Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16151
Title: A probabilistic paradigm for handling uncertain objects in GIS by randomized graph algebra
Authors: Shi, WZ 
Wu, HY
Keywords: GIS
Uncertain objects
Graph algebra
Measure space
Issue Date: 2003
Publisher: Elsevier
Source: Progress in natural science, 2003, v. 13, no. 9, p. 648-657 How to cite?
Journal: Progress in natural science 
Abstract: Probability theory faces difficulties when it is applied to describing uncertain objects in geographic information system (GIS). This is mainly due to the fact that an object in GIS is normally described by a series of discrete vertexes. Modeling uncertainty objects should be therefore based on error of the composed vertexes. This type of model is normally complex and relatively difficult to implement because of many unknown factors, such as the number of vertexes of a polygon, error nature of each individual vertex and error correlation among the vertexes. In this paper, a probabilistic paradigm for handling uncertain objects in GIS by randomized graph algebra is presented. The theoretical basis for this paradigm is the randomized graph algebra-a probability theory for graph-which is newly proposed in this study. Classical probability theory is based on numerical algebra and is also an extension of numerical algebra by further defining probability density within a numerical domain. In the same token, this study begins with defining graph algebra as the basis for probability theory for graph. First, we adopt the theory of graph algebra and further refine the theory by defining the modulo operation for graph. As a result, a graph can thereafter be treated as a "number" and operated by "addition", "subtraction" and others. Second, we construct a measure space by generating sigma-algebra and defining measurable function upon it. The measure space becomes a probability space when the measurable function is a probability density function. Third, we propose the probabilistic paradigm for describing and inferring the uncertainty of geometric objects in GIS by applying the developed randomized graph algebra.
URI: http://hdl.handle.net/10397/16151
ISSN: 1002-0071
EISSN: 1745-5391
DOI: 10.1080/10020070312331344190
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