Qualitative Spatial Reasoning Extracting and Reasoning with Spatial Aggregates


  • Chris Bailey-Kellogg
  • Feng Zhao




Reasoning about spatial data is a key task in many applications, including geographic information systems, meteorological and fluid-flow analysis, computer-aided design, and protein structure databases. Such applications often require the identifi- cation and manipulation of qualitative spatial representations, for example, to detect whether one object will soon occlude another in a digital image or efficiently determine relationships between a proposed road and wetland regions in a geographic data set. Qualitative spatial reasoning (QSR) provides representational primitives (a spatial "vocabulary") and inference mechanisms for these tasks. This article first reviews representative work on QSR for data-poor scenarios, where the goal is to design representations that can answer qualitative queries without much numeric information. It then turns to the data-rich case, where the goal is to derive and manipulate qualitative spatial representations that efficiently and correctly abstract important spatial aspects of the underlying data for use in subsequent tasks. This article focuses on how a particular QSR system, SPATIAL AGGREGATION, can help answer spatial queries for scientific and engineering data sets. A case study application of weather analysis illustrates the effective representation and reasoning supported by both data-poor and data-rich forms of QSR




How to Cite

Bailey-Kellogg, C., & Zhao, F. (2003). Qualitative Spatial Reasoning Extracting and Reasoning with Spatial Aggregates. AI Magazine, 24(4), 47. https://doi.org/10.1609/aimag.v24i4.1730