Abstract
In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this article combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients’ situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3)An efficient epidemic prevention and anti-epidemic method is proposed which is of referential significance. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients’ relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups.
Highlights
D URING the COVID-19 (Coronavirus Disease 2019) outbreak, related methods and spatiotemporal big data have been continuously used to form differentiated prevention and control measures
Spatiotemporal big data played a significant role in the emergency stage of the epidemic prevention and control, but they have no significant role in real-time disease monitoring and the spatiotemporal prediction of the epidemic’s spread
In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this paper combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of related technologies of the geographic knowledge graph and big data visual analysis
Summary
D URING the COVID-19 (Coronavirus Disease 2019) outbreak, related methods and spatiotemporal big data have been continuously used to form differentiated prevention and control measures. Measures such as COVID-19 patients’ maps, the population flow and distribution of potential patients, the spatiotemporal tracking of case trajectories, the allocation of medical resources in epidemic areas and the differentiated control of the epidemic have been used in the areas where they may have social and economic impacts. Single domain and single mode data analyses conduct analyses and make predictions through the adjustment, correlation and optimization of an algorithm’s components that focus on a single domain They lack the comprehensive analysis of multiple domains and multiple modalities. It is necessary to consider comprehensively analysing the association of large-scale heterogeneous data and using heterogeneous data fusion to expand the multimodal knowledge support of epidemic in-
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