Effective Density Visualization of Multiple Overlapping Axis-Aligned Objects

Large-scale analytics of multiple overlapping axis-aligned objects is a challenging computational geometry problem that can inform several applications and services, in diverse domains. The primary focus of this research is, given many axis-aligned objects, to devise efficient and effective data visualization methods that inform whether, where and how much they overlap. Currently, such visualizations rely on inefficient implementations to determine the size of the overlap of objects. We 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 propose OL-HeatMap, a novel density-based visualization technique that can be used to represent complex information about overlapping objects. Our experimental evaluation demonstrates the effectiveness of the proposed method in several synthetic and real-world data sets.