Abstract
Space-filling visualization techniques have proved their capability in visualizing large hierarchical structured data. However, most existing techniques restrict their partitioning process in vertical and horizontal direction only, which cause problem with identifying hierarchical structures. According to Gestalt research, limiting tree map visualisation to rectangles blocks the utilisation of human capability on object recognition, due to the same fixed size (90 degrees) of all the angles of the shapes in the tree visualisation. However, this assertion was only supported by theory and not rooted in empirical perception data. We conducted a series of controlled experiments to investigate the effect of shape variation of data elements and container in visual data analysis process. We first studied how shape variation affects user's perception in the visual data analysis process. We compared combined treemap with traditional rectangular treemaps, slice a dice treemaps and squarifed treemaps. Finally, we demonstrated the effect of the new approach which combines rectangular and non-rectangular treemaps and validate the method based on the empirical results.
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