Change is constant. Why should your data stand still?
There is so much we can learn about biodiversity by observing and analyzing changes over time. With MOTIL, we use GBIF occurrence data to track and visualize the movement of species. Typically, tracking requires that animals be fitted with tracking devices and monitored constantly. However these devices are costly, and affixing them to the animal can be invasive and dangerous. We demonstrate that we can use data mining and artificial intelligence techniques to infer the movement of animals from visual observations. Using GBIF occurrence data, we place a grid over a region of interest and estimate the transition likelihood between adjacent grid locations. In this submission we include source code, and a runnable Java application that analyzes black bear occurrences in north-eastern United States. We include a PDF that describes our approach and contains screenshots of our analysis, and how a complete visualization would appear in our proposed application. A completed application will utilize dynamic visualization to show movement and potential tracks of animals, their likely habitat, and even species interaction. These tracks will help biodiversity experts monitor species, and can also identify areas that need increased monitoring- providing a positive feedback loop to increase tracking accuracy and predictive capabilities.