MovePattern: Interactive framework to provide scalable visualization of movement patterns

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.