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. 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 nonnegative 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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This work was supported in part by the Open Project Program of the State Key Lab of CAD&CG (Grant No. A2013), Zhejiang University, the National Natural Science Foundation of China (Grant No. 61802283), the Fundamental Research Funds for the Central Universities in China, and the Natural Science Foundation of Shanghai, China (Grant No. 20ZR1461500).
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Jin, Z., Cao, N., Shi, Y. et al. EcoLens: visual analysis of ecological regions in urban contexts using traffic data. J Vis (2020). https://doi.org/10.1007/s12650-020-00707-1
- Visual analysis
- Urban segmentation
- Matrix factorization
- Traffic data