# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 4 Discussion  
The results of the present study differed to some extent from those of [PERSON] et al. [37] who extracted the maritime traffic routes in Korea based on a density analysis [37]. Three key features of [PERSON] et al.'s study are important. First, they divided the coastal waters of Korea into 16 areas for the density analysis. Second, the density calculation was based on the lengths of the ship trajectory lines, with a search radius of 500 m and an individual cell size of 100 m \(\times\) 100 m. Third, the density values were divided into equal-size sets using a quantile classification method. The top 50% of the total maritime traffic data were extracted as maritime traffic routes, which were then divided into three categories: main route, outer branch route, and inner branch route.  
The contribution of [PERSON] et al. [37] is highly valued as the first attempt to develop a national maritime traffic route for safe navigation of ships through Korean coastal waters. However, the analysis method and extraction process have certain limitations. First, the area division was based on the volume of maritime traffic and the geographic distribution of major ports. Although the traffic volume differed across sea areas, the same threshold of maritime traffic volume was applied to the route extraction in each area, which degraded the consistency and reliability of the relative analysis results. The line density method allows a quantitative analysis because it calculates the length of the ship trajectory lines; however, the analysis result depends on the parameter settings. Furthermore, the quantile classification method used in the route extraction process can excellently explain maritime traffic ratios but is inapplicable to ship density data, which have wide and skewed statistical distributions. For example, cells with density values of 10 and 1500 are displayed in the same color with no difference between them, indicating that extremely high densities are not distinguished from lower densities. Another limitation is that the traffic route is extracted and classified in a polygon-like form by the author rather than by an algorithm based on the 50% density value.  
To overcome the limitations of the previous study, the method proposed in this study performs a density analysis over the entire coast of Korea with no spatial division. Moreover, the new method efficiently automates the processing of large amounts of data using an SQL database. By adopting a spatial-temporal approach, it overcomes the limitations of the AIS data characteristics that influence the existing density analysis methods. Furthermore, the stretching method adopted in the new study is advantageous for visualization. Stretching is a statistical method that reduces the influence of extreme values and extracts the values within a range of one single standard deviation from the mean. Table 5 comprehensively summarizes the differences between this study and [PERSON] et al. [37], each of which analyzed the maritime traffic density in the coastal waters of Korea. The final density analysis results of the two studies are compared in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **[PERSON] et al. [37]** & **This Study** \\ \hline Data volume & 1 week per season, & 1 month per season, \\  & Totaling 1 month & Totaling 4 months \\  & Korean coastal waters & Korean coastal waters \\  & (divided into 16 areas) & Korean coastal waters \\ Density analysis & Line density & Spatial-temporal density \\  & (based on ship tracks) & (based on ship occupancy time) \\ Density values & Summed line length per square meter & Summed hours per square kilometer \\ Visualization & Quantile & Stretch \\  & (10 classes) & (1 standard deviation) \\ \hline \end{tabular}
\end{table}
Table 5: Overall comparison of this study and [PERSON] et al. [37] in terms of data, analysis methods, and symbolization.