# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 4 Results and Discussion
### Document Co-Citation Network Analysis  
In this section, we conducted a document co-citation analysis to determine the key literature from 2012 to 2022. Similar to selecting the top N nodes for network analysis \begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **Size** & **Silhouette** & **Mean (Year)** & **Label (LLR)** \\ \hline
0 & 37 & 0.758 & 2013 & Weather research \\
1 & 31 & 0.83 & 2014 & Rainfall threshold; \\  & & & & Debris flow \\
2 & 20 & 0.777 & 2019 & Deep learning; \\  & & & & Convolutional neural network \\
3 & 13 & 0.794 & 2015 & Ground motion model; \\  & & & & Ground motion prediction equation \\
4 & 12 & 0.946 & 2012 & Eruption forecasting; \\  & & & & Earthquake forerunner \\ \hline \end{tabular}
\end{table} 
Table 6: The largest five clusters in the author co-citation network. Figure 6: The clusters in the author co-citation network for the years 2012 to 2022.  
in Section 4.2, we selected the top 20 documents during each time slice for document co-citation network analysis. The merged network includes 283 nodes and 469 links as shown in Figure 7. The nodes represent independent documents, each of which is indicated by the author and publication time. The links refer to the co-citation relationship between documents. Similar to the author's co-citation network, the concentric rings of different colors in the document co-citation network reflect the co-citation patterns of the documents over time. The largest node in the network represents a paper published by [PERSON] (2020) with the highest citation frequency of 30 times. The orange and red thick rings enclosing this node indicate that this paper has been frequently cited and received extensive attention from scholars in 2021 and 2022, aligning with the color bar shown at the left bottom of Figure 6.  
The outermost ring surrounding the node represents the betweenness centrality of the document, indicated by the color purple. The thicker the purple ring, the higher the betweenness centrality of the document. The information regarding the top five documents with higher betweenness centrality in the document co-citation network is shown in Table 7, including title, betweenness centrality, degree centrality, closeness centrality, authors, year of publication, and published journal. The betweenness centrality scores of those documents are all around 0.40 and published in highly impacted journals, covering various topics such as forecasting shallow landslides, determining rainfall intensity and duration, predicting traffic flow, reviewing social media-based event detection techniques, and predicting human behaviors. Specifically, the paper entitled \"Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy\" which was published in Geomorphology in 2015 stands out with the highest betweenness centrality of 0.45 and highest degree centrality, reflecting this paper plays an important intermediary role and serves as a bridge connecting different research groups in the event prediction field. Similarly, there is a slight difference among documents regarding closeness centrality. The journal paper \"Deep learning for short-term traffic flow prediction\" published in Transportation Research Part C: Emerging Technologies in 2017 has the highest closeness centrality of 0.67, indicating that this paper plays an important role in connecting all documents through the shortest path in the network. Figure 7: The document co-citation network for the years 2012–2022. When a document experiences a substantial increase in the number of citations within a specific timeframe, it is recognized as having a pronounced citation burst. The paper titled \"The JRA-55 reanalysis: general specifications and basic characteristics\" was proposed by the Japan Meteorological Agency (JMA) who performed the second Japanese global atmospheric reanalysis (JRA-55) from 1958, gaining the strongest citation burst of 8.03 (see Table 8).