MovePattern：提供运动模式可伸缩可视化的交互式框架
MovePattern: Interactive Framework to Provide Scalable Visualization of Movement Patterns
摘要：
全球定位系统轨迹、交通网络和社交媒体等移动数据源的迅速增长，为分析人员提供了以近乎实时的方式探索地理移动的集体模式的机会。一个快速和交互式的可视化框架可以帮助分析人员理解这些大量的动态变化的数据集。然而，以往的研究运动可视化要么忽略了地理运动的独特属性或无法处理今天的海量数据。在本文中，我们开发MovePattern，一个新的框架，1）有效地构建一个简洁的多层次的运动视图，使用可扩展的和空间感知的MapReduce为基础的方法和2）提出了一个快速和高度交互式的基于Web的环境，从事基于矢量的可视化，包括在飞行中的定制和能力，以增强分析功能，通过存储元数据的地方和运动。我们评估的框架使用的Twitter用户从地理标记的推文捕获的运动。实验证实，我们的框架能够在几分钟内聚合近1.8亿个动作。此外，我们在框架的前端运行了一系列压力测试，以确保并发的用户查询不会导致用户响应的长延迟。
英文摘要：
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.
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