IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v107y2021i3d10.1007_s11069-021-04521-2.html
   My bibliography  Save this article

Haze risk: information diffusion based on cellular automata

Author

Listed:
  • Chaoyu Zheng

    (Nanjing University of Information Science and Technology)

  • Benhong Peng

    (Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology)

  • Xin Sheng

    (Nanjing University of Information Science and Technology)

  • Anxia Wan

    (Nanjing University of Information Science and Technology)

Abstract

Negative effects of haze risk can easily spread faster and more widely, however, existing studies rarely investigate the whole period or cycle of diffusion events, which leads to lowered public knowledge, often resulting in exaggerated negative effects. In this paper, the diffusion simulation model based on cellular automata is used to evaluate the diffusion of haze risk information. Firstly, according to the whole-life cycle of emergencies, the public affected by haze risk information is classified by resembling the SEIR infectious disease model. Secondly, a diffusion rule from unknown to exposed individuals is developed based on the theory of cellular automata. Then, according to the individual state transformation at different stages, a whole-life cycle model regarding haze risk information diffusion and propagation model is constructed. Finally, appropriate parameters are selected to calculate the results without intervention. The results show that during the whole evolution process, the unknowns continue to decrease, and the lurkers continue to increase. Due to the existence of the immunization period, the immunized persons reach their maximum number before they are about to lose immunity, and the number of communicators reaches their minimum. Afterward, the number of immunized persons reduced to a stable level, and the number of communicators continues to increase toward an agglomeration benefit. Therefore, in order to achieve effective control of the spread of haze risk information, strong and weak control measures are taken for each type of individuals, and the immunity of unknowns and lurkers is increased for individual type control. For the entire information diffusion control, increase communicator immunity and reduce immunization conversion rate. The study of the spread of haze risk information helps to increase the public's sense of responsibility, helps to improve the government's credibility, and contributes to the establishment of a harmonious society.

Suggested Citation

  • Chaoyu Zheng & Benhong Peng & Xin Sheng & Anxia Wan, 2021. "Haze risk: information diffusion based on cellular automata," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2605-2623, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04521-2
    DOI: 10.1007/s11069-021-04521-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04521-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-04521-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Jooyoung & Ahn, Chiwon & Lee, Seungjae, 2018. "Modeling handicapped pedestrians considering physical characteristics using cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 507-517.
    2. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    3. Guan, Junbiao & Wang, Kaihua & Chen, Fangyue, 2016. "A cellular automaton model for evacuation flow using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 655-661.
    4. Rui, Xiaobin & Meng, Fanrong & Wang, Zhixiao & Yuan, Guan & Du, Changjiang, 2018. "SPIR: The potential spreaders involved SIR model for information diffusion in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 254-269.
    5. Guseo, Renato & Guidolin, Mariangela, 2010. "Cellular Automata with network incubation in information technology diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2422-2433.
    6. Juan Benjamin Duarte Duarte & Leonardo Hernán Talero Sarmiento & Katherine Julieth Sierra Suárez, 2017. "Evaluation of the effect of investor psychology on an artificial stock market through its degree of efficiency," Contaduría y Administración, Accounting and Management, vol. 62(4), pages 1361-1376, Octubre-D.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Linhe & Liu, Wenshan & Zhang, Zhengdi, 2021. "Interplay between epidemic and information spreading on multiplex networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 268-279.
    2. Wang, Haiying & Moore, Jack Murdoch & Wang, Jun & Small, Michael, 2021. "The distinct roles of initial transmission and retransmission in the persistence of knowledge in complex networks," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    3. Guseo, Renato, 2016. "Diffusion of innovations dynamics, biological growth and catenary function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 1-10.
    4. Kurdi, Heba & Almulifi, Asma & Al-Megren, Shiroq & Youcef-Toumi, Kamal, 2021. "A balanced evacuation algorithm for facilities with multiple exits," European Journal of Operational Research, Elsevier, vol. 289(1), pages 285-296.
    5. Leonardo Hernán Talero-Sarmiento & Henry Lamos-Díaz & Edwin Alberto Garavito-Hernández, 2019. "Evaluación de la hipótesis de eficiencia débil y análisis de causalidad en las centrales de abastos de Colombia," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 38(67), pages 35-69, February.
    6. Li, Xiaopeng & Sun, Shiwen & Xia, Chengyi, 2019. "Reputation-based adaptive adjustment of link weight among individuals promotes the cooperation in spatial social dilemmas," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 810-820.
    7. Juang, Jonq & Liang, Yu-Hao, 2024. "Epidemic models in well-mixed multiplex networks with distributed time delay," Applied Mathematics and Computation, Elsevier, vol. 474(C).
    8. Mendonça, J.P. & Brum, Arthur A. & Lyra, M.L. & Lira, Sérgio A., 2024. "Evolutionary game dynamics and the phase portrait diversity in a pandemic scenario," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    9. Li, Shuang & Yu, Xiaohui & Zhang, Yanjuan & Zhai, Changhai, 2018. "A numerical simulation strategy on occupant evacuation behaviors and casualty prediction in a building during earthquakes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1238-1250.
    10. Guseo, Renato & Guidolin, Mariangela, 2015. "Heterogeneity in diffusion of innovations modelling: A few fundamental types," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 514-524.
    11. Liu, Xiaoxiao & Sun, Shiwen & Wang, Jiawei & Xia, Chengyi, 2019. "Onion structure optimizes attack robustness of interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    12. Hu, Jun & Xia, Chengyi & Li, Huijia & Zhu, Peican & Xiong, Wenjun, 2020. "Properties and structural analyses of USA’s regional electricity market: A visibility graph network approach," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    13. Geng, Zhongfei & Li, Xingli & Kuang, Hua & Bai, Xuecen & Fan, Yanhong, 2019. "Effect of uncertain information on pedestrian dynamics under adverse sight conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 681-691.
    14. Yin, Qian & Wang, Zhishuang & Xia, Chengyi & Dehmer, Matthias & Emmert-Streib, Frank & Jin, Zhen, 2020. "A novel epidemic model considering demographics and intercity commuting on complex dynamical networks," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    15. Jun, Seung-Pyo & Yoo, Hyoung Sun & Lee, Jae-Seong, 2021. "The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    16. Lyu, Yibo & Zhu, Yuqing & Han, Shaojie & He, Binyuan & Bao, Lining, 2020. "Open innovation and innovation "Radicalness"—the moderating effect of network embeddedness," Technology in Society, Elsevier, vol. 62(C).
    17. Tian, Huan-huan & Wei, Yan-fang & Dong, Li-yun & Xue, Yu & Zheng, Rong-sen, 2018. "Resolution of conflicts in cellular automaton evacuation model with the game-theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 991-1006.
    18. Guan, Junbiao & Wang, Kaihua, 2020. "Cooperative evolution in pedestrian room evacuation considering different individual behaviors," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    19. Sang, Chun-Yan & Liao, Shi-Gen, 2020. "Modeling and simulation of information dissemination model considering user’s awareness behavior in mobile social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    20. Tong, Chao & Ding, Shuai & Wang, Bin & Yang, Shanlin, 2020. "Assessing the target-availability of China’s investments for green growth using time series prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04521-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.