Modeling_and_Analysis_of_Spatial-Temporal_Relationship_and_Risk_Evolution_of_Emergencies_Based_on_Big_Data_from_Social_Media

There are many kinds of emergencies in the real world, and there is a close connection between different events. However, the current research on the evolution of emergency risk is limited by the volume and period of the data, which makes the conclusions drawn from these studies have certain limitations today. To describe the emergency’s temporal and spatial distribution and its casual emergency chain from the perspective of statistics, we obtain a large amount of data in terms of emergency from Sina Weibo, one of the most famous social media in China. The research outcome is expected to contribute to early warning for preventing and controlling emergencies by fully using accumulated, updated, and rich emergency data from social media.

Recommended citation: S. Ma, P. Li, M. Yang, C. Sun and R. Wang, \"Modeling and Analysis of Spatial-Temporal Relationship and Risk Evolution of Emergencies Based on Big Data from Social Media,\" 2022 2nd International Signal Processing, Communications and Engineering Management Conference (ISPCEM), Montreal, ON, Canada, 2022, pp. 14-18, doi: 10.1109/ISPCEM57418.2022.00009. keywords: {Analytical models;Graphical models;Social networking (online);Statistical analysis;Engineering management;Signal processing;Emergency services;emergencies;emergency chain;data mining;co-occurrence matrix;complex network}, http://ymx10086.github.io/files/Modeling_and_Analysis_of_Spatial-Temporal_Relationship_and_Risk_Evolution_of_Emergencies_Based_on_Big_Data_from_Social_Media.pdf