Feature Extraction and Visualization for Symbolic People Flow Data

摘要

People flow information brings us useful knowledge in various industrial and social fields including traffic, disaster prevention and marketing. However, it is still an open problem to develop effective people flow analysis techniques. We suppose compression and data mining techniques are especially important for analysis and visualization of large-scale people flow datasets. This paper presents a visualization tool for large-scale people flow dataset featuring compression and data mining techniques. This tool firstly compresses the people flow datasets using UniversalSAX, an extended method of SAX (Symbolic Aggregate Approximation). Next, we apply natural language algorithms to extract movement patterns. Finally, we visualize trajectories of people flow and extracted features to represent popular walking routes and congestions.