# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 3 Method: Data Modeling of Marine Casualty
### Requirements Identification  
Requirements analysis is the starting point of data modeling, aimed at identifying and formalizing the real-world entities, attributes, and relationships covered by the data model. This process provides the foundation for the definition of the application schema and the development of the FC.  
To establish a foundation for structuring marine casualty data, three representative sources were reviewed: the GISIS managed by the IMO [28], the Marine Accident Investigation Branch (MAIB) of the United Kingdom [29], and the Maritime Transportation Safety Information System (MTIS) of the Republic of Korea [30]. These datasets were selected due to their relevance, public accessibility, and coverage of incident types.  
While all three contain useful information, they differ in structure and terminology, requiring a comparative analysis to identify common elements and reconcile classification differences. Among the datasets, certain attributes were commonly available and  
Figure 2: S-100-based procedure for marine casualty data modeling.  
consistently structured, including incident type, time of occurrence, and geographical location. These were identified as core attributes essential for spatial representation and integration into ENCs. In contrast, information on human casualties--such as fatalities, injuries, and missing persons--was explicitly provided in MTIS, while in GISIS and MAIB, such information had to be extracted from unstructured textual descriptions.  
Regarding incident classification, both GISIS and MAIB follow the IMO _MSC-MEPC.3/Circ.4/Rev.1_ guidelines [31], whereas MTIS applies a separate taxonomy based on the _Administrative Guidelines on the Investigation of and Inquiry into Marine Accidents_ established by the Republic of Korea [32]. Although these classification systems share similar conceptual foundations, they differ in terminology, granularity, and categorization logic, making direct alignment challenging. To enable consistent structuring and facilitate integration into a unified model, a reclassification process was carried out. This involved identifying semantically equivalent categories across the datasets and evaluating their applicability for spatial portrayal in navigational systems. Table 2 presents the resulting correspondence matrix, which also helped to reveal overlapping or ambiguous categories that required consolidation or clarification.  
\begin{table}
**GISIS/MAIB Classification** & **MTIS Classification** & **Categorized Navigational Utility** \\ \hline Collision & Collision & Collisions or contacts with vessels or external objects function similarly in route avoidance decisions. Navigators can identify high-risk areas and take actions such as slowing down or maneuvering to avoid potential collisions. \\ \hline Contact & \begin{tabular}{p{113.8 pt} p{113.8 pt}} Contact, \\ floating object \\ \end{tabular} & \
\begin{tabular}{p{113.8 pt} p{113.8 pt}} Contact, \\ \end{tabular} &
\begin{tabular}{p{113.  
For example, 'collision' and 'contact' are both physical impact events with similar implications for route planning and navigational response. In the context of digital chart systems, combining them into a single category improves clarity and usability. In contrast, some categories such as'marine pollution' represent secondary consequences of other incidents and are not suitable as primary classifications. Similarly, incidents such as 'occupational accidents', which occur internally on board and lack a clear spatial dimension, were excluded due to their limited applicability to location-based decision-making. Based on these criteria, this paper established a classification scheme that prioritizes incident types with spatial significance and relevance to maritime transportation safety. The selected categories form the basis for defining feature types in the subsequent data modeling process.