When compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t

Overview. Introduction. Sources of Uncertainty in Spatial Data and Spatial Analysis. Mathematical Foundations. Modeling Uncertainties in Spatial Data. Modeling Positional Uncertainty in Spatial Data. Modeling Attribute Uncertainty. Modeling Integrated Positional and Attribute Uncertainty. Modeling Uncertain Topological Relations. Modeling Uncertainties in Spatial Model. Uncertainty in Digital Elevation Models. Modeling Uncertainties in Spatial Analyses. Modeling Positional Uncertainties in Overlay Analysis. Modeling Positional Uncertainty in Buffer Analysis. Modeling Positional Uncertainty in Line Simplification Analysis. Quality Control of Spatial Data. Quality Control for Object-Based GIS Data. Quality Control for Field-Based GIS Data. Improved Interpolation Methods for Digital Elevation Model. Presentation of Data Quality Information. Visualization of Uncertainties in Spatial Data and Analyses. Metadata on Spatial Data Quality. Web Service-Based Spatial Data Quality Information System. Epilog.