Illegal, unreported, and unregulated (IUU) fishing is a key driver of global overfishing, threatens marine ecosystems, puts food security and regional stability at risk, and is linked to major human rights violations and even organized crime [8]. The ability to predict both where and when IUU fishing is likely occurring provides significant advantages to coast guards with limited resources. In this paper, we provide three resources to aid in the ability to identify IUU fishing. First, we created an algorithm to identify refrigeration ships that often meet with fishing vessels to transport the catch to port. Next, we explored the relationships between these vessels and ports. To do so, we displayed not only the traffic coming and going, but also the statistics surrounding the vessels next visited port via an interactive dashboard. Lastly, we explored an Item Response Theory (IRT) Model that bears a relationship between variables obtained from the fishing vessels’ AIS communication and probability that IUU fishing activity is occurring. We also created and used a Density Based Spatial Clustering of Applications with Noise (DBSCAN) model to explore areas where IUU fishing occurs most often. We approached the problem as an exploratory data problem, with the primary objective of understanding the significance of certain parameters, models, or instances of IUU activity in the data as an initial point of reference for future work.