Principles of modeling uncertainties in spatial data and spatial analysis

Modeling uncertainties in geographic information science is essential to the development of the field. This book addresses one of the fundamental theoretical issues: uncertainties in spatial data and analysis. Along with the latest research findings, the text provides methods to control uncertainties in GIS applications. The author contributes his own unique research in modeling positional uncertainty based on probability theory and statistics. He introduces new areas, such as uncertainty-based spatial mining, providing a new prospective on the theory and applications of GIS. Researchers, students, and professionalsworking in GIS can benefit from the insightful presentation of new ideas. INDICE: Introduction. Uncertainty Sources of Spatial Data and Spatial Analyses. Mathematical Foundations. Modeling Positional Uncertainties in Linear Features in GIS. Modeling Uncertainties in Digital Evolution Models. Modeling Thematic Uncertainties in GIS Data. Modeling Integrated Positional and Thematic Elements. Modeling Uncertain Topological Relations. Modeling Positional Uncertainties in Overlay Analysis. Modeling Positional Uncertainty in Buffer Analysis. Uncertainty Visualization. Uncertainty Metadata for Spatial Data. Uncertainty-Based Spatial Data Mining. Quality Control for Cadastral Data. Web Service-Based GIS Data Quality Information System.

• ISBN: 978-1-4200-5927-4
• Editorial: CRC Press
• Encuadernacion: Cartoné
• Páginas: 464
• Fecha Publicación: 01/08/2008
• Nº Volúmenes: 1
• Idioma: Inglés