Abstract
Mining the mobile pattern of the urban population plays an important role in city construction, and visual analysis is a powerful technique in studying mobile patterns. In this paper, based on the taxi trajectory data in Hangzhou, we share our design for an interactive visual analytic system, which helps analyzers leverage their domain knowledge to gain insight into travel patterns, including travel time rules of tourists and the distribution rules of pick-up and drop-off locations. Besides, our system can present the dynamic travel process and the Point of Interest (POIs) information of the origin and the destination. A case study has been conducted, which verifies that our system can provide tools for urban managers or urban experts on the design of scenic spot open entrances and exits and travel route planning.
Highlights
In recent years, with the development of Internet of Things (IoT) technologies and the growing of urban populations, urban data, such as social entertainment data, trajectory data and financial data, is exponentially growing
Thousands of taxis produce a lot of data, which is typically spatiotemporal data, every day
With the help of visualization techniques, we can find the travel pattern of a large scale people based on taxi data, which provides decision-making assistance for city managers through visual analysis
Summary
With the development of Internet of Things (IoT) technologies and the growing of urban populations, urban data, such as social entertainment data, trajectory data and financial data, is exponentially growing. With the help of visualization techniques, we can find the travel pattern of a large scale people based on taxi data, which provides decision-making assistance for city managers through visual analysis. In this paper, based on taxi trajectory data of Hangzhou, we provide a visual analysis system with the scenic West Lake as the target area. Combined with the background of Hangzhou tourism city, through the design of the visualization system, the travel patterns specific to tourists can be explored by visual analysis. Our system demonstrates the entire process of a trip and uses the clustering algorithm to group the tourists For these tourists, our system can offer both time-consuming comparisons and changes in spatial distance.
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