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Bibliografická citace

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BK
New Jersey : John Wiley & Sons, c2003
xi,436 s. : il.

objednat
ISBN 0-471-21176-1 (váz.)
Obsahuje fotografie, grafy, mapy, bibliografické odkazy a rejstřík, údaje o autorech
Systémy informační geografické - pojednání
000033224
Science/Geography // Clear, up-to-date coverage of methods for analyzing geographical information in a GIS context // Geographic Information Analysis presents clear and up-to-date coverage of the foundations of spatial analysis in a geographic information systems environment. Focusing on the universal aspects of spatial data and their analysis, this book covers the scientific assumptions and limitations of methods available in many geographic information systems. // Throughout, the fundamental idea of a map as a realization of a spatial stochastic process is central to the discussion. Key spatial concepts are covered, including point pattern, line objects and networks, area objects, and continuous fields. Analytical techniques for each of these are addressed, as are methods for combining maps, exploring multivariate data, and performing computationally intensive analysis. Appendixes provide primers on basic statistics and linear algebra using matrices. // // Complete with chapter objectives, summaries, “thought exercises,” a wealth of explanatory diagrams, and an annotated bibliography, Geographic Information Analysis is a practical book for students, as well as a valuable resource for researchers and professionals in the industry. // DAVID O’SULLIVAN, PhD // is Assistant Professor of Geography at The Pennsylvania State University in University Park, Pennsylvania. // // // i u ioy i vai // formerly Professor of Geography at Birkbeck College in the University of London,
UK, is currently Director of Learning Programmes at UKeUniversities Worldwide. He is also the author of Computer Programming for Geographers (with J. A. Dawson) and coeditor of Visualization in Geographic Information Systems (with Hilary M. Hearnshaw), both published by Wiley. // Cover Design: Anne Michele Abbott // Cover Image: Early example of geographic information analysis of cholera deaths in London, 1854. Courtesy of Professor R. R. Frerichs, UCLA Dept, of Epidemiology, Los Angeles, California. // Subscribe to our free Engineering eNewsletter at www.wiley.com/enewsletters // Visit www.wiley.com/engineering // WILEY // wiley.com // Quad // // \ \ // s’ // \  vT- 1 // V  \§ ’ \ j // ? v) L // _ <7 _ // Contents // Preface // ix // 1 Geographie Information Analysis and Spatial Data 1 // Chapter Objectives 1 // 1.1 Introduction 2 // 1.2 Spatial Data Types 4 // 1.3 Scales for Attribute Description 11 // 1.4 GIS Analysis, Spatial Data Manipulation, and // Spatial Analysis 17 // 1.5 Conclusion 22 // Chapter Review 23 // References 24 // 2 The Pitfalls and Potential of Spatial Data 26 // Chapter Objectives 26 // 2.1 Introduction 2 7 // 2.2 The Bad News: The Pitfalls of Spatial Data 28 // 2.3 The Good News: The Potential of Spatial Data 34 // 2.4 Preview: The Variogram Cloud and the Semivariogram 45 // Chapter Review 49 // References 49 // 3 Fundamentals: Maps as Outcomes of Processes // Chapter Objectives // 3.1 Introduction // 3.2 Processes and the Patterns They Make // 3.3 Predicting
the Pattern Generated by a Process // 3.4 More Definitions // 3.5 Stochastic Processes in Lines, Areas, and Fields // 3.6 Conclusion Chapter Review References // MASARYKOVA UNIVERZITA V BRNE // prírodovedecká fakulta // GEOGRAFICKÝ ÚSTAV // KNIHOVNA // 611 37 BRNO, KOTLÁRSKÁ 2 V // 51 // 51 // 52 // 53 58 64 66 73 75 75 // vi // Contents // 4 Point Pattern Analysis 77 // Chapter Objectives 77 // 4.1 Introduction 78 // 4.2 Describing a Point Pattern 79 // 4.3 Density-Based Point Pattern Measures 81 // 4.4 Distance-Based Point Pattern Measures 88 // 4.5 Assessing Point Patterns Statistically 95 // 4.6 Two Critiques of Spatial Statistical Analysis 108 // 4.7 Conclusion 110 // Chapter Review 112 // References 113 // 5 Practical Point Pattern Analysis 115 // Chapter Objectives 115 // 5.1 Point Pattern Analysis versus Cluster Detection 116 // 5.2 Extensions of Basic Point Pattern Measures 123 // 5.3 Using Density and Distance: Proximity Polygons 126 // 5.4 Note on Distance Matrices and Point Pattern Analysis 129 // 5.5 Conclusion 132 // Chapter Review 132 // References 133 // 6 Lines and Networks 135 // Chapter Objectives 135 // 6.1 Introduction 136 // 6.2 Representing and Storing Linear Entities 137 // 6.3 Line Length: More Than Meets the Eye 142 // 6.4 Connection in Line Data: Trees and Graphs 152 // 6.5 Statistical Analysis of Geographical Line Data 161 // 6.6 Conclusion 163 // Chapter Review 164 // References 165 // 7 Area Objects and Spatial Autocorrelation 167 // Chapter
Objectives 167 // 7.1 Introduction 168 // 7.2 Types of Area Object 169 // 7.3 Geometric Properties of Areas 173 // 7.4 Spatial Autocorrelation: Introducing the Joins Count // Approach 180 // 7.5 Fully Worked Example: The 2000 U.S. Presidential // Election 192 // 7.6 Other Measures of Spatial Autocorrelation 196 // Contents // vii // 7.7 Local Indicators of Spatial Association 203 // Chapter Review 205 // References 206 // 8 Describing and Analyzing Fields 209 // Chapter Objectives 209 // 8.1 Introduction 210 // 8.2 Modeling and Storing Field Data 213 // 8.3 Spatial Interpolation 220 // 8.4 Derived Measures on Surfaces 234 // 8.5 Conclusion 242 // Chapter Review 243 // References 244 // 9 Knowing the Unknowable: The Statistics of Fields 246 // Chapter Objectives 246 // 9.1 Introduction 247 // 9.2 Review of Regression 248 // 9.3 Regression on Spatial Coordinates: Trend Surface // Analysis 256 // 9.4 Statistical Approach to Interpolation: Kriging 265 // 9.5 Conclusion 281 // Chapter Review 282 // References 283 // 10 Putting Maps Together: Map Overlay 284 // Chapter Objectives 284 // 10.1 Introduction 285 // 10.2 Polygon Overlay and Sieve Mapping 287 // 10.3 Problems in Simple Boolean Polygon Overlay 302 // 10.4 Toward a General Model: Alternatives to Boolean // Overlay 304 // 10.5 Conclusion 311 // Chapter Review 312 // References 312 // 11 Multivariate Data, Multidimensional Space, and // Spatialization . 315 // Chapter Objectives 315 // 11.1 Introduction 316 // 11.2 Multivariate
Data and Multidimensional Space 317 // 11.3 Distance, Difference, and Similarity 323 // vili // Contents // 11.4 Cluster Analysis: Identifying Groups of Similar // Observations 327 // 11.5 Spatialization: Mapping Multivariate Data 336 // 11.6 Reducing the Number of Variables: Principal Components // Analysis 343 // 11.7 Conclusion 352 // Chapter Review 353 // References 355 // 12 New Approaches to Spatial Analysis 356 // Chapter Objectives 356 // 12.1 Introduction 357 // 12.2 Geocomputation 361 // 12.3 Spatial Models 370 // 12.4 Conclusion 378 // Chapter Review 379 // References 380 // A The Elements of Statistics 384 // A.l Introduction 384 // A.2 Describing Data 388 // A.3 Probability Theory 392 // A.4 Processes and Random Variables 396 // A.5 Sampling Distributions and Hypothesis Testing 402 // A. 6 Example 406 // Reference 411 // ? Matrices and Matrix Mathematics 412 // B. l Introduction 412 // B.2 Matrix Basics and Notation 412 // B.3 Simple Mathematics 415 // B.4 Solving Simultaneous Equations Using Matrices 420 // B.5 Matrices, Vectors, and Geometry 425 // Reference 430 // 431 // Index

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