Fundamentals of spatial data quality / / edited by Rodolphe Devillers, Robert Jeansoulin

• Record Nr.: UNINA9910816273203321
• Pubbl/distr/stampa: London ; ; Newport Beach, CA, : ISTE, c2006
• ISBN: 1-280-60349-6; 9786610603497; 1-84704-506-5; 0-470-61215-0; 0-470-39481-1; 1-84704-606-1
• Descrizione fisica: 1 online resource (311 p.)
• Collana: Geographical information systems series
• Altri autori (Persone): DevillersRodolphe; JeansoulinRobert
• Disciplina: 526.0285
• Soggetti: Geographic information systems - Data processing - Quality control
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Part of this book adapted from "Qualite de l'information geographique" published in France by Hermes Science/Lavoisier in 2005."
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Fundamentals 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; 2.3.2. 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; 4.2.1. 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; 4.2.3.2. 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; Chapter 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; Chapter 6. The Impact of Positional Accuracy on the Computation of Cost Functions
• Sommario/riassunto: This 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. It also covers the key concepts in this field, such as: how to describe the quality of vector or raster data; how to enhance this quality; how to evaluate and document it, using methods such as metadata

Handbook on semidefinite, conic and polynomial optimization / / Miguel F. Anjos, Jean B. Lasserre ; editors

• Record Nr.: UNINA9910953421503321
• Pubbl/distr/stampa: New York, : Springer, c2012
• ISBN: 9786613443564; 9781283443562; 1283443562; 9781461407690; 1461407699
• Edizione: [1st ed. 2012.]
• Descrizione fisica: 1 online resource (954 p.)
• Collana: International series in operations research & management science ; ; v. 166
• Altri autori (Persone): AnjosMiguel F; LasserreJean-Bernard <1953->
• Disciplina: 519.6
• Soggetti: Mathematical optimization - Research; Linear programming
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction to Semidefinite, Conic and Polynomial Optimization -- The Approach of Moments for Polynomial Equations -- Algebraic Degree in Semidefinite and Polynomial Optimization -- Semidefinite Representation of Convex Sets and Convex Hulls -- Convex Hulls of Algebraic Sets -- Convex Relations and Integrality Gaps -- Relaxations of Combinatorial Problems via Association Schemes -- Copositive Programming -- Invariant Semidefinite Programs -- A "Joint+Marginal" Approach in Optimization -- An Introduction to Formally Real Jordan Algebras and Their Applications in Optimization -- Complementarity Problems Over Symmetric Conics: A Survey of Recent Developments in Several Aspects -- Convexity and Semidefinite Programming in Dimension-Free Matrix Unknowns -- Positivity and Optimization: Beyond Polynomials -- Self-Regular Interior-Point Methods for Semidefinite Optimization -- Elementary Optimality Conditions for Nonlinear SDPs -- Recent Progress in Interior-Point Methods: Cutting Plane Algorithms and Warm Starts -- Exploiting Sparsity in SDP ; Relaxation of Polynomial Optimization Problems -- Block Coordinate Descent Methods for Semidefinite Programming -- Projection Methods in Conic Optimization -- SDP Relaxations for Non-Commutative Polynomial Optimization -- Semidefinite Programming and Constraint Programming -- The State-of-the-Art in Conic Optimization Software -- Latest Developments in SDPA Family for Solving Large-Scale SDPs -- On the Implementation and Usage of SDPT3: A MATLAB Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0 -- PENNON: Software for Linear and Nonlinear Matrix Inequalities -- SDP Relaxations for Some Combinatorial Optimization Problems -- Computational Approaches to Max-Cut -- Global Approaches for Facility Layout and VLSI Floorplanning -- Euclidean Distance Matrices and Applications -- Sparse PCA: Convex Relaxations, Algorithms and Applications.
• Sommario/riassunto: Semidefinite and conic optimization is a major and thriving research area within the optimization community. Although semidefinite optimization has been studied (under different names) since at least the 1940s, its importance grew immensely during the 1990s after polynomial-time interior-point methods for linear optimization were extended to solve semidefinite optimization problems. Since the beginning of the 21st century, not only has research into semidefinite and conic optimization continued unabated, but also a fruitful interaction has developed with algebraic geometry through the close connections between semidefinite matrices and polynomial optimization. This has brought about important new results and led to an even higher level of research activity. This Handbook on Semidefinite, Conic and Polynomial Optimization provides the reader with a snapshot of the state-of-the-art in the growing and mutually enriching areas of semidefinite optimization, conic optimization, and polynomial optimization. It contains a compendium of the recent research activity that has taken place in these thrilling areas, and will appeal to doctoral students, young graduates, and experienced researchers alike. The Handbook’s thirty-one chapters are organized into four parts: Theory, covering significant theoretical developments as well as the interactions between conic optimization and polynomial optimization; Algorithms, documenting the directions of current algorithmic development; Software, providing an overview of the state-of-the-art; Applications, dealing with the application areas where semidefinite and conic optimization has made a significant impact in recent years.