Spatialization is the transformation of high-dimensional data into lower-dimensional geometric representations on the basis of computational methods and spatial metaphors. Its aim is to enable people to discover patterns and relationships within complex, n-dimensional data while leveraging existing perceptual and cognitive abilities. Spatialization can be applied to various types of data, from numerical attributes to text documents and imagery.

Spatial Metaphors

The main cognitive underpinning of spatialization lies in the extensive use of spatial metaphors, including cartographic and geographic metaphors, such as map, scale, distance, region, and so forth, which enable users to “see” n-dimensional relationships within a low-dimensional visualization. Empirical support for this approach is growing. For example, human subject studies have shown that people expect that distances between point symbols representing text documents correspond to ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles