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OL-HEATMAP: Effective Density Visualization of Multiple Overlapping Rectangles
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DOI：10.1016/j.bdr.2021.100235.png)
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摘要
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Visualization of the density of multiple overlapping axis-aligned objects is a challenging computational problem that can inform large-scale visual analytics, in diverse domains. For example, when dealing with crowd simulations, we care about constructing interaction maps, and in urban planning we care about city areas mostly frequented by people, to name a few. The primary objective of this research is, given a large set of axis-aligned two-dimensional (2D) objects, or simply rectangles, to devise efficient and effective data visualization methods that inform whether, where and how much these rectangles overlap. Currently, such visualizations rely on inefficient implementations of determining the size of the overlapping rectangles that do not scale well and are hard to accomplish. Approximate methods have also been proposed in the literature. To the contrary of these approaches, we aim to address this problem by exploiting state-of-the-art computational geometry methods based on the sweep line paradigm. These methods are fast and can determine the exact size of the overlap of multiple axis aligned objects, therefore can effectively inform the visualization method. Towards that end, we present OL-HEATMAP, a novel type of a heat-map visualization that can be used to represent and perceive density of overlapping rectangles. Our experimental evaluation demonstrates the effectiveness of the proposed method in terms of both accuracy and running time for synthetic and real-world data-sets. (C) 2021 Elsevier Inc. All rights reserved.
Keyword:
Information visualization
Density-based visualization
Overlapping objects
Computational geometry
Heat-maps
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论文数:
421
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1.1K
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