MovePattern: Interactive Framework to Provide Scalable Visualization of Movement Patterns

TL;DR

MovePattern is an interactive, scalable visualization framework that efficiently processes massive movement datasets like GPS and social media data, enabling real-time analysis and customization for movement pattern exploration.

Contribution

The paper introduces a novel framework combining scalable data aggregation with interactive web-based visualization tailored for large-scale movement data.

Findings

Aggregates 180 million movements in minutes

Supports real-time, interactive visualization with on-the-fly customization

Handles high user concurrency with low latency

Abstract

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.