ISSN:
1573-8981
Keywords:
Dempster-Shafer belief function
;
Representation
;
Geographic information system (GIS)
;
Data integration
;
Spatially distributed map pattern
;
Favorability function
;
Fuzzy set
;
Certainty factor
;
Geopotential map
;
Prediction
;
Natural resources
;
Natural hazard
;
Probability
Source:
Springer Online Journal Archives 1860-2000
Topics:
Geosciences
Notes:
Abstract In mineral exploration, resource assessment, or natural hazard assessment, many layers of geoscience maps such as lithology, structure, geophysics, geochemistry, hydrology, slope stability, mineral deposits, and preprocessed remotely sensed data can be used as evidence to delineate potential areas for further investigation. Today's PC-based data base management systems, statistical packages, spreadsheets, image processing systems, and geographical information systems provide almost unlimited capabilities of manipulating data. Generally such manipulations make a strategic separation of spatial and nonspatial attributes, which are conveniently linked in relational data bases. The first step in integration procedures usually consists of studying the individual charateristics of map features and interrelationships, and then representing them in numerical form (statistics) for finding the areas of high potential (or impact). Data representation is a transformation of our experience of the real world into a computational domain. As such, it must comply with models and rules to provide us with useful information. Quantitative representation of spatially distributed map patterns or phenomena plays a pivotal role in integration because it also determines the types of combination rules applied to them. Three representation methods—probability measures, Dempster-Shafer belief functions, and membership functions in fuzzy sets—and their corresponding estimation procedures are presented here with analyses of the implications and of the assumptions that are required in each approach to thematic mapping. Difficulties associated with the construction of probability measures, belief functions, and membership functions are also discussed; alternative procedures to overcome these difficulties are proposed. These proposed techniques are illustrated by using a simple, artificially constructed data set.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02272809
Permalink
