Authors from Burrough (1992) to Heuvelink et al. (2007) have highlighted the importance of GIS frameworks which can handle incomplete knowledge in data inputs, in decision rules and in the geometries and attributes modelled. It is particularly important for this uncertainty to be characterised and quantified when GI data is used for spatial decision making. Despite a substantial and valuable literature on means of representing and encoding uncertainty and its propagation in GI (e.g.,Hunter and Goodchild 1993; Duckham et al. 2001; Couclelis 2003), no framework yet exists to describe and communicate uncertainty in an interoperable way. This limits the usability of Internet resources of geospatial data, which are ever-increasing, based on specifications that provide frameworks for the ‘GeoWeb’ (Botts and Robin 2007; Cox 2006). In this paper we present UncertML, an XML schema which provides a framework for describing uncertainty as it propagates through many applications, including online risk management chains. This uncertainty description ranges from simple summary statistics (e.g., mean and variance) to complex representations such as parametric, multivariate distributions at each point of a regular grid. The philosophy adopted in UncertML is that all data values are inherently uncertain, (i.e., they are random variables, rather than values with defined quality metadata).
|Title of host publication||Proceedings of the GIS Research UK 16th Annual Conference GISRUK 2008|
|Publisher||Manchester Metropolitan University|
|Number of pages||5|
|Publication status||Published - Apr 2008|
Bibliographical noteGIS Research UK 16th Annual Conference GISRUK 2008, 2-4 April 2008, Manchester (UK).
- GIS frameworks
- GI data
- spatial decision making
- encoding uncertainty
- Internet resources
- geospatial data
- online risk management chains