Visualization analysis & design:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2015]
|
| Schriftenreihe: | A.K. Peters visualization series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxiii, 404 Seiten Illustrationen |
| ISBN: | 9781466508910 |
Internformat
MARC
| LEADER | 00000nam a22000002c 4500 | ||
|---|---|---|---|
| 001 | BV042191434 | ||
| 003 | DE-604 | ||
| 005 | 20240116 | ||
| 007 | t | ||
| 008 | 141117s2015 a||| |||| 00||| eng d | ||
| 016 | 7 | |a 787119725 |2 DE-101 | |
| 020 | |a 9781466508910 |c alk. paper |9 978-1-4665-0891-0 | ||
| 035 | |a (OCoLC)897392709 | ||
| 035 | |a (DE-599)GBV787119725 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-525 |a DE-739 |a DE-188 |a DE-859 |a DE-B768 |a DE-M382 |a DE-11 |a DE-384 |a DE-1043 |a DE-523 |a DE-861 |a DE-858 | ||
| 082 | 0 | |a 001.4226 | |
| 084 | |a ST 320 |0 (DE-625)143657: |2 rvk | ||
| 100 | 1 | |a Munzner, Tamara |e Verfasser |0 (DE-588)1077383320 |4 aut | |
| 245 | 1 | 0 | |a Visualization analysis & design |c Tamara Munzner, Department of Computer Science, University of British Columbia |
| 246 | 1 | 3 | |a Visualization analysis and design |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c [2015] | |
| 300 | |a xxiii, 404 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a A.K. Peters visualization series | |
| 650 | 0 | 7 | |a Visualisierung |0 (DE-588)4188417-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computergrafik |0 (DE-588)4010450-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Computergrafik |0 (DE-588)4010450-3 |D s |
| 689 | 0 | 1 | |a Visualisierung |0 (DE-588)4188417-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027630507&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027630507 | ||
Datensatz im Suchindex
| _version_ | 1804152702979014656 |
|---|---|
| adam_text | Why a New Book?................................................................... xv
Existing Books................................................................... xvi
Audience........................................................................ xvii
Who’s Who................................................................... xviii
Structure: What’s in This Book............................................. xviii
What’s Not in This Book........................................................... xx
Acknowledgments................................................................... xx
Vl:: vjVVny UO !iV
1.1 The Big Picture........................................................... 1
1.2 Why Have a Human in the Loop? ............................................... 2
1.3 Why Have a Computer in the Loop?............................................ 4
1.4 Why Use an External Representation?......................................... 6
1.5 Why Depend on Vision?........................................................ 6
1.6 Why Show the Data in Detail?................................................. 7
1.7 Why Use Interactivity?...................................................... 9
1.8 Why Is the Vis Idiom Design Space Huge?................................... 10
1.9 Why Focus on Tasks?......................................................... 11
1.10 Why Focus on Effectiveness?................................................. 11
1.11 Why Are Most Designs Ineffective?.......................................... 12
1.12 Why Is Validation Difficult?................................................ 14
1.13 Why Are There Resource Limitations?......................................... 14
1.14 Why Analyze?................................................................ 16
1.15 Further Reading......................................................... 18
vViu···; i lain AN Viaion
2.1 The Big Picture.......................................................... . 21
2.2 Why Do Data Semantics and Types Matter?..................................... 21
2.3 Data Types............................................................... 23
2.4 Dataset Types............................................................. 24
2.4.1 Tables................................................................ 25
2.4.2 Networks and Trees.................................................... 26
2.4.2.1 Trees....................................................... 27
(iontoi vU։)
2.4.3 Fields................................................................ 27
2.4.3.1 Spatial Fields............................................... 28
2.4.3.2 Grid Types .................................................. 29
2.4.4 Geometry.............................................................. 29
2.4.5 Other Combinations.................................................... 30
2.4.6 Dataset Availability.................................................. 31
2.5 Attribute Types.............................................................. 31
2.5.1 Categorical........................................................... 32
2.5.2 Ordered: Ordinal and Quantitative.................................... 32
2.5.2.1 Sequential versus Diverging.................................. 33
2.5.2.2 Cyclic....................................................... 33
2.5.3 Hierarchical Attributes............................................... 33
2.6 Semantics.................................................................... 34
2.6.1 Key versus Value Semantics............................................ 34
2.6.1.1 Flat Tables.................................................. 34
2.6.1.2 Multidimensional Tables ..................................... 36
2.6.1.3 Fields .................................................... 37
2.6.1.4 Scalar Fields .............................................. 37
2.6.1.5 Vector Fields................................................ 37
2.6.1.6 Tensor Fields................................................ 38
2.6.1.7 Field Semantics ............................................. 38
2.6.2 Temporal Semantics................................................... 38
2.6.2.1 Time-Varying Data............................................ 39
2.7 Further Reading.............................................................. 40
3.1 The Big Picture..............
3.2 Why Analyze Tasks Abstractly?
3.3 Who: Designer or User ....
3.4 Actions......................
3.4.1 Analyze 3.4.1.1 Discover
3.4.1.2 Present
3.4.2 3.4.1.3 Produce Enjoy .
3.4.2.1 Annotate
3.4.2.2 Record .
3.4.2.3 Derive .
3.4.3 Search . 3.4.3.1 3.4.3.2 Lookup Locate .
3.4.3.3 Browse .
3.4.3.4 Explore
43
43
44
45
45
47
47
48
49
49
49
50
53
53
53
53
54
3.4.4 Query................................................................. 54
3.4.4.1 Identify ..................................................... 54
3.4.4.2 Compare....................................................... 55
3.4.4.3 Summarize..................................................... 55
3.5 Targets...................................................................... 55
3.6 How: A Preview.............................................................. 57
3.7 Analyzing and Deriving: Examples............................................. 59
3.7.1 Comparing Т ю Idioms................................................. 59
3.7.2 Deriving One Attribute................................................ 60
3.7.3 Deriving Many New Attributes.......................................... 62
3.8 Further Reading.......................................................... 64
4.1 The Big Picture.............................................................. 67
4.2 Why Validate?.............................................................. 67
4.3 Four Levels of Design . .................................................. 67
4.3.1 Domain Situation...................................................... 69
4.3.2 Task and Data Abstraction............................................. 70
4.3.3 Visual Encoding and Interaction Idiom................................. 71
4.3.4 Algorithm............................................................. 72
4.4 Angles of Attack ........................................................ 73
4.5 Threats to Validity........................................................ 74
4.6 Validation Approaches........................................................ 75
4.6.1 Domain Validation..................................................... 77
4.6.2 Abstraction Validation ............................................... 78
4.6.3 Idiom Validation...................................................... 78
4.6.4 Algorithm Validation.................................................. 80
4.6.5 Mismatches.......................................................... 81
4.7 Validation Examples.......................................................... 81
4.7.1 Genealogical Graphs .................................................. 81
4.7.2 MatrixExplorer........................................................ 83
4.7.3 Flow Maps............................................................ 85
4.7.4 LiveRAC............................................................... 87
4.7.5 LinLog.............................................................. 89
4.7.6 Sizing the Horizon.................................................... 90
4.8 Further Reading............................................................ 91
5.1 The Big Picture . ........................................................... 95
5.2 Why Marks and Channels?................................................... 95
5.3 Defining Marks and Channels.................................................. 95
5.3.1 Channel Types......................................................... 99
5.3.2 Mark Tfypes........................................................... 99
5.4 Using Marks and Channels.................................................. 99
5.4.1 Expressiveness and Effectiveness................................... 100
5.4.2 Channel Rankings................................................... 101
5.5 Channel Effectiveness.................................................... 103
5.5.1 Accuracy........................................................... 103
5.5.2 Discriminability................................................... 106
5.5.3 Separability....................................................... 106
5.5.4 Popout............................................................. 109
5.5.5 Grouping........................................................... Ill
5.6 Relative versus Absolute Judgements..................................... 112
5.7 Further Reading.......................................................... 114
6.1 The Big Picture...........................
6.2 Why and When to Follow Rules of Thumb?
6.3 No Unjustified 3D ........................
6.3.1 The Power of the Plane............
6.3.2 The Disparity of Depth............
6.3.3 Occlusion Hides Information ....
6.3.4 Perspective Distortion Dangers . .
6.3.5 Other Depth Cues..................
6.3.6 Tilted Text Isn’t Legibile........
6.3.7 Benefits of 3D: Shape Perception .
6.3.8 Justification and Alternatives . . .
6.3.9 Empirical Evidence........................
6.4 No Unjustified 2D ...............................
6.5 Eyes Beat Memory.................................
6.5.1 Memory and Attention .....................
6.5.2 Animation versus Side-by-Side Views.......
6.5.3 Change Blindness..........................
6.6 Resolution over Immersion........................
6.7 Overview First, Zoom and Filter, Details on Demand
6.8 Responsiveness Is Required . .....................
6.8.1 Visual Feedback...........................
6.8.2 Latency and Interaction Design............
6.8.3 Interactivity Costs.......................
6.9 Get It Right in Black and White..................
6.10 Function First, Form Next .......................
6.11 Further Reading..................................
117
117
117
118
118
120
121
123
124
124
125
125
128
129
131
131
132
132
133
134
135
137
138
138
140
140
140
141
7.5
7.8
7.1 The Big Picture......................
7.2 Why Arrange?.........................
7.3 Arrange by Keys and Values ....
7.4 Express: Quantitative Values ....
Example; GCrU.i.ej. pio ic . . .
Separate, Order, and Align: Categorical Regio
7.5.1 List Alignment: One Key ....
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7.5.2 Matrix Alignment: Two Keys
7.5.3 Volumetric Grid: Three Keys . .
7.5.4 Recursive Subdivision: Multiple
7.6 Spatial Axis Orientation.................
7.6.1 Rectilinear Layouts............
7.6.2 Parallel Layouts...............
Keys
7.6.3 Radial Layouts..................
.Hxarnpie: Kaciiäi. .Ha.a Ho ait a . .
Example: :ie Cb.arto .........
7.7 Spatial Layout Density .................
7.7.1 Dense............................
Example: Dense Eoilwarc (ever՝՝/
7.7.2 Space-Filling..................
Further Reading......................
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8.1 The Big Picture....................
8.2 Why Use Given?.....................
8.3 Geometiy.......................... .
8.3.1 Geographic Data.............
8.3.2 Other Derived Geometry . . .
8.4 Scalar Fields: One Value
8.4.1 Isocontours ...........
8.4.2 Direct Volume Rendering . . .
8.5 Vector Fields: Multiple Values............................................ 189
8.5.1 Flow Glyphs ....................................................... 191
8.5.2 Geometric Flow .................................................... 191
............................... 192
8.5.3 Texture Flow...................................................... 193
8.5.4 Feature Flow....................................................... 193
8.6 Tensor Fields: Many Values................................................ 194
.................................. 194
8.7 Further Reading........................................................... 197
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9.1 The Big Picture........................................................... 201
9.2 Connection: Link Marks.................................................... 201
. 204
...................................................... 207
9.3 Matrix Views ............................................................. 208
................................ 208
9.4 Costs and Benefits: Connection versus Matrix.............................. 209
9.5 Containment: Hierarchy Marks ............................................. 213
.............................................. . 213
............................................. 215
9.6 Further Reading........................................................... 216
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10.1 The Big Picture........................................................... 219
10.2 Color Theory ............................................................. 219
10.2.1 Color Vision ...................................................... 219
10.2.2 ColorSpaces........................................................ 220
10.2.3 Luminance, Saturation, and Hue..................................... 223
10.2.4 Transparency..................................................... 225
10.3 Colormaps................................................................. 225
10.3.1 Categorical Colormaps ............................................. 226
10.3.2 Ordered Colormaps................................................ 229
10.3.3 Bivariate Colormaps................................................ 234
10.3.4 Colorblind-Safe Colormap Design.................................... 235
10.4 Other Channels............................................................ 236
10.4.1 Size Channels...................................................... 236
10.4.2 Angle Channel...................................................... 237
10.4.3 Curvature Channel.................................................. 238
10.4.4 Shape Channel ................................................... 238
10.4.5 Motion Channels.................................................... 238
10.4.6 Texture and Stippling.............................................. 239
10.5 Further Reading........................................................... 240
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11.1 The Big Picture.......
11.2 Why Change?...........
11.3 Change View over Time
11.4 Select Elements...............
11.4.1 Selection Design Choices
11.4.2 Highlighting ..........
11.4.3 Selection Outcomes . . .
11.5 Navigate: Changing Viewpoint .
11.5.1 Geometric Zooming . . .
11.5.2 Semantic Zooming . . . .
11.5.3 Constrained Navigation .
11.6 Navigate: Reducing Attributes .
11.6.1 Slice..................
243
244
244
246
248
249
250
251
253
254
254
255
255
256
258
258
11.6.2 Cut............................................................................ 260
11.6.3 Project........................................................................ 261
11.7 Further Reading......................................................................... 261
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12.1 The Big Picture......................................................................... 265
12.2 Why Facet?.............................................................................. 265
12.3 Juxtapose and Coordinate Views ......................................................... 267
12.3.1 Share Encoding: Same/Different................................................. 267
268
269
270
271
274
12.3.3 Share Navigation: Synchronize .................................... 276
12.3.4 Combinations ................................................. 276
............................................... 277
12.3.5 Juxtapose Views................................................... 278
12.4 Partition into Views .............................................. 279
12.4.1 Regions, Glyphs, and Views........................................ 279
12.4.2 List Alignments ............................................... 281
12.4.3 Matrix Alignments................................................. 282
................................................ 282
12.4.4 Recursive Subdivision............................................. 285
12.5 Superimpose Layers....................................................... 288
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12.3.2 Share Data: All, Subset, None
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12.5.1 Visually Distinguishable Layers
12.5.2 Static Layers...................................
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12.5.3 Dynamic Layers
12.6 Further Reading . . . .
It] : induor; item;, and AUrilxilcr.
13.1 The Big Picture..............
13.2 Why Reduce?..................
13.3 Filter.......................
13.3.1 Item Filtering.......
TRaurupie: Film Finder
13.3.2 Attribute Filtering . .
hmampie: DÜFFA . .
13.4 Aggregate......................
13.4.1 Item Aggregation . . .
£xa n pic ; BI otogra me
¡example: Continuous Cco
Example: Boxuiol Charin
Example: GoIarPlol
Example: .1 1.10111IL ( i 111 ( J iiJ ,i {i 1
...... ....„Jei .Coordinates
13.4.2 Spatial Aggregation......................
Е.тпrole: Geographically Weighted Boxoloto
13.4.3 Attribute Aggregation: Dimensionality Reduction.................
13.4.3.1 Why and When to Use DR?................................
Example: Dimensionality Reduction (or Document Col.lect.iono . . .
13.4.3.2 How to Show DR Data?...................................
13.5 Further Reading........................................................
11 ·: imbud: i oouo; Conioxt
14.1 The Big Picture.............................
14.2 Why Embed?........................
14.3 Elide.......................................
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14.4 Superimpose.................................
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14.5 Distort.....................................
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289
289
289
290
292
294
295
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n .DK
299
299
300
301
301
303
304
305
305
306
307
308
310
311
313
313
315
316
316
319
320
323
323
324
325
326
326
327
327
328
329
Example: Stretch and Cquioh Navigation ............................. 331
Example: Nonlinear· Magnification Fields ............................. 333
14.6 Costs and Benefits: Distortion................................................ 334
14.7 Further Reading............................................................... 337
: i A! j; ilynie f o ! nu !ior. 340
15.1 The Big Picture............................................................... 341
15.2 Why Analyze Case Studies?..................................................... 341
15.3 Graph-Theoretic Scagnos tics................................................ 342
15.4 VisDB...................................................................... 347
15.5 Hierarchical Clustering Explorer............................................ 351
15.6 PivotGraph ................................................................... 355
15.7 InterRing .................................................................... 358
15.8 Constellation........................................................... 360
15.9 Further Reading............................................................. 366
! .mdit; ■··.-·
lonoorx index
|
| any_adam_object | 1 |
| author | Munzner, Tamara |
| author_GND | (DE-588)1077383320 |
| author_facet | Munzner, Tamara |
| author_role | aut |
| author_sort | Munzner, Tamara |
| author_variant | t m tm |
| building | Verbundindex |
| bvnumber | BV042191434 |
| classification_rvk | ST 320 |
| ctrlnum | (OCoLC)897392709 (DE-599)GBV787119725 |
| dewey-full | 001.4226 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 001 - Knowledge |
| dewey-raw | 001.4226 |
| dewey-search | 001.4226 |
| dewey-sort | 11.4226 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Allgemeines Informatik |
| format | Book |
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| id | DE-604.BV042191434 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:14:55Z |
| institution | BVB |
| isbn | 9781466508910 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027630507 |
| oclc_num | 897392709 |
| open_access_boolean | |
| owner | DE-525 DE-739 DE-188 DE-859 DE-B768 DE-M382 DE-11 DE-384 DE-1043 DE-523 DE-861 DE-858 |
| owner_facet | DE-525 DE-739 DE-188 DE-859 DE-B768 DE-M382 DE-11 DE-384 DE-1043 DE-523 DE-861 DE-858 |
| physical | xxiii, 404 Seiten Illustrationen |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | A.K. Peters visualization series |
| spelling | Munzner, Tamara Verfasser (DE-588)1077383320 aut Visualization analysis & design Tamara Munzner, Department of Computer Science, University of British Columbia Visualization analysis and design Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2015] xxiii, 404 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier A.K. Peters visualization series Visualisierung (DE-588)4188417-6 gnd rswk-swf Computergrafik (DE-588)4010450-3 gnd rswk-swf Computergrafik (DE-588)4010450-3 s Visualisierung (DE-588)4188417-6 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027630507&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Munzner, Tamara Visualization analysis & design Visualisierung (DE-588)4188417-6 gnd Computergrafik (DE-588)4010450-3 gnd |
| subject_GND | (DE-588)4188417-6 (DE-588)4010450-3 |
| title | Visualization analysis & design |
| title_alt | Visualization analysis and design |
| title_auth | Visualization analysis & design |
| title_exact_search | Visualization analysis & design |
| title_full | Visualization analysis & design Tamara Munzner, Department of Computer Science, University of British Columbia |
| title_fullStr | Visualization analysis & design Tamara Munzner, Department of Computer Science, University of British Columbia |
| title_full_unstemmed | Visualization analysis & design Tamara Munzner, Department of Computer Science, University of British Columbia |
| title_short | Visualization analysis & design |
| title_sort | visualization analysis design |
| topic | Visualisierung (DE-588)4188417-6 gnd Computergrafik (DE-588)4010450-3 gnd |
| topic_facet | Visualisierung Computergrafik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027630507&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT munznertamara visualizationanalysisdesign AT munznertamara visualizationanalysisanddesign |