Trajectory analysis using Automatic Identification System (AIS) in New Zealand waters

Trajectory analysis is one of the most actively researched areas of spatiotemporal databases. Exploring and analysing large datasets of movement data has become a vital part of research in many disciplines and decision-making fields. The major challenge involved during the analysis process of trajectory data is to visualize, understand and extract meaningful patterns (Adrienko & Adrienko, 2011) out of millions of locations collected from Automatic Identification Systems (AIS) data points. AIS datasets are used in the maritime industry to assist in tracking and monitoring vessel movements. The ultimate aim of the study is to understand the characteristics of different types (Dodge, Weibel, & Forootan, 2009) of vessels using AIS movement data. The intention of the study is also to outline challenges encountered during this thesis and describe approaches taken to overcome them. AIS movement datasets are voluminous and are coded via a complex standard. Therefore, to conduct analysis on raw data to trajectory involved a two-phased methodological process. The first phase focused on development of a decoder to extract significant information from the raw data. The information extracted from movement data was then utilized to perform knowledge discovery in regard to dynamic objects. The second phase centred on trajectory analysis utilizing proposed spatio-temporal approach and clustering techniques. Each phase accounted for a part of the contribution made towards this thesis. Phase 1 primarily focused on handling the large dataset and development of a decoder. Given a large dataset (2GB 30 Million rows) of spatio-temporal movement observations, the goal was to perform a segmentation analysis into