Exploring the Hidden Potential of Common Spatial Data Models to Visualize Uncertainty

Abstract

Common Spatial Data Models (SDMs) such the vector, raster, and quadtree have well understood and widely practiced conventions of storage and visualization. This paper explores what happens when the conventions of visualization are not strictly adhered to, and the SDMs are used in an atypical fashion. A framework based on a quasi similarity measure is presented, which quantifies (in terms of "distance") the relationship between the storage format and the visualization output, following an accepted protocol. This research used a transformation process (Tp) to define this distance. Then, the atypical use of the quadtree SDM to represent choropleth spatial boundary uncertainty and attribute uncertainty was quantified using the same framework. This research shows that if a SDM is used outside of its original context, then the distance between the storage format and its visual output can alter; in our case, the distance decreased. This result was interpreted as evidence for the creation of a new spatial data structure. The formalization of the relationship between an SDM and its visual output will be valuable for future exploration of the non-conventional visualization of common SDMs.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 5 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.