Visual exploration of mobility dynamics based on multi-source mobility datasets and POI information
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Learning more about human mobility is crucial for official decision makers and urban planners. Mobility datasets characterize human daily travel behaviors. Most current researches only studied human dynamics from one kind of mobility dataset. However, people may use different means of transportation to different places for different purposes. Besides, the spatial distributions of different types of point of interests (POIs) reflect the land-use types. How to jointly analyze the multi-source mobility datasets and POI information is a great challenge. In this paper, we adopt multi-source datasets, including taxi dataset, public bicycle system dataset and POI dataset, and propose a visual analytics methodology to explore human mobility dynamics insightfully. Two region–feature–time tensors are constructed first, and a tensor decomposition method is employed to classify the mobility patterns automatically. Then, a new POI–mobility glyph is designed to visualize multi-source datasets in a compact manner. Several interactive visual views are also designed to visualize the spatiotemporal patterns from global, regional and locational perspectives. Case studies based on real-world datasets demonstrate the effectiveness of our method, which supports the visual reasoning of trip purposes and mixed urban functions.
KeywordsMulti-source datasets Visual analysis Human dynamics Tensor decomposition
This work was supported in part by the National Natural Science Foundation of China under Grant 61903109 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grants LY19F020047 and LY17F020023.
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