The rapid growth of movement data sources such as GPS traces, trafic networks and social media have provided analysts with the opportunity to explore collective patterns of geographical movements in a nearly real-time fashion. A fast and interactive visualization framework can help ana- lysts to understand these massive and dynamically changing datasets. However, previous studies on movement visual- ization either ignore the unique properties of geographical movement or are unable to handle today's massive data. In this paper, we develop MovePattern, a novel framework to 1) efficiently construct a concise multi-level view of movements using a scalable and spatially-aware MapReduce-based ap- proach and 2) present a fast and highly interactive web- based environment which engages vector-based visualiza- tion to include on-the-y customization and the ability to enhance analytical functions by storing metadata for both places and movements. We evaluate the framework using the movements of Twitter users captured from geo-tagged tweets. The experiments conformed that our framework is able to aggregate close to 180 million movements in a few minutes. In addition, we run series of stress tests on the front-end of the framework to ensure that simultaneous user queries do not lead to long latency in the user response.