A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data

Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can obtain specific positions, shapes, colors, etc. and people’s visual and image thinking can play a role. In addition, the theories and methods used in the space field could be applied to help people observe and analyze abstract data. However, current research has limited the use of these space theories and methods. For instance, many existing map theories and methods are not well combined. In addition, it is difficult to fully display data in virtual terrains, such as showing the structure and relationship at the same time. Facing the above problems, this study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective. First, the conversion from high-dimensional non-location data to two-dimensional discrete points is achieved by a dimensionality reduction algorithm to reflect the data relationship. Based on this, kernel density estimation interpolation and fractal noise algorithms are used to construct terrain features in the virtual terrains. Under the control of the kernel density search radius and noise proportion, a multi-scale terrain model is built with the help of level of detail (LOD) technology to express the hierarchical structure and support the multi-scale analysis of data. Finally, experiments with actual data are carried out to verify the proposed method.