## 3 Bibliometric Analysis  
### Keyword Analysis  
We also performed a keyword analysis with VOSviewer to visualize scientific landscapes based on the keyword co-occurrence data of our study results. Figure 3 presents a bibliometric network visualization showing the keyword occurrence. Co-occurrence author keywords were selected using the software and the full counting method. We obtained 398 keywords with a threshold of 1, with a linlog/modularity normalization method. The central theme, 'aviation', suggests that the surrounding keywords are related to research within the aviation field. Clusters of keywords are differentiated by color, indicating thematic groupings. The lines connecting the nodes represent the relationship's strength, with a higher number of lines indicating a stronger association. The size of the nodes corresponds to the frequency of the keyword's appearance in the dataset, and larger nodes such as 'aviation', 'general aviation', and 'decision-making' were more prevalent. Of the 398 keywords, we selected the 20 with the high link strength, as shown in Table 3. In summary, the visualization comprehensively represents the relationships between keywords in aviation research, showing the frequency and connection strength of terms within the field. It is an effective instrument for identifying research trends, prominent topics, and potential gaps in the literature. Figure 2: Contributing factors related to the time reference. Table 3 shows that the keywords \"general aviation\", \"situation awareness\", \"human\", \"decision-making\", \"civil aviation\", \"pilots\", \"adult\", \"airplane pilot\", \"safety\", \"risk management\", \"aircraft accident\", \"aircraft\", \"humans\", \"risk perception\", \"machine learning\", \"weather situation awareness\", \"weather\", \"critical accidents\" and \"pilot performance\" have a significant role in aviation with 39 occurrences and a 417 link strength. From the network visualization of the occurrence of keywords (Figure 3), we also performed a keyword occurrence using year overlay visualization (Figure 4). Figure 4 is a bibliometric visualization also created with VOSviewer, mapping out key relationships between terms by year in the aviation research literature obtained in our study. The largest nodes--'aviation', 'general aviation', and 'decision-making'--indicate these are the most researched and yearly discussed topics. The interconnections between terms like 'human', 'cognitive health', and 'psychology' point toward a significant focus on human factors in aviation. Safety concerns are highlighted by the cluster of terms associated with'safety', 'risk management', and 'aircraft safety', indicating a strong emphasis on identifying and managing risks. There is a clear trajectory of integrating technology into aviation, evidenced by terms such as 'artificial neural network' and'machine learning'. The inclusion of 'weather' and 'COVID-19' suggests that external factors and their impacts on aviation are of growing interest in the literature. The timeline at the bottom, with lines extending to various years, reveals how these themes have become more or less prominent over time; for instance, 'COVID-19' is a term that spiked in relevance in recent years. The visualization also serves as a research tool to identify trends, emerging topics, and potential gaps in the literature where thinner connections or smaller nodes appear. Furthermore, the network indicates interdisciplinary research connections, integrating insights from technology, psychology, and health into the field of aviation. This bibliometric map is invaluable for researchers and policymakers to understand the current state of aviation research, to follow its evolution over time, and to identify areas that may require further exploration.