## 3 Materials and Methods
### Traffic Density
#### 3.2.2 Visualization Process of Traffic Density  
Maritime traffic density visualization is crucial for visually understanding vessel movement patterns and traffic congestion zones. The construction process involves four steps, as shown in Figure 2. Step 1 involves data preprocessing, an essential step in analyzing maritime traffic density using AIS data. To conduct research based on the AIS data, it is necessary to ensure the accuracy and reliability of the data. In this process, duplicate data and error values collected simultaneously are removed, and each ship's voyage is sorted chronologically by its Maritime Mobile Service Identity (MMSI) number. AIS data are crucial for traffic density analysis, containing key information such as vessel position, speed, and direction of travel. Step 2 involves dividing the sea area into appropriate grids. The maritime area is spatially divided into a grid to facilitate systematic analysis of the navigation data. The grid is usually divided into rectangular or square shapes, and the cell size is adjusted depending on the purpose of the analysis and the characteristics of the target area. For example, a dense area such as a harbor may have a finer grid, whereas a large area such as an ocean may have a relatively large grid to allow for efficient analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grid Item** & **Grid Level** & \multicolumn{2}{c}{**Grid Interval**} \\ \hline  & 1 & 1\({}^{\circ}\) & abt. 100 km \\ Horizontal Grid by & 2 & 15\({}^{\prime}\) & abt. 25 km \\ KHOA & 3 & 3\({}^{\prime}\) & abt. 5 km \\ (6 levels) & 4 & 1\({}^{\prime}\)30\({}^{\prime\prime}\) & abt. 2.5 km \\
5 & 5 & 30\({}^{\prime\prime}\) & abt. 1 km \\
6 & 3\({}^{\prime\prime}\) & abt. 100 m \\ \hline Proposed Grid & 7 & 1\({}^{\prime\prime}\) & abt. 30 m \\ \hline \hline \end{tabular} Note: Horizontal grid level interval refers to “Korea Hydrographic and Oceanographic Agency (KHOA)”.  
\end{table} 
Table 1: Grid cell interval as per grid level. Step 3 involves counting the number of ship voyages passing through the constructed grid cells. This process provides a numerical representation of maritime traffic density and calculates the traffic density of each grid cell based on the frequency of vessels passing through a particular area. This step allows a quantitative analysis of the spatial distribution of maritime traffic and provides an intuitive understanding of the maritime traffic situation in a particular area. The calculated traffic density data are visualized as a heatmap, where color variations represent the traffic density of each grid cell, thus enabling the intuitive identification of dense and relatively quiet areas of maritime traffic. Additionally, a colormap mapping method was applied to the heatmap visualization. A colormap is an essential tool for visualizing the sequence and information of observations by adjusting the intensity and brightness of colors, enabling an intuitive representation of data trends and distributions. As shown in Figure 3, two representative sequential colormap mapping methods for maritime traffic density visualization are the single-color gradient mapping method and the multi-color gradient mapping method. The single-color gradient mapping method visualizes data by adjusting brightness or saturation, with low density represented by lighter colors and high density by darker colors. This method is simple yet effective in clearly presenting the distribution of data. The multi-color gradient mapping method uses multiple colors to highlight the range of data values. Typically, colors transition gradually from low to high values, providing a clear and intuitive visualization of data ranges and trends. In this study, a multi-color gradient method was applied, transitioning from blue (low density) to green (medium density) and red (high density). This approach effectively highlights significant variations in maritime traffic density by distinguishing between high- and low-density areas on the grid map.