zhang B, Shi J, Zhang R, Chen X, Zhao Y, Zhang J (2023) Reliable knowledge graph fact prediction via reinforcement learning. Vis Comput Ind Biomed Art 6(1):21

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhu X, Liu Y (2019) An efficient frequent pattern mining algorithm using a highly compressed prefix tree. Intell Data Anal 23(S1):153–173

    Article  MathSciNet  Google Scholar 

  • Zou Q, Lin G, Jiang X, Liu X, Zeng X (2020) Sequence clustering in bioinformatics: an empirical study. Brief Bioinform 21(1):1–10

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62272071 and U1836114.

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Authors and Affiliations

  1. School of Big Data and Software Engineering, Chongqing University, Chongqing, China

    Xiaoqi Yue, Desheng Sun, Chao Liu & Haibo Hu

  2. Alibaba Group, Shanghai, China

    Dan Feng

  3. College of Computer Science, Chongqing University, Chongqing, China

    Hongxing Qin

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  1. Xiaoqi Yue
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  4. Chao Liu
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Correspondence to Haibo Hu.

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Yue, X., Feng, D., Sun, D. et al. AirPollutionViz: visual analytics for understanding the spatio-temporal evolution of air pollution. J Vis 27, 215–233 (2024). https://doi.org/10.1007/s12650-024-00958-2

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