Research on Collaborative Governance of Public Crisis Events Based on Big Data Information Processing Technology

Yan Ke and Ying Wei ()
Additional contact information
Yan Ke: Wuhan University of Science and Technology, School of Grammar & Economics
Ying Wei: Wuhan University of Science and Technology, School of Grammar & Economics

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 78-86 from Springer

Abstract: Abstract This paper adopts a quantitative approach to the study of collaborative public crisis management in the context of big data. Quantitative analysis is conducted on the data of population movement and epidemic distribution, production and deployment of epidemic prevention resources and public service provision during the epidemic. The results show that information processing technology based on big data can uncover the development pattern of the epidemic, implement precise prevention and control, carry out information-based deployment of epidemic prevention resources and realise digital citizen-friendly services. On this basis, a collaborative public crisis management model based on big data information processing technology is proposed.

Keywords: big data; information processing; public crisis; collaborative governance (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-256-9_9

Ordering information: This item can be ordered from
http://www.springer.com/9789464632569

DOI: 10.2991/978-94-6463-256-9_9

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().