Recent years have seen a rapid growth in movement research owing to new technologies contributing to the miniaturization and reduced costs of tracking devices. Similar trends have occurred in how environmental data are being collected (e.g., through satellites, unmanned aerial vehicles, and sensor networks). However, the development of analytical techniques for movement research has failed to keep pace with the data collection advances. There is a need for new methods capable of integrating increasingly detailed movement data with a myriad of contextual data - termed context aware movement analysis (CAMA). CAMA investigates more than movement geometry, by including biological and environmental conditions that may influence movement. However, there is a shortage of methods relating movement patterns to contextual factors, which is still limiting our ability to extract meaningful information from movement data. This thesis contributes to this methodological research gap by assessing the state-of-the art for CAMA within movement ecology and human mobility research, developing innovative methods to consider the spatio-temporal differences between movement data and contextual data and exploring computational methods that allow identification of patterns in contextualized movement data. We developed new methods and demonstrated how they facilitated and improved the integration between high frequency tracking data and temporally dynamic environmental variables. One of the methods, multi-channel sequence analysis, is then used to discover varying human behaviour relative to weather conditions in a large human GPS tracking dataset from Scotland. The second method is developed for combing multi-sensor satellite imagery (i.e., image fusion) of differing spatial and temporal resolutions. This method is applied to a GPS tracking data on maned wolves in Brazil to understand fine-scale movement behaviours related to vegetation changes across seasons. In summary, this thesis provides a significant development in terms of new ideas and techniques for performing CAMA for human and wildlife movement studies.