Multiple sources geographic attribute data uncertainty and information fusion schemes

There are many methods for dealing with spatial uncertainty. However, a separate discussion for attribute uncertainty of geographic information is still rare. With the rapid development of multiple sources of geographic data, the uncertainty of attributes and their fusion is important for accuracy assessment and decision making. Attribute uncertainty of geographic information is an important aspect of geographic information quality and will further affect geographic decision making. This paper has discussed the different types of attribute measurement and uncertainty of geo-data and information fusion schemes for multiple sources of geo-data. Two types of attributes, i.e. continuous variable attribute and categorical variable attribute, and their uncertainty, i.e. random and error, vagueness and ambiguity, are discussed. Five geographic information fusion schemes are given for multi-sources uncertainty attribute information, i.e. (a) univariate multi-sources data fusion, (b) multivariate continuous attributes fusion, (c) multivariate categorical attribute fusion, (d) multivariate normalized attribute fusion and (e) multivariate continuous-category attribute fusion. Methods for different fusion schemes are discussed, i.e. maximum likelihood estimation, multivariate regression, Artificial Neural Network, Dempster-Shafer evidence, multi-attribute decision making (MADA), and ordered weighted averaging (OWA). Finally an example of information fusion for precipitation estimation and flood susceptibility assessment are discussed.