Uncertainty in Geographical InformationAs Geographic Information Systems (GIS) develop, there is a need to demystify the complex geographical world to facilitate computerization in GIS by the inaccuracies that emerge from man-machine interactions in data acquisition and by error propagation in geoprocessing. Users need to be aware of the impacts of uncertainties in spatial analysis and decision-making. Uncertainty in Geographical Information discusses theoretical and practical aspects of spatial data processing and uncertainties, and covers a wide range of types of errors and fuzziness and emphasizes description and modeling. High level GIS professionals, researchers and graduate students will find this a constructive book. |
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Uncertainty in Geographical Information Jingxiong Zhang,Michael F. Goodchild No preview available - 2002 |
Common terms and phrases
accuracy analysis analytical applications approaches approximation assessment assumed attributes boundaries calculated categorical cells chapter classification combination complex conditional continuous coordinates correlation covariance data sets defined derived described digitised discrete discussed distance distributions effects elements elevation Equation estimates evaluation example fields function further fuzzy fuzzy sets geographical information geostatistical given Goodchild grid ground handling heights important indicator individual interpretation known Kriging land cover levels locations maps mean measured membership methods modelling objects observed obtained occurrences phenomena photogrammetric pixels points polygons positional errors possible probabilistic probability problem propagation random raster reasoning reference Remote Sensing representation represented resolution respectively samples scale segments semivariogram shown in Figure simulation slope sources space spatial spatial dependence specific spectral standard statistical subjective surface surveying Systems techniques terrain theory types uncertainty underlying vagueness values variables variance varying vector