LEADER 05667nam 22006974a 450 001 9910816273203321 005 20240912174005.0 010 $a1-280-60349-6 010 $a9786610603497 010 $a1-84704-506-5 010 $a0-470-61215-0 010 $a0-470-39481-1 010 $a1-84704-606-1 035 $a(CKB)1000000000335539 035 $a(EBL)700757 035 $a(SSID)ssj0000159998 035 $a(PQKBManifestationID)11158906 035 $a(PQKBTitleCode)TC0000159998 035 $a(PQKBWorkID)10181319 035 $a(PQKB)10563430 035 $a(Au-PeEL)EBL700757 035 $a(CaPaEBR)ebr10361259 035 $a(CaONFJC)MIL60349 035 $a(Au-PeEL)EBL261398 035 $a(MiAaPQ)EBC700757 035 $a(OCoLC)501312934 035 $a(MiAaPQ)EBC261398 035 $a(EXLCZ)991000000000335539 100 $a20060321d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aFundamentals of spatial data quality /$fedited by Rodolphe Devillers, Robert Jeansoulin 210 $aLondon ;$aNewport Beach, CA $cISTE$dc2006 215 $a1 online resource (311 p.) 225 1 $aGeographical information systems series 300 $a"Part of this book adapted from "Qualite de l'information geographique" published in France by Hermes Science/Lavoisier in 2005." 311 $a1-905209-56-8 320 $aIncludes bibliographical references and index. 327 $aFundamentals of Spatial Data Quality; Table of Contents; Foreword; Introduction; PART 1. Quality and Uncertainty: Introduction to the Problem; Chapter 1. Development in the Treatment of Spatial Data Quality; 1.1. Introduction; 1.2. In the beginning; 1.3. Changing the scene; 1.3.1. Accuracy beyond position; 1.3.2. Topology and logical consistency; 1.3.3. Fitness for use; 1.4. Elements of novelty; 1.5. References; Chapter 2. Spatial Data Quality: Concepts; 2.1. Introduction; 2.2. Sources and types of errors; 2.3. Definitions of the concept of quality; 2.3.1. Internal quality 327 $a2.3.2. External quality2.4. Conclusion; 2.5. References; Chapter 3. Approaches to Uncertainty in Spatial Data; 3.1. Introduction; 3.2. The problem of definition; 3.2.1. Examples of well-defined geographical objects; 3.2.2. Examples of poorly defined geographical objects; 3.3. Error; 3.4. Vagueness; 3.5. Ambiguity; 3.5.1. Discord; 3.5.2. Non-specificity; 3.6. Data quality; 3.7. Precision; 3.8. Conclusion: uncertainty in practice; 3.9. References; PART 2. Academic Case Studies: Raster, Chloropleth and Land Use; Chapter 4. Quality of Raster Data; 4.1. Introduction; 4.2. Geometry quality 327 $a4.2.1. Image reference system and modeling of the viewing geometry4.2.1.1. Image reference system in matrix representation; 4.2.1.2. Direct and inverse localization; 4.2.1.3. Geometric transforms of images; 4.2.1.4. Acquisition models; 4.2.2. Definitions; 4.2.2.1. Georeferenced image; 4.2.2.2. Geocoded image; 4.2.2.3. Orthorectified image; 4.2.2.4. Check points; 4.2.2.5. Tie points; 4.2.2.6. Localization error; 4.2.2.7. Mean quadratic error; 4.2.2.8. Error vector field; 4.2.2.9. Native projection of a map; 4.2.3. Some geometry defects; 4.2.3.1. Absolute localization defect 327 $a4.2.3.2. Global defects of internal geometry4.2.3.3. Local defects of internal geometry; 4.2.4. Localization control and global models; 4.2.5. Internal geometry control; 4.3. Radiometry quality; 4.3.1. Radiometry quantities; 4.3.2. Overview of the radiometric defects; 4.3.2.1. Diffraction and defocalization; 4.3.2.2. Polarization of the instrument; 4.3.2.3. Stray light; 4.3.2.4. Aerial photos; 4.3.3. Calibration of the radiometric data; 4.3.3.1. Radiometric calibration; 4.3.3.2. Spectral calibration; 4.3.4. Atmospheric correction; 4.4. References 327 $aChapter 5. Understanding the Nature and Magnitude of Uncertainty in Geopolitical and Interpretive Choropleth Maps5.1. Introduction; 5.2. Uncertainty in geopolitical maps; 5.2.1. Locational uncertainty in geopolitical maps; 5.2.2. Attribute uncertainty in geopolitical maps; 5.3. Uncertainty in interpretive maps; 5.3.1. Construction of interpretive polygonal maps; 5.3.2. Uncertainty in boundaries of interpretive polygonal maps; 5.3.3. Uncertainty in attributes of interpretive polygonal maps; 5.4. Interpretive map case studies; 5.5. Conclusion; 5.6. References 327 $aChapter 6. The Impact of Positional Accuracy on the Computation of Cost Functions 330 $aThis book explains the concept of spatial data quality, a key theory for minimizing the risks of data misuse in a specific decision-making context. Drawing together chapters written by authors who are specialists in their particular field, it provides both the data producer and the data user perspectives on how to evaluate the quality of vector or raster data which are both produced and used. 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