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When a major natural disaster occurs, the data in the disaster area is usually incomplete and information islands may appear. Considering the time required by the on-site survey statistical methods, geospatial information diffusion models can be used to infer the rescue needs of information islands. The basic principle is to spread the disaster information around the information island to the information island. Data that is related to rescue needs and can be collected before a disaster occurs is called background data. The background data in each geographic unit in the disaster area played a role as a bridge for information diffusion. The rapid assessment of earthquake disasters is an important basis for earthquake emergency response and government decision-making, and reliable rapid assessment of earthquake disasters is of great significance to improving earthquake emergency response capabilities. The rapid earthquake disaster assessment is an emergency assessment based on the preparation of the preliminary data and the difficulty of obtaining the post-disaster data, starting from the time of the earthquake. Under the requirements of rapid disaster assessment, what needs to be sought is a simple and efficient relationship that meets certain accuracy requirements, so that the rapid assessment of the disaster situation can be carried out based on easily accessible or available indicators in advance. This paper uses the literature analysis method and uses CiteSpace to study the highfrequency co-occurrence keywords in the field of earthquake disaster loss assessment from 1996 to 2020 and the evolution of research hotspots in the field from 2010 to 2020, and obtain parameters that can be used as background data.

Research on Earthquake Disaster Background Data in Spatial Information Diffusion Model