# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 7 Conclusions  
Marine traffic in the Aegean Sea has dramatically increased throughout recent years, mainly due to the need for marine transportation of fuels, materials and goods to and from the Middle East countries and the development of commercial agreements with Chinese factories and logistics. The increase in maritime traffic, despite the commercial benefits to the region, is posing significant challenges to the  
Figure 16: The network of Figure 15, layered upon a Google map of the area. Figure 15: SNA of real ship transportation data for a given day (27/09/2018) in the Aegean Sea. Nodes are sized according to the betweenness centrality metric and colored according to their modularity class. Edges are weighted according to the number of ships passing between the nodes. preservation of the local ecosystem and safety as well. This danger is further deteriorated from lack of regulations and unregulated waterways. Marine accidents may cause major ecological disasters and have socio-economic impacts in Greece. Monitoring the marine data is of major importance and one of the primary goals of the current research. Real-time monitoring and alerting can be extremely useful to local authorities, companies, NGO's and the public in general. Apart from real-time applications, the discovery of hidden patterns and trends is also significant. Towards this direction, a simulation tool for marine data has been designed and developed. Recent advances in data storage and management as well as the great progress on data analytics methods have enabled Big Data analytics by researchers and developers. Based on free and commercial solutions, nowadays, an analyst could create advanced applications using minimal effort. Existing and newly developed solutions have been incorporated in order to create a framework. The framework analyses historical data about ships and area conditions, of varying time and space granularity, measures critical parameters that could influence the hazard of a specific region and clusters such data according to their similarity. This process covers research question no. 1, namely the incorporation of various heterogeneous maritime and weather data towards identifying hazardous conditions. Upon this unsupervised step, the degree of hazard is estimated and along with the other parameters is fed into a special type of Bayesian networks [28; 29], in order to infer on future situations, thus, simulating future data based on past conditions and addressing the related research question no. 2. The use of such a system is particularly beneficial for multiple stakeholders, such as the port authorities, the ministry of Mercantile Marine, etc. mainly due to the fact that specific policy options can be evaluated and re-designed based on feedback from our framework. Finally, maritime flow has been modeled and analyzed as a social network to address the third research question, which is the creation of useful visualization and insights for the domain. Experimental results have portrayed that the suggested technique is more beneficial than other methods, such as neural nets and linear models for a variety of reasons, ranging from accuracy to computational issues. The tool is customizable in the sense that new criteria could be added, new danger estimation functions could also be supported and clustering could only be serving as a data organization method. In order to stress the practical applicability of the proposed study, the simulation framework could serve as an analysis and risk assessment tool as well as a decision support system for policy-making process by local authorities and domain experts. [PERSON] is the sole author of this article, therefore, conceptualization, software, validation, writing--review and editing, were carried out by himself. This research was funded by AMINESS, Analysis of Marine Information for Environmentally Safe Shipping project, granted by \"COOPERATION 2011--Partnerships of Production and Research Institutions in Focused Research and Technology Sectors\" action.