-
Notifications
You must be signed in to change notification settings - Fork 0
/
VIHI_v1_1.twb
917 lines (916 loc) · 59.2 KB
/
VIHI_v1_1.twb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
<?xml version='1.0' encoding='utf-8' ?>
<!-- build 20183.18.1018.1932 -->
<workbook original-version='18.1' source-build='2018.3.0 (20183.18.1018.1932)' source-platform='win' version='18.1' xmlns:user='http://www.tableausoftware.com/xml/user'>
<document-format-change-manifest>
<WindowsPersistSimpleIdentifiers />
<ZoneBackgroundTransparency />
</document-format-change-manifest>
<preferences>
<preference name='ui.encoding.shelf.height' value='24' />
<preference name='ui.shelf.height' value='26' />
</preferences>
<datasources>
<datasource caption='Sheet1 (UnionVIHI)' inline='true' name='federated.0k72kmr0jrwig318xcfss0nkls34' version='18.1'>
<connection class='federated'>
<named-connections>
<named-connection caption='UnionVIHI' name='excel-direct.0tys9e40zyiw6v183m39c1fxt19x'>
<connection class='excel-direct' cleaning='no' compat='no' dataRefreshTime='' filename='C:/Users/deirdre.corr/Documents/College/Visualisation/Assignment/Tableau/UnionVIHI.xlsx' interpretationMode='0' password='' server='' validate='no' />
</named-connection>
</named-connections>
<relation connection='excel-direct.0tys9e40zyiw6v183m39c1fxt19x' name='Sheet1' table='[Sheet1$]' type='table'>
<columns gridOrigin='A1:H45:no:A1:H45:0' header='yes' outcome='2'>
<column datatype='string' name='Type' ordinal='0' />
<column datatype='integer' name='TypeNumeric' ordinal='1' />
<column datatype='string' name='Characteristic' ordinal='2' />
<column datatype='real' name='White only' ordinal='3' />
<column datatype='real' name='Black or African American only' ordinal='4' />
<column datatype='real' name='American Indian or Alaska Native only' ordinal='5' />
<column datatype='real' name='Asian only' ordinal='6' />
<column datatype='real' name='Hispanic or Latino' ordinal='7' />
</columns>
</relation>
<metadata-records>
<metadata-record class='column'>
<remote-name>Type</remote-name>
<remote-type>130</remote-type>
<local-name>[Type]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>Type</remote-alias>
<ordinal>0</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<contains-null>true</contains-null>
<collation flag='1' name='LEN_RIE_S2' />
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"WSTR"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>TypeNumeric</remote-name>
<remote-type>20</remote-type>
<local-name>[TypeNumeric]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>TypeNumeric</remote-alias>
<ordinal>1</ordinal>
<local-type>integer</local-type>
<aggregation>Sum</aggregation>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"I8"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>Characteristic</remote-name>
<remote-type>130</remote-type>
<local-name>[Characteristic]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>Characteristic</remote-alias>
<ordinal>2</ordinal>
<local-type>string</local-type>
<aggregation>Count</aggregation>
<contains-null>true</contains-null>
<collation flag='1' name='LEN_RIE_S2' />
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"WSTR"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>White only</remote-name>
<remote-type>5</remote-type>
<local-name>[White only]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>White only</remote-alias>
<ordinal>3</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<precision>15</precision>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"R8"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>Black or African American only</remote-name>
<remote-type>5</remote-type>
<local-name>[Black or African American only]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>Black or African American only</remote-alias>
<ordinal>4</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<precision>15</precision>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"R8"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>American Indian or Alaska Native only</remote-name>
<remote-type>5</remote-type>
<local-name>[American Indian or Alaska Native only]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>American Indian or Alaska Native only</remote-alias>
<ordinal>5</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<precision>15</precision>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"R8"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>Asian only</remote-name>
<remote-type>5</remote-type>
<local-name>[Asian only]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>Asian only</remote-alias>
<ordinal>6</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<precision>15</precision>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"R8"</attribute>
</attributes>
</metadata-record>
<metadata-record class='column'>
<remote-name>Hispanic or Latino</remote-name>
<remote-type>5</remote-type>
<local-name>[Hispanic or Latino]</local-name>
<parent-name>[Sheet1]</parent-name>
<remote-alias>Hispanic or Latino</remote-alias>
<ordinal>7</ordinal>
<local-type>real</local-type>
<aggregation>Sum</aggregation>
<precision>15</precision>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='string' name='DebugRemoteType'>"R8"</attribute>
</attributes>
</metadata-record>
<metadata-record class='capability'>
<remote-name />
<remote-type>0</remote-type>
<parent-name>[Sheet1]</parent-name>
<remote-alias />
<aggregation>Count</aggregation>
<contains-null>true</contains-null>
<attributes>
<attribute datatype='integer' name='context'>6</attribute>
<attribute datatype='string' name='gridOrigin'>"A1:H45:no:A1:H45:0"</attribute>
<attribute datatype='boolean' name='header'>true</attribute>
<attribute datatype='integer' name='outcome'>2</attribute>
</attributes>
</metadata-record>
</metadata-records>
</connection>
<aliases enabled='yes' />
<column datatype='real' name='[American Indian or Alaska Native only]' role='measure' type='quantitative' />
<column datatype='real' name='[Asian only]' role='measure' type='quantitative' />
<column datatype='real' name='[Black or African American only]' role='measure' type='quantitative' />
<column caption='Year' datatype='string' name='[Characteristic]' role='dimension' type='nominal' />
<column datatype='real' name='[Hispanic or Latino]' role='measure' type='quantitative' />
<column datatype='integer' name='[Number of Records]' role='measure' type='quantitative' user:auto-column='numrec'>
<calculation class='tableau' formula='1' />
</column>
<column datatype='integer' name='[TypeNumeric]' role='measure' type='quantitative' />
<column datatype='string' name='[Type]' role='dimension' type='nominal'>
<aliases>
<alias key='"HI"' value='Without Private Health Insurance in %' />
<alias key='"VI"' value='With Vision Impairment in %' />
</aliases>
</column>
<column datatype='real' name='[White only]' role='measure' type='quantitative' />
<column-instance column='[American Indian or Alaska Native only]' derivation='Sum' name='[sum:American Indian or Alaska Native only:qk]' pivot='key' type='quantitative' />
<column-instance column='[Asian only]' derivation='Sum' name='[sum:Asian only:qk]' pivot='key' type='quantitative' />
<column-instance column='[Black or African American only]' derivation='Sum' name='[sum:Black or African American only:qk]' pivot='key' type='quantitative' />
<column-instance column='[Hispanic or Latino]' derivation='Sum' name='[sum:Hispanic or Latino:qk]' pivot='key' type='quantitative' />
<column-instance column='[Number of Records]' derivation='Sum' name='[sum:Number of Records:qk]' pivot='key' type='quantitative' />
<column-instance column='[TypeNumeric]' derivation='Sum' name='[sum:TypeNumeric:qk]' pivot='key' type='quantitative' />
<column-instance column='[White only]' derivation='Sum' name='[sum:White only:qk]' pivot='key' type='quantitative' />
<layout dim-ordering='alphabetic' dim-percentage='0.518672' measure-ordering='alphabetic' measure-percentage='0.481328' show-aliased-fields='true' show-structure='true' />
<style>
<style-rule element='mark'>
<encoding attr='color' field='[:Measure Names]' palette='color_blind_10_0' type='palette'>
<map to='#1170aa'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Black or African American only:qk]"</bucket>
</map>
<map to='#4e79a7'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:American Indian or Alaska Native only VI:qk]"</bucket>
</map>
<map to='#57606c'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Hispanic or Latino VI:qk]"</bucket>
</map>
<map to='#57606c'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Hispanic or Latino:qk]"</bucket>
</map>
<map to='#59a14f'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Number of Records:qk]"</bucket>
</map>
<map to='#76b7b2'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34]"</bucket>
</map>
<map to='#c85200'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:American Indian or Alaska Native only:qk]"</bucket>
</map>
<map to='#c85200'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Black or African American only VI:qk]"</bucket>
</map>
<map to='#c8d0d9'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:White only VI:qk]"</bucket>
</map>
<map to='#c8d0d9'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:White only:qk]"</bucket>
</map>
<map to='#e15759'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:TypeNumeric:qk]"</bucket>
</map>
<map to='#f28e2b'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Asian only VI:qk]"</bucket>
</map>
<map to='#ffbc79'>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Asian only:qk]"</bucket>
</map>
</encoding>
</style-rule>
</style>
<semantic-values>
<semantic-value key='[Country].[Name]' value='"Ireland"' />
</semantic-values>
</datasource>
</datasources>
<worksheets>
<worksheet name='VI vs HI'>
<layout-options>
<title>
<formatted-text>
<run>% of US Adult Citizens with Moderate to Severe Visual Impairment vs US Adult Citizens without Private Health Insurance</run>
</formatted-text>
</title>
</layout-options>
<table>
<view>
<datasources>
<datasource caption='Sheet1 (UnionVIHI)' name='federated.0k72kmr0jrwig318xcfss0nkls34' />
</datasources>
<datasource-dependencies datasource='federated.0k72kmr0jrwig318xcfss0nkls34'>
<column datatype='real' name='[American Indian or Alaska Native only]' role='measure' type='quantitative' />
<column datatype='real' name='[Asian only]' role='measure' type='quantitative' />
<column datatype='real' name='[Black or African American only]' role='measure' type='quantitative' />
<column caption='Year' datatype='string' name='[Characteristic]' role='dimension' type='nominal' />
<column datatype='real' name='[Hispanic or Latino]' role='measure' type='quantitative' />
<column datatype='string' name='[Type]' role='dimension' type='nominal'>
<aliases>
<alias key='"HI"' value='Without Private Health Insurance in %' />
<alias key='"VI"' value='With Vision Impairment in %' />
</aliases>
</column>
<column datatype='real' name='[White only]' role='measure' type='quantitative' />
<column-instance column='[Characteristic]' derivation='None' name='[none:Characteristic:nk]' pivot='key' type='nominal' />
<column-instance column='[Type]' derivation='None' name='[none:Type:nk]' pivot='key' type='nominal' />
<column-instance column='[American Indian or Alaska Native only]' derivation='Sum' name='[sum:American Indian or Alaska Native only:qk]' pivot='key' type='quantitative' />
<column-instance column='[Asian only]' derivation='Sum' name='[sum:Asian only:qk]' pivot='key' type='quantitative' />
<column-instance column='[Black or African American only]' derivation='Sum' name='[sum:Black or African American only:qk]' pivot='key' type='quantitative' />
<column-instance column='[Hispanic or Latino]' derivation='Sum' name='[sum:Hispanic or Latino:qk]' pivot='key' type='quantitative' />
<column-instance column='[White only]' derivation='Sum' name='[sum:White only:qk]' pivot='key' type='quantitative' />
</datasource-dependencies>
<filter class='categorical' column='[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]'>
<groupfilter function='union' user:op='manual'>
<groupfilter function='member' level='[:Measure Names]' member='"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:American Indian or Alaska Native only:qk]"' />
<groupfilter function='member' level='[:Measure Names]' member='"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Asian only:qk]"' />
<groupfilter function='member' level='[:Measure Names]' member='"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Black or African American only:qk]"' />
<groupfilter function='member' level='[:Measure Names]' member='"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:Hispanic or Latino:qk]"' />
<groupfilter function='member' level='[:Measure Names]' member='"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:White only:qk]"' />
</groupfilter>
</filter>
<filter class='categorical' column='[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]'>
<groupfilter from='"1997"' function='range' level='[none:Characteristic:nk]' to='"2016"' user:ui-domain='relevant' user:ui-enumeration='inclusive' user:ui-marker='enumerate' />
</filter>
<slices>
<column>[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]</column>
<column>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]</column>
</slices>
<aggregation value='true' />
</view>
<style>
<style-rule element='axis'>
<format attr='title' class='0' field='[federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values]' scope='rows' value='In Percent %' />
<format attr='display' class='0' field='[federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values]' scope='rows' value='false' />
</style-rule>
<style-rule element='cell'>
<format attr='width' field='[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]' value='33' />
<format attr='text-format' field='[federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values]' value='n#,##0.0"%";-#,##0.0"%"' />
</style-rule>
<style-rule element='worksheet'>
<format attr='display-field-labels' scope='rows' value='false' />
</style-rule>
<style-rule element='legend-title-text'>
<format attr='color' field='[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]' value='Race/Ethnic Group'>
<formatted-text>
<run>Race/Ethnic Group</run>
</formatted-text>
</format>
</style-rule>
<style-rule element='page-card-title'>
<format attr='title' value='Compare'>
<formatted-text>
<run>Compare</run>
</formatted-text>
</format>
</style-rule>
</style>
<panes>
<pane selection-relaxation-option='selection-relaxation-allow'>
<view>
<breakdown value='auto' />
</view>
<mark class='Line' />
<encodings>
<color column='[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]' />
<lod column='[federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values]' />
</encodings>
<style>
<style-rule element='mark'>
<format attr='mark-markers-mode' value='all' />
</style-rule>
<style-rule element='pane'>
<format attr='minwidth' value='-1' />
<format attr='maxwidth' value='-1' />
</style-rule>
</style>
</pane>
</panes>
<rows>([federated.0k72kmr0jrwig318xcfss0nkls34].[none:Type:nk] * [federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values])</rows>
<cols>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]</cols>
<pages>
<column>[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]</column>
</pages>
<page-trail-options mark-type='all' trail-effect='none' />
</table>
</worksheet>
</worksheets>
<dashboards>
<dashboard name='VI vs HI Dashboard'>
<style />
<size maxheight='800' maxwidth='1000' minheight='800' minwidth='1000' />
<zones>
<zone h='57250' id='1' name='VI vs HI' w='73600' x='1100' y='1250' />
<zone h='14750' id='6' name='VI vs HI' pane-specification-id='0' param='[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]' type='color' w='23300' x='76300' y='58375' />
<zone h='15250' id='7' name='VI vs HI' synchronized='1' type='currpage' w='74100' x='1100' y='58375' />
<zone h='39750' id='8' type='text' w='22700' x='76100' y='13500'>
<formatted-text>
<run>Data sourced from the US Census Bureau </run>
<run auto-url='true' fontcolor='#0000ff' fontname='Calibri,sans-serif' fontsize='11' hyperlink='tabdoc:load-url url=&quot;https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml&quot;' underline='true'>https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml</run>
<run auto-url='true' hyperlink='tabdoc:load-url url=&quot;,&quot;'>,</run>
<run>Æ </run>
<run auto-url='true' fontcolor='#0000ff' fontname='Calibri,sans-serif' fontsize='11' hyperlink='tabdoc:load-url url=&quot;http://www.cdc.gov/nchs/hus/contents2016.htm#043&quot;' underline='true'>http://www.cdc.gov/nchs/hus/contents2016.htm#043</run>
</formatted-text>
</zone>
</zones>
</dashboard>
</dashboards>
<windows source-height='30'>
<window class='worksheet' name='VI vs HI'>
<cards>
<edge name='left'>
<strip size='160'>
<card type='pages' />
<card type='filters' />
<card type='marks' />
<card type='measures' />
</strip>
</edge>
<edge name='top'>
<strip size='2147483647'>
<card type='columns' />
</strip>
<strip size='2147483647'>
<card type='rows' />
</strip>
<strip size='2147483647'>
<card type='title' />
</strip>
</edge>
<edge name='right'>
<strip size='194'>
<card looped='1' type='currpage' />
<card pane-specification-id='0' param='[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]' type='color' />
</strip>
</edge>
</cards>
<viewpoint>
<current-page>
<multibucket>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:White only:qk]"</bucket>
</multibucket>
</current-page>
<highlight>
<color-one-way>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]</field>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]</field>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Type:nk]</field>
</color-one-way>
</highlight>
</viewpoint>
<simple-id uuid='{C19A861A-247C-4ADF-8DD2-717DE01CE1A0}' />
</window>
<window class='dashboard' maximized='true' name='VI vs HI Dashboard'>
<viewpoints>
<viewpoint name='VI vs HI'>
<current-page>
<multibucket>
<bucket>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:White only:qk]"</bucket>
</multibucket>
</current-page>
<zoom type='entire-view' />
<selection-collection>
<tuple-selection>
<tuple-reference>
<tuple-descriptor>
<pane-descriptor>
<x-fields>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]</field>
</x-fields>
<y-fields>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Type:nk]</field>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values]</field>
</y-fields>
</pane-descriptor>
<columns>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Characteristic:nk]</field>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[none:Type:nk]</field>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]</field>
<field>[federated.0k72kmr0jrwig318xcfss0nkls34].[Multiple Values]</field>
</columns>
</tuple-descriptor>
<tuple>
<value>"2016"</value>
<value>"VI"</value>
<value>"[federated.0k72kmr0jrwig318xcfss0nkls34].[sum:White only:qk]"</value>
<value>9.8000000000000007</value>
</tuple>
</tuple-reference>
</tuple-selection>
</selection-collection>
<highlight field='[federated.0k72kmr0jrwig318xcfss0nkls34].[:Measure Names]'>
<bucket-selection />
</highlight>
</viewpoint>
</viewpoints>
<active id='-1' />
<simple-id uuid='{2C37B6C3-C2F2-4958-8DF2-1AC991A944D9}' />
</window>
</windows>
<thumbnails>
<thumbnail height='192' name='VI vs HI' width='192'>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</thumbnail>
<thumbnail height='192' name='VI vs HI Dashboard' width='192'>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</thumbnail>
</thumbnails>
</workbook>