Smart Emergency Management Based on Social Big Data Analytics

Sahoh, Bukhoree; Choksuriwong, Anant

doi:10.1145/3176653.3176657

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Sahih points out that smart emergency management (SEM) contains big social data (SBD)-- emergency data sources from physical and social sensors [ 31 ], computing technology (CT)-- methods of processing and analysis, such as machine learning (ML) and data mining (DM), emergency management (EM)-- a strategic procedures for using information and knowledge to deal with emergencies [ 32 ], and to some extent intersects. Currently, research is focused on SBD as a source of information in SEM, especially as a real-time data source to track disaster situations in order to deal with emergencies more effectively [ 33 ]. Dimakis [ 34 ] built a distributed building evacuation simulator for smart emergency management, which plays a role in production safety and social security emergencies.…”

Section: Literature Reviewmentioning

confidence: 99%

How to improve smart emergency preparedness for natural disasters? ---- Evidence from the experience of ten pilot provinces in China for smart emergency

Liu,

Qian,

2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

How to improve smart emergency preparedness for natural disasters? ---- Evidence from the experience of ten pilot provinces in China for smart emergency

Liu,

Qian,

2024

Heliyon

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“…First, basic information, such as the locations of rescue vehicles, the location of rescue, and the dynamic situation of roads is obtained, and the above information is transmitted to the rescue path guidance processing unit. Through big data analysis [46], information fusion [47], [48], optimization methods [49] and other technologies, the best dynamic path is optimized, and rescue vehicles are guided to the incident site. When the optimization result cannot effectively improve the traveling speed of rescue vehicles, the system will interfere with the traffic signals in the above areas and propose how to open a green rescue path to the organizers of emergency response.…”

Section: ) Data Collection and Analysis Of Rescue Vehiclesmentioning

confidence: 99%

Design of Traffic Emergency Response System Based on Internet of Things and Data Mining in Emergencies

Liu

Wang

2019

IEEE Access

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Urban emergencies are hard to avoid. Traffic emergency response after an incident plays an important role in reducing losses and is a key link in urban emergency management. However, the traditional traffic management methods have been difficult to face the complicated conditions and requirements in such problems. The introduction of Internet of Things and data mining technology to establish a traffic emergency response system under urban emergencies can significantly improve the level of urban emergency response and realize efficient intensive management. The system mainly includes sub-systems, such as personnel evacuation data collection system, vehicle operation data collection system, rescue material distribution data collection system, personnel settlement place data collection system, traffic bayonet intelligent identification system, etc. It also devises the working programs for command management, personnel evacuation and disaster disposal in case of emergency, and improves the urban emergency support management system. With the support of Internet of Things and the data mining technology, the traffic emergency response system can timely and accurately control the flowing information of personnel and vehicles, quickly and conveniently resettle personnel and vehicles, effectively carry out follow-up rescue work, effectively improve rescue efficiency and improve the level of urban management.INDEX TERMS Internet of Things, data mining, emergencies, emergency rescue, system design.

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“…e studies mainly focus on proposing suggestions for improving the construction of public health governance systems using case studies [11][12][13][14]. Some scholars have also used traditional statistical regression methods and big data statistical methods to study the factors for the governance efficiency of public health events [15][16][17][18] to achieve accurate governance and prevention and control of major public health emergencies.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Game Analysis of Cross-Regional Coordinated Governance of Major Public Health Emergencies: The Example of the Spread of the COVID-19 Outbreak

Xiao

Peng

2021

Mathematical Problems in Engineering

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Cross-regional governance of government often faces various problems, which often brings great loss to the society. The global outbreak of the novel coronavirus pneumonia (NCP) in early 2020 has not only caused serious economic and social losses to various countries but also put the current public health event governance system to a severe test. The cross-regional character and spillover effects of public health outbreak governance often make it difficult to coordinate cross-regional governance. In this context, this paper adopts a regional evolutionary game analysis framework and studies the cross-regional governance of public health emergencies by constructing a symmetric game of peripheral regions and an asymmetric game of core-peripheral regions. The marginal contribution of this paper is to attempt to construct a symmetric game model for peripheral regions and an asymmetric game model for core and peripheral regions using an evolutionary game approach to study the behavioral strategies of multiple regions in the governance of public health emergencies, and it is found that when the regional spillover effects and governance costs are small or the economic and social damages caused by public health emergencies are large, all regions will choose to conduct coordinated governance. Otherwise, there will be regions that choose to “free-ride.” This “free-rider” mentality has led to the failure in achieving good cross-regional collaborative governance of public health emergencies, resulting in a lack of efficiency in the overall governance of public health in society. However, when the spillover effect of regional governance exceeds a certain critical value, the result of the regional governance game is also the socially optimal result, when public health emergencies are effectively governed. At the same time, the relevant findings and analytical framework of this paper will provide a policy reference for the cross-regional governance of the current new crown epidemic.

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Smart Emergency Management Based on Social Big Data Analytics

Cited by 11 publications

References 33 publications

How to improve smart emergency preparedness for natural disasters? ---- Evidence from the experience of ten pilot provinces in China for smart emergency

How to improve smart emergency preparedness for natural disasters? ---- Evidence from the experience of ten pilot provinces in China for smart emergency

Design of Traffic Emergency Response System Based on Internet of Things and Data Mining in Emergencies

Evolutionary Game Analysis of Cross-Regional Coordinated Governance of Major Public Health Emergencies: The Example of the Spread of the COVID-19 Outbreak

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