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arXiv · 1908.00181

EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data

Zhuochen Jin·Nan Cao·Yang Shi·Hanghang Tong·Yingcai Wu
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Abstract

The rapid development of urbanization during the past decades has significantly improved people's lives but also introduced new challenges on effective functional urban planning and transportation management. The functional regions defined based on a static boundary rarely reflect an individual's daily experience of the space in which they live and visit for a variety of purposes. Fortunately, the increasing availability of spatiotemporal data provides unprecedented opportunities for understanding the structure of an urban area in terms of people's activity pattern and how they form the latent regions over time. These ecological regions, where people temporarily share a similar moving behavior during a short period of time, could provide insights into urban planning and smart-city services. However, existing solutions are limited in their capacity of capturing the evolutionary patterns of dynamic latent regions within urban context. In this work, we introduce an interactive visual analysis approach, EcoLens, that allows analysts to progressively explore and analyze the complex dynamic segmentation patterns of a city using traffic data. We propose an extended non-negative Matrix Factorization based algorithm smoothed over both spatial and temporal dimensions to capture the spatiotemporal dynamics of the city. The algorithm also ensures the orthogonality of its result to facilitate the interpretation of different patterns. A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time. We evaluate the effectiveness of our system via case studies using a real-world dataset and a qualitative interview with the domain expert.

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Zhuochen Jin, Nan Cao, Yang Shi, Hanghang Tong, Yingcai Wu. 2019-07-29. EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data. https://arxiv.org/abs/1908.00181

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