# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 5 Conclusions  
This study utilizes CiteSpace software to conduct a scientometric analysis of research focused on event prediction. To explore research productivity and emerging trends in the surveyed field, we gathered documents published from 2012 to 2022 from the Web of Science database. The number of publications concerning event prediction has kept increasing in the last decade, especially with significant increases since 2019, indicating this topic has attracted more and more attention from the research communities over time. Four networks were then generated and visualized based on the collected documents for scientometric analysis, including the author co-citation network, document co-citation network, collaborative institution network, and keyword co-occurrence network. The four types of network analysis have produced several remarkable findings. The representative authors and documents are from different disciplines (e.g., meteorology science, computer science, and data mining), showing that event prediction research holds obvious interdisciplinary characteristics promoting cooperation and communication among research institutions worldwide. The frequently co-occurring keywords reveal hot research topics (e.g., extreme events and weather events) and widely used methods (e.g., deep learning and machine learning methods) as well as the research trends (e.g., artificial intelligence aiding event prediction) with great potential. In addition, sensor data has been playing an integral role in event prediction since 2014, especially for predicting weather events and meteorological events (e.g., monitoring sea surface temperature and weather sensor data for predicting El Nino). The real-time and multivariable monitoring features of sensor data enable it to be a reliable source for predicting multiple types of events.  
In spite of the notable results achieved in this work, there is still room for improvement in the near future. The scope of this study was limited to articles written in English and collected exclusively from the Web of Science database. As a result, it is possible that certain pertinent research might have been inadvertently omitted from this survey. Aiming at overcoming this limitation, the documents written in other languages (such as Chinese) and literature data obtained from other databases (such as Scopus) can be incorporated to enable a more comprehensive comparison and analysis in the future.  
S.X.: Contributed to conceptualization, writing--review and editing, funding acquisition, and supervision. J.L.: Responsible for methodology, software, data curation, and writing--original draft. S.L.: Involved in writing--review and editing. S.Y.: Contributed to investigation, writing--review and editing. F.L.: Assisted with review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Beijing Association for Science and Technology Young Elite Scientist Sponsorship Program (BYESS2023008), the Key Laboratory of Urban Spatial Informatics, Ministry of Natural Resources of the People's Republic of China (2023 ZD002), China Scholarship Council (03998521001), and the Natural Sciences and Engineering Research Council of Canada (RGPIN-2017-05950).  
Data Availability Statement: Data availability is not applicable to this article as no new data were created or analyzed in this study.  
The authors declare no conflict of interest.