Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses

Wenzhong Shi’s primary innovation is the integration of positional uncertainty and attribute uncertainty for both data and analysis. He presents case studies using common GIS techniques to demonstrate approaches that describe positional uncertainties in data and also to show how they would affect the results of analysis. His book is a well-organized text, emphasizing the recent literature on positional uncertainty. ... [The book] provides a template for the incorporation of spatial and attribute uncertainty in spatial analysis, and the template could be expanded to techniques more commonly employed by regional scientists. ... This text is an important reference for someone embarking on a research effort in spatial data uncertainty and modeling ...—Eun-Hye Enki Yoo and Jared Aldstadt, Department of Geography, University at Buffalo, The State University of New York (SUNY), in Journal of Regional Science, Vol. 51, No. 4, 2011

• 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.