  The rapid growth of movement data sources such as GPS traces, traffic
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 analysts to
understand these massive and dynamically changing datasets. However, previous
studies on movement visualization 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 approach and 2) present a fast and highly interactive webbased
environment which engages vector-based visualization to include on-the-fly
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
confirmed 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.
