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E-grāmata: Handbook of Data Visualization

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Antony Unwin, Chun-houh Chen, Wolfgang K. Härdle 1. 1 Computational Statistics and Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Data Visualization and Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Presentation and Exploratory Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Graphics and Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1. 2 The Chapters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Summary and Overview; Part II. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Summary and Overview; Part III. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Summary and Overview; Part IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 The Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1. 3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Antony Unwin, Chun-houh Chen, Wolfgang K. Härdle Computational Statistics 1. 1 and DataVisualization Tis book is the third volume of the Handbook of Computational Statistics and c- ers the ?eld of data visualization. In line with the companion volumes, it contains a collection of chapters by experts in the ?eld to present readers with an up-to-date and comprehensive overview of the state of the art. Data visualization is an active area of application and research, and this is a good time to gather together a summary of current knowledge. Graphic displays are ofen very e ective at communicating information. Tey are also very ofen not e ective at communicating information. Two important reasons for this state of a airs are that graphics can be produced with a few clicks of the mouse without any thought and the design of graphics is not taken seriously in many scienti c textbooks.

Recenzijas

From the reviews:









"This handbook shows hundreds of ways to visualize data by using modern, high-quality statistical graphics. It is most enjoyable to see such a large number of specialists sharing their insights of these methods within one volume. This book really feeds the imagination of the reader. High-dimensionally recommended!" (Kimmo Vehkalahti, International Statistical Review, Vol. 76 (3), 2008)

I. Data Visualization
Introduction
Antony Unwin
Chun-houh Chen
Wolfgang K. Hardle
Computational Statistics and Data Visualization
4(2)
Data Visualization and Theory
4(1)
Presentation and Exploratory Graphics
4(1)
Graphics and Computing
5(1)
The
Chapters
6(6)
Summary and Overview; Part II
7(2)
Summary and Overview; Part III
9(1)
Summary and Overview; Part IV
10(1)
The Authors
11(1)
Outlook
12(4)
II. Principles
A Brief History of Data Visualization
Michael Friendly
Introduction
16(1)
Milestones Tour
17(25)
Pre-17th Century: Early Maps and Diagrams
17(2)
1600--1699: Measurement and Theory
19(3)
1700--1799: New Graphic Forms
22(3)
1800--1850: Beginnings of Modern Graphics
25(3)
1850--1900: The Golden Age of Statistical Graphics
28(9)
1900--1950: The Modern Dark Ages
37(2)
1950--1975: Rebirth of Data Visualization
39(1)
1975--present: High-D, Interactive and Dynamic Data Visualization
40(2)
Statistical Historiography
42(6)
History as `Data'
42(1)
Analysing Milestones Data
43(2)
What Was He Thinking? -- Understanding Through Reproduction
45(3)
Final Thoughts
48(10)
Good Graphics?
Antony Unwin
Introduction
58(2)
Content, Context and Construction
58(1)
Presentation Graphics and Exploratory Graphics
59(1)
Background
60(2)
History
60(1)
Literature
61(1)
The Media and Graphics
62(1)
Presentation (What to Whom, How and Why)
62(1)
Scientific Design Choices in Data Visualization
63(7)
Choice of Graphical Form
64(1)
Graphical Display Options
64(6)
Higher-dimensional Displays and Special Structures
70(6)
Scatterplot Matrices (Sploms)
70(1)
Parallel Coordinates
70(1)
Mosaic Plots
71(1)
Small Multiples and Trellis Displays
72(2)
Time Series and Maps
74(2)
Practical Advice
76(1)
Software
76(1)
Bad Practice and Good Practice (Principles)
77(1)
And Finally
77(4)
Static Graphics
Paul Murrell
Complete Plots
81(3)
Sensible Defaults
82(2)
User Interface
84(1)
Customization
84(8)
Setting Parameters
84(3)
Arranging Plots
87(1)
Annotation
88(4)
The User Interface
92(1)
Extensibility
92(6)
Building Blocks
93(4)
Combining Graphical Elements
97(1)
The User Interface
98(1)
Other Issues
98(2)
3-D Plots
98(1)
Speed
98(1)
Output Formats
99(1)
Data Handling
99(1)
Summary
100(4)
Data Visualization Through Their Graph Representations
George Michailidis
Introduction
104(1)
Data and Graphs
104(2)
Graph Layout Techniques
106(12)
Force-directed Techniques
109(1)
Multidimensional Scaling
110(3)
The Pulling Under Constraints Model
113(1)
Bipartite Graphs
114(4)
Discussion and Concluding Remarks
118(4)
Graph-theoretic Graphics
Leland Wilkinson
Introduction
122(1)
Definitions
122(2)
Trees
123(1)
Graph Drawing
124(12)
Hierarchical Trees
125(6)
Spanning Trees
131(3)
Networks
134(1)
Directed Graphs
134(1)
Treemaps
135(1)
Geometric Graphs
136(7)
Disk Exclusion
137(4)
Disk Inclusion
141(2)
Graph-theoretic Analytics
143(9)
Scagnostics
143(1)
Sequence Analysis
144(3)
Graph Matching
147(1)
Conclusion
148(4)
High-dimensional Data Visualization
Martin Theus
Introduction
152(1)
Mosaic Plots
153(3)
Associations in High-dimensional Data
153(2)
Response Models
155(1)
Models
156(1)
Trellis Displays
156(8)
Definition
157(1)
Trellis Display vs. Mosaic Plots
158(3)
Trellis Displays and Interactivity
161(1)
Visualization of Models
162(2)
Parallel Coordinate Plots
164(8)
Geometrical Aspects vs. Data Analysis Aspects
164(2)
Limits
166(3)
Sorting and Scaling Issues
169(2)
Wrap-up
171(1)
Projection Pursuit and the Grand Tour
172(3)
Grand Tour vs. Parallel Coordinate Plots
174(1)
Recommendations
175(5)
Multivariate Data Glyphs: Principles and Practice
Matthew O. Ward
Introduction
180(1)
Data
180(1)
Mappings
181(1)
Examples of Existing Glyphs
182(1)
Biases in Glyph Mappings
183(1)
Ordering of Data Dimensions/Variables
184(4)
Correlation-driven
185(1)
Symmetry-driven
185(1)
Data-driven
185(1)
User-driven
186(2)
Glyph Layout Options
188(3)
Data-driven Placement
188(1)
Structure-driven Placement
189(2)
Evaluation
191(4)
Summary
195(5)
Linked Views for Visual Exploration
Adalbert Wilhelm
Visual Exploration by Linked Views
200(2)
Theoretical Structures for Linked Views
202(7)
Linking Sample Populations
204(1)
Linking Models
205(3)
Linking Types
208(1)
Linking Frames
209(1)
Visualization Techniques for Linked Views
209(4)
Replacement
209(1)
Overlaying
210(1)
Repetition
211(1)
Special Forms of Linked Highlighting
212(1)
Software
213(1)
Conclusion
214(4)
Linked Data Views
Graham Wills
Motivation: Why Use Linked Views?
218(3)
The Linked Views Paradigm
221(3)
Brushing Scatterplot Matrices and Other Nonaggregated Views
224(3)
Generalizing to Aggregated Views
227(4)
Distance-based Linking
231(1)
Linking from Multiple Views
232(3)
Linking to Domain-specific Views
235(3)
Summary
238(1)
Data Used in This
Chapter
239(5)
Visualizing Trees and Forests
Simon Urbanek
Introduction
244(1)
Individual Trees
244(12)
Hierarchical Views
245(4)
Recursive Views
249(5)
Fitting Tree Models
254(2)
Visualizing Forests
256(6)
Split Variables
257(2)
Data View
259(1)
Trace Plot
260(2)
Conclusion
262(6)
III. Methodologies
Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data
Jurgen Symanzik
Daniel B. Carr
Introduction
268(4)
A Motivational Example
272(2)
Design Issues and Variations on Static Micromaps
274(2)
Web-based Applications of LM Plots
276(7)
Micromaps on the EPA CEP Web Site
278(1)
Micromaps on the USDA--NASS Web Site
278(1)
Micromaps on the NCI Web Site
279(2)
Micromaps at Utah State University
281(2)
Constructing LM Plots
283(5)
Micromaps via S-Plus
283(3)
Micromaps via nViZn
286(1)
Micromaps via Java and Other Statistical Packages
287(1)
Discussion
288(8)
Grand Tours, Projection Pursuit Guided Tours, and Manual Controls
Dianne Cook
Andreas Buja
Eun-Kyung Lee
Hadley Wickham
Introductory Notes
296(5)
Some Basics on Projections
297(2)
What Structure Is Interesting?
299(2)
Tours
301(9)
Terminology: Plane, Basis, Frame, Projection
302(1)
Interpolating Between Projections: Making a Movie
302(1)
Choosing the Target Plane
303(7)
A Note on Transformations
310(1)
A Note on Scaling
310(1)
Using Tours with Numerical Methods
310(2)
End Notes
312(4)
Multidimensional Scaling
Michael A.A. Cox
Trevor F. Cox
Proximity Data
316(3)
Metric MDS
319(3)
Non-metric MDS
322(3)
Example: Shakespeare Keywords
325(5)
Procrustes Analysis
330(1)
Unidimensional Scaling
331(2)
INDSCAL
333(5)
Correspondence Analysis and Reciprocal Averaging
338(3)
Large Data Sets and Other Numerical Approaches
341(10)
Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age
Francesco Palumbo
Domenico Vistocco
Alain Morineau
Introduction
351(1)
The Geometric Approach to the Statistical Analysis
352(3)
Distance and Metric Space
353(1)
OECD Countries Dataset
354(1)
Factorial Analysis
355(5)
Principal Component Analysis
356(4)
Distance Visualization in Rp
360(5)
Hierarchical Clustering
362(3)
Principal Axis Methods and Classification: a Unified View
365(1)
Computational Issues
365(25)
Partitioning Methods
366(2)
Mixed Strategy for Very Large Datasets
368(22)
Multivariate Visualization by Density Estimation
Michael C. Minnotte
Stephan R. Sain
David W. Scott
Univariate Density Estimates
390(11)
Histograms
390(3)
Improved Binned Density Estimates
393(1)
Kernel Density Estimates
394(3)
Kernel Variants
397(3)
Multiscale Visualization of Density Estimates
400(1)
Bivariate Density Estimates
401(5)
Bivariate Histograms
402(2)
Bivariate Kernel Density Estimators
404(2)
Higher-dimensional Density Estimates
406(11)
Structured Sets of Graphs
Richard M. Heiberger
Burt Holland
Introduction
417(1)
Cartesian Products and the Trellis Paradigm
417(2)
Trellis Paradigm
418(1)
Implementation of Trellis Graphics
418(1)
Scatterplot Matrices: splom and xysplom
419(10)
Example -- Life Expectancy
419(1)
Display of Scatterplot Matrix
420(2)
Example -- A Scatterplot Matrix with Conditioning
422(1)
Coordinating Sets of Related Graphs
422(3)
Summary Plot with Legend
425(1)
Example -- an xysplom with Labeled Correlation Coefficients
426(1)
Ladder of Powers Plot -- Wool Data
427(2)
Regression Diagnostic Plots
429(2)
Case Statistics
429(1)
Example -- Kidney Data
429(2)
Analysis of Covariance Plots
431(3)
Example -- Hot Dog Data
432(1)
Cartesian Product of Model Parameters
433(1)
Interaction Plots
434(5)
Two-factor Rhizobium Example
434(1)
Extended Two-way Interaction Plot
434(1)
Three-factor Vulcanized Rubber Example
435(2)
Design Issues for the Two-way Interaction Plot
437(1)
Two-way Interaction Plots with Simple Effects
437(2)
Boxplots
439(3)
Assessing Three-way Interaction
439(1)
Sequences of Boxplots
440(1)
Microplots
441(1)
Example -- Catalyst Data
441(1)
Example -- Muscle Data, continued
442(1)
Graphical Display of Incidence and Relative Risk
442(2)
Summary
444(1)
File Name Conventions
444(4)
Regression by Parts: Fitting Visually Interpretable Models with Guide
Wei-Yin Loh
Introduction
448(1)
Boston Housing Data -- Effects of Collinearity
449(4)
Extension to Guide
453(2)
Mussels -- Categorical Predictors and SIR
455(4)
Crash Tests -- Outlier Detection Under Confounding
459(6)
Car Insurance Rates -- Poisson Regression
465(3)
Conclusion
468(4)
Structural Adaptive Smoothing by Propagation--Separation Methods
Jorg Polzehl
Vladimir Spokoiny
Nonparametric Regression
472(3)
Examples
472(1)
Local Modeling
473(2)
Structural Adaptation
475(3)
Adaptive Weights Smoothing
476(1)
Choice of Parameters: Propagation Condition
477(1)
An Illustrative Univariate Example
478(2)
Examples and Applications
480(9)
Application 1: Adaptive Edge-Preserving Smoothing in 3-D
480(1)
Examples: Binary and Poisson Data
481(2)
Example: Denoising of Digital Color Images
483(2)
Example: Local Polynomial Propagation--Separation (PS) Approach
485(4)
Concluding Remarks
489(5)
Smoothing Techniques for Visualisation
Adrian W. Bowman
Introduction
494(2)
Smoothing in One Dimension
496(6)
Smoothing in Two Dimensions
502(5)
Additive Models
507(4)
Discussion
511(29)
Data Visualization via Kernel Machines
Yuan-chin Ivan Chang
Yuh-Jye Lee
Hsing-Kuo Pao
Mei-Hsien Lee
Su-Yun Huang
Introduction
540(1)
Kernel Machines in the Framework of an RKHS
541(2)
Kernel Principal Component Analysis
543(8)
Computation of KPCA
544(7)
Kernel Canonical Correlation Analysis
551(3)
Kernel Cluster Analysis
554(8)
Visualizing Cluster Analysis and Finite Mixture Models
Friedrich Leisch
Introduction
562(2)
The Data Sets
562(2)
Software
564(1)
Hierarchical Cluster Analysis
564(3)
Dendrograms
565(2)
Heatmaps
567(1)
Partitioning Cluster Analysis
567(13)
Convex Cluster Hulls
569(1)
The Voronoi Partition
570(1)
Neighborhood Graphs
571(1)
Cluster Silhouettes
572(2)
Cluster Location and Dispersion
574(2)
Using Background Variables
576(1)
Self-Organizing Maps
577(3)
Model-Based Clustering
580(6)
Summary
586(4)
Visualizing Contingency Tables
David Meyer
Achim Zeileis
Kurt Hornik
Introduction
590(1)
Two-Way Tables
591(7)
Mosaic Displays
592(3)
Sieve Plots
595(1)
Association Plots
596(2)
Summary
598(1)
Using Colors for Residual-Based Shadings
598(8)
A Note on Colors and Color Palettes
598(3)
Highlighting and Color-Based Shadings
601(2)
Visualizing Test Statistics
603(2)
Summary
605(1)
Selected Methods for Multiway Tables
606(8)
Exploratory Visualization Techniques
607(1)
Model-Based Displays for Conditional Independence Models
608(3)
A Four-Way Example
611(3)
Summary
614(1)
Conclusion
614(5)
Mosaic Plots and Their Variants
Heike Hofmann
Definition and Construction
619(3)
Interpreting Mosaic Plots
622(5)
Probabilities in Mosaic Plots
622(2)
Visualizing Interaction Effects
624(3)
Variants
627(8)
Doubledecker Plots
627(1)
Fluctuation Diagrams
628(4)
Others
632(3)
Related Work and Generalization
635(5)
Treemaps
635(1)
Trellis Plots
636(2)
Pivot Tables
638(2)
Implementations
640(4)
Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data
Alfred Inselberg
Introduction
644(4)
Origins
644(2)
The Case for Visualization
646(2)
Exploratory Data Analysis with || -coords
648(16)
Multidimensional Detective
648(1)
An Easy Case Study: GIS Data
649(6)
Compound Queries: Financial Data
655(8)
Hundreds of Variables
663(1)
Classification
664(4)
Visual and Computational Models
668(3)
Parallel Coordinates: Quick Overview
671(5)
Lines
671(1)
Planes and Hyperplanes
672(2)
Nonlinear Multivariate Relations: Hypersurfaces
674(2)
Future
676(6)
Matrix Visualization
Han-Ming Wu
ShengLi Tzeng
Chun-Houh Chen
Introduction
682(1)
Related Works
682(1)
The Basic Principles of Matrix Visualization
683(7)
Presentation of the Raw Data Matrix
684(2)
Seriation of Proximity Matrices and the Raw Data Matrix
686(4)
Generalization and Flexibility
690(3)
Summarizing Matrix Visualization
690(1)
Sediment Display
691(1)
Sectional Display
692(1)
Restricted Display
692(1)
An Example
693(4)
Comparison with Other Graphical Techniques
697(3)
Matrix Visualization of Binary Data
700(4)
Similarity Measure for Binary Data
700(2)
Matrix Visualization of the KEGG Metabolism Pathway Data
702(2)
Other Modules and Extensions of MV
704(1)
MV for Nominal Data
704(1)
MV for Covariate Adjustment
704(1)
Data with Missing Values
705(1)
Modeling Proximity Matrices
705(1)
Conclusion
705(5)
Visualization in Bayesian Data Analysis
Jouni Kerman
Andrew Gelman
Tian Zheng
Yuejing Ding
Introduction
710(2)
The Role of EDA in Model Comprehension and Model-Checking
710(1)
Comparable Non-Bayesian Approaches
711(1)
Using Visualization to Understand and Check Models
712(4)
Using Statistical Graphics in Model-Based Data Analysis
712(1)
Bayesian Exploratory Data Analysis
712(3)
Hierarchical Models and Parameter Naming Conventions
715(1)
Model-Checking
715(1)
Example: A Hierarchical Model of Structure in Social Networks
716(5)
Posterior Predictive Checks
720(1)
Challenges Associated with the Graphical Display of Bayesian Inferences
721(1)
Integrating Graphics and Bayesian Modeling
722(1)
Summary
722(4)
Programming Statistical Data Visualization in the Java Language
Junji Nakano
Yoshikazu Yamamoto
Keisuke Honda
Introduction
726(1)
Basics of Statistical Graphics Libraries and Java Programming
727(8)
Required Functions for Statistical Graphics Libraries
727(2)
Advantages of Java for Programming Statistical Graphics
729(1)
Basics of Java Graphics
730(1)
GoF Design Patterns
731(2)
MVC Design Pattern
733(1)
Class Diagrams in UML
733(2)
Design and Implementation of a Java Graphics Library
735(18)
Overview of Jasplot
735(2)
Summary of Basic Interfaces and Classes with an Example
737(3)
Classes for Original Data
740(2)
Classes for Data about Basic Graphics
742(1)
Classes for Drawing Basic Graphics
743(1)
Classes of Panels for Drawing
744(1)
Classes for Interactive Operations
745(6)
Classes for Tables of Data
751(1)
A Class for Building Complicated Graphics
751(2)
Concluding Remarks
753(5)
Web-Based Statistical Graphics using XML Technologies
Yoshiro Yamamoto
Masaya lizuka
Tomokazu Fujino
Introduction
758(1)
The Web, Statistics and Statistical Graphics
758(1)
XML and Statistical Graphics
759(1)
XML-Based Vector Graphics Formats
759(6)
What is XML?
759(6)
SVG
765(6)
Overview of SVG
765(1)
Basic Structure
765(3)
Implementation of Interactive Functionality via JavaScript
768(3)
X3D
771(6)
Overview of X3D
771(3)
Basic Structure
774(3)
X3D Scatter Plot Function of R
777(1)
Applications
777(17)
SVG Application as Teachware
777(1)
Application to Three-Dimensional Representations
778(1)
GIS Applications
779(5)
Authoring Tool for SVG Statistical Graphics in R
784(10)
IV. Selected Applications
Visualization for Genetic Network Reconstruction
Grace S. Shieh
Chin-Yuan Guo
Introduction
794(1)
Visualization for Data Preprocessing
794(2)
Outlier Detection
794(1)
Data Augmentation
795(1)
Visualization for Genetic Network Reconstruction
796(18)
Clustering and Graphical Models
797(2)
A Time-lagged Correlation Approach
799(2)
A Smooth Response Surface Approach
801(1)
A Regression Approach
802(4)
A Pattern Recognition Approach
806(8)
Reconstruction, Visualization and Analysis of Medical Images
Henry Horng-Shing Lu
Introduction
814(1)
PET Images
815(4)
Ultrasound Images
819(3)
Magnetic Resonance Images
822(4)
Conclusion and Discussion
826(6)
Exploratory Graphics of a Financial Dataset
Antony Unwin
Martin Theus
Wolfgang K. Hardle
Introduction
832(1)
Description of the Data
833(1)
First Graphics
834(3)
Outliers
837(4)
Scatterplots
841(2)
Mosaic Plots
843(1)
Initial Comparisons Between Bankrupt Companies
844(4)
Investigating Bigger Companies
848(3)
Summary
851(1)
Software
852(2)
Graphical Data Representation in Bankruptcy Analysis
Wolfgang K. Hardle
Rouslan A. Moro
Dorothea Schafer
Company Rating Methodology
854(3)
The SVM Approach
857(2)
Company Score Evaluation
859(1)
Variable Selection
860(5)
Conversion of Scores into PDs
865(2)
Colour Coding
867(4)
Conclusion
871(3)
Visualizing Functional Data with an Application to eBay's Online Auctions
Wolfgang Jank
Galit Shmueli
Catherine Plaisant
Ben Shneiderman
Introduction
874(2)
Online Auction Data from eBay
876(1)
Visualization at the Object Recovery Stage
877(5)
Visualizing Functional Observations
882(8)
Visualizing Individual Objects and Their Dynamics
882(4)
Visualizing Relationships Among Functional Data
886(1)
Visualizing Functional and Cross-sectional Information
887(3)
Interactive Information Visualization of Functional and Cross-sectional Information via TimeSearcher
890(5)
Capabilities of TimeSearcher
891(3)
Forecasting with TimeSearcher
894(1)
Further Challenges and Future Directions
895(5)
Concurrency of Functional Events
897(1)
Dimensionality of Functional Data
897(1)
Complex Functional Relationships
897(3)
Visualization Tools for Insurance Risk Processes
Krzysztof Burnecki
Rafal Weron
Introduction
900(2)
Software
902(1)
Fitting Loss and Waiting Time Distributions
902(10)
Mean Excess Function
902(5)
Limited Expected Value Function
907(1)
Probability Plot
908(4)
Risk Process and its Visualization
912(9)
Ruin Probability Plots
912(3)
Density Evolution
915(1)
Quantile Lines
916(3)
Probability Gates
919(2)
Subject Index 921
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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