Visualising Uncertainty in Spatially-Referenced Attribute Data using Hierarchical Spatial Data Structures

This paper presents the use of hierarchical spatial data structures to visualise uncertainty in spatial information. This is demonstrated using spatial data from the New Zealand 2001 Census. Current visualisation of uncertainty techniques are evaluated, and a new method is proposed. This paper assesses the value of hierarchical tree structures as a geovisualisation of attribute uncertainty technique. Two such structures will be compared: the region quadtree and the Hexagonal or Rhombus (HoR) quadtree, both variable resolution structures. An area where attribute data is uncertain will show less resolution through the data structure than an area that is not, exemplifying a metaphor of detail. The major question is: can an uncertainty value be effectively communicated using tree structures when simultaneously displayed with the attribute information? Principles of possible implementations are presented through outlining a demonstration program, TRUST (The Representation of Uncertainty using Scaleunspecific Tessellations).