Supporting Large-scale Geographical Visualization in a Multi-granularity Way.
WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining Marina Del Rey CA USA February, 2018(2018)
摘要
Urban data (e.g., real estate data, crime data) often have multiple attributes which are highly geography-related. With the scale of data increases, directly visualizing millions of individual data points on top of a map would overwhelm users' perceptual and cognitive capacity and lead to high latency when users interact with the data. In this demo, we present ConvexCubes, a system that supports interactive visualization of large-scale multidimensional urban data in a multi-granularity way. Comparing to state-of-the-art visualization-driven data structures, it exploits real-world geographic semantics (e.g., country, state, city) rather than using grid-based aggregation. Instead of calculating everything on demand, ConvexCubes utilizes existing visualization results to efficiently support different kinds of user interactions, such as zooming & panning, filtering and granularity control. Our system can be accessed at http://115.146.89.158/ConvexCubes/.
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