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The rapid development of urbanization during the past decades hassignificantly improved people's lives but also introduced new challenges oneffective functional urban planning and transportation management. Thefunctional regions defined based on a static boundary rarely reflect anindividual's daily experience of the space in which they live and visit for avariety of purposes. Fortunately, the increasing availability of spatiotemporaldata provides unprecedented opportunities for understanding the structure of anurban area in terms of people's activity pattern and how they form the latentregions over time. These ecological regions, where people temporarily share asimilar moving behavior during a short period of time, could provide insightsinto urban planning and smart-city services. However, existing solutions arelimited in their capacity of capturing the evolutionary patterns of dynamiclatent regions within urban context. In this work, we introduce an interactivevisual analysis approach, EcoLens, that allows analysts to progressivelyexplore and analyze the complex dynamic segmentation patterns of a city usingtraffic data. We propose an extended non-negative Matrix Factorization basedalgorithm smoothed over both spatial and temporal dimensions to capture thespatiotemporal dynamics of the city. The algorithm also ensures theorthogonality of its result to facilitate the interpretation of differentpatterns. A suite of visualizations is designed to illustrate the dynamics ofcity segmentation and the corresponding interactions are added to support theexploration of the segmentation patterns over time. We evaluate theeffectiveness of our system via case studies using a real-world dataset and aqualitative interview with the domain expert.

EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data