Patent landscaping is a significant activity for modern businesses. Understanding and taking action on information gleaned from analyses of intellectual property (IP) in specific product or technology domains is necessary for many business decisions. Examples include decisions about future product development, about how to guard against litigation threats from competitors, about how to set R&D priorities, about how to value IP for sale or licensing, and about how to target companies for mergers and acquisitions. This paper proposes a novel method for visualizing patent landscapes that supports the complex hierarchical, multi-dimensional, multi-typed data found in this domain. Our solution directly addresses the problem of how to provide high-level overviews of such a complex domain while at the same time providing enough detail to draw attention to the most significant areas of difference for further drill-down leading to actionable intelligence.