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degree possible. The structures of underlying data representations are generally neither accessible nor intuitive to the user of the visual analytics tool. They are frequently more complex in nature than the original data and are not necessarily smaller in size than the original data. The structures of the data representations may contain hundreds or thousands of dimensions and be unintelligible to a person, but they must be transformable into lower-dimensional representations for visualization and analysis.
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developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences.
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Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology
517:
Data representations are structured forms suitable for computer-based transformations. These structures must exist in the original data or be derivable from the data themselves. They must retain the information and knowledge content and the related context within the original data to the greatest
394:
principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst’s task of applying human judgments to
487:
Analytical reasoning techniques are the method by which users obtain deep insights that directly support situation assessment, planning, and decision making. Visual analytics must facilitate high-quality human judgment with a limited investment of the analysts’ time. Visual analytics tools must
578:
Visual representations translate data into a visible form that highlights important features, including commonalities and anomalies. These visual representations make it easy for users to perceive salient aspects of their data quickly. Augmenting the cognitive reasoning process with perceptual
508:
These tasks will be conducted through a combination of individual and collaborative analysis, often under extreme time pressure. Visual analytics must enable hypothesis-based and scenario-based analytical techniques, providing support for the analyst to reason based on the available evidence.
424:
Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways:
1238:. In Andreas Kerren, John T. Stasko, Jean-Daniel Fekete, and Chris North (Eds.), Information Visualization - Human-Centered Issues and Perspectives, pages 154-175, Lecture Notes in Computer Science 4950, Springer Berlin Heidelberg.
597:
1170:
A. Kerren and F. Schreiber. Toward the Role of
Interaction in Visual Analytics. In Proceedings of the 2012 Winter Simulation Conference (WSC '12), pages 420:1-420:13, Berlin, Germany, 2012. IEEE Computer Society
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as the results of automated analysis methods. This formalization of the visual analytics process is illustrated in the following figure. Arrows represent the transitions from one set to another one.
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Techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences.
448:
These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.
1161:
Stuart Card, J.D. Mackinlay, and Ben
Shneiderman (1999). "Readings in Information Visualization: Using Vision to Think". Morgan Kaufmann Publishers, San Francisco.
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that take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once.
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Visual analytics is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures,
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Pie chart representing the relative distribution of job satisfaction according to the
National Longitudinal Survey of Youth, 1997 cohort, survey year 2008
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406:. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows:
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Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis
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Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making
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1999:
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Daniel A. Keim, Florian
Mansmann, Jörn Schneidewind, Jim Thomas, and Hartmut Ziegler (2008). "Visual Analytics: Scope and Challenges"
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techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making).
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The typical data pre-processing applying data cleaning, data integration and data transformation functions is defined as
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Understanding past and present situations quickly, as well as the trends and events that have produced current conditions
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by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values
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Kielman, J. and Thomas, J. (Guest Eds.) (2009). "Special Issue: Foundations and
Frontiers of Visual Analytics". in:
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reasoning through visual representations permits the analytical reasoning process to become faster and more focused.
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by enhancing the recognition of patterns, such as when information is organized in space by its time relationships,
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represents the hypotheses generation process. We distinguish between functions that generate hypotheses from data
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Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and
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are an integral part of the visual analytics process. User interactions can either effect only visualizations
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Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows).
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Daniel Keim, Gennady
Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon (2008).
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by generating a new hypotheses from given ones. Furthermore, insight can be concluded from visualizations
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to enable human-information discourse. The design of the tools and techniques is based on
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Analyse First – Show the
Important – Zoom, Filter and Analyse Further – Details on Demand
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Visual analytics is a multidisciplinary field that includes the following focus areas:
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Monitoring current events for emergence of warning signs as well as unexpected events
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symbolizes the visualization functions, which are either functions visualizing data
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by reducing search, such as by representing a large amount of data in a small space,
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Visual and
Spatial Analysis: Advances in Data Mining, Reasoning, and Problem Soving
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are applied to the data, in order to reveal patterns as shown in the figure above.
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Information visualization handles abstract data structures such as trees or graphs.
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Computation and
Visualization for Understanding Dynamics in Geographic Domains.
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Kawa Nazemi (2014). Adaptive
Semantics Visualization. Eurographics Association
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Advances in Visual Information Systems: 9th International Conference (VISUAL).
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Visual Analytics Science and Technology (VAST), A Symposium of the IEEE 2007.
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data sources (i.e., the internet, newspapers, books, scientific experiments,
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Mastering the Visualization Age: Solving Problems with Visual Analytics
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that are needed to make analysis functions applicable to the data set.
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is a multidisciplinary science and technology field that emerged from
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The input for the data sets used in the visual analytics process are
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Bar chart representing the relative distribution of job satisfaction
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by perceptual monitoring of a large number of potential events, and
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876:. After the pre-processing step either automated analysis methods
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Determining indicators of the intent of an action or an individual
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reach conclusions from a combination of evidence and assumptions.
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Identifying possible alternative futures and their warning signs
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Visual analytics has some overlapping goals and techniques with
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In general the following paradigm is used to process the data:
892:(i.e., statistics, data mining, etc.) or visualization methods
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Illuminating the Path: The R&D Agenda for Visual Analytics
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Pak Chung Wong and J. Thomas (2004). "Visual Analytics". in:
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Keim D. A, Mansmann F, Schneidewind J, Thomas J, Ziegler H:
821:(i.e., selecting or zooming), or can effect only hypotheses
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and functions that generate hypotheses from visualizations
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1236:
Visual Analytics: Definition, Process, and Challenges
1086:, Volume 24, Issue 5, Sept.-Oct. 2004 Page(s): 20–21.
1137:. ITCS 4122/5122, Fall 2007. Retrieved 28 June 2008.
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1152:, Volume 8, Number 4, Winter 2009 Page(s): 239-314.
504:Supporting the decision maker in times of crisis.
670:More formally the visual analytics process is a
1228:May Yuan, Kathleen and Stewart Hornsby (2007).
652:. The goal or output of the process is insight
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1210:Boris Kovalerchuk and James Schwing (2004).
1119:National Visualization and Analytics Center.
1514:
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1250:. TU Darmstadt Dissertation. Eurographics.
986:Starlight Information Visualization System
621:). From these rich sources, the data sets
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279:List of concept- and mind-mapping software
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557:Automated design of optimal visualization
488:enable diverse analytical tasks such as:
722:including data transformation functions
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1084:IEEE Computer Graphics and Applications
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1191:Visual analytics: Scope and challenges
7:
768:or functions visualizing hypotheses
720: : S → S and W ∈ {T, C, SL, I}
526:Theories of visualization include:
1113:and Kristin A. Cook (Ed.) (2005).
362:can be facilitated by interactive
25:
1260:
976:Social network analysis software
681:is a concatenation of functions
596:
584:
30:
1065:(1987), graphical data analysis
743:and data integration functions
483:Analytical reasoning techniques
358:. It focuses on how analytical
92:Ontology (information science)
1:
1319:Biological data visualization
805:Moreover, user interactions
966:Interactive visual analysis
736:, data selection functions
660:or through confirmation of
474:Visual representations and
294:Problem structuring methods
2226:
1359:Mathematical visualization
941:Computational visualistics
729:, data cleaning functions
1354:Information visualization
1339:Educational visualization
1222:IEEE, Inc. Staff (2007).
1149:Information Visualization
710:describes the basic data
632:are chosen, whereas each
522:Theories of visualization
400:information visualization
352:information visualization
269:Entity–relationship model
52:Business decision mapping
2195:Scientific visualization
2190:Visualization (graphics)
1530:Charles-René de Fourcroy
1379:Scientific visualization
1306:of technical information
404:scientific visualization
356:scientific visualization
77:Knowledge visualization
1950:Christopher R. Johnson
1502:Technical illustration
1389:Software visualization
1131:Robert Kosara (2007).
981:Software visualization
574:Visual representations
476:interaction techniques
380:visual representations
264:Diagrammatic reasoning
87:Morphological analysis
2175:Computational science
1844:Lawrence J. Rosenblum
1657:Edward Walter Maunder
1581:Charles Joseph Minard
1399:User interface design
1374:Product visualization
1035:Lawrence J. Rosenblum
536:Semiology of Graphics
289:Ontology (philosophy)
188:Layered graph drawing
62:Graphic communication
2124:Scientific modelling
2099:Information graphics
1839:Clifford A. Pickover
1789:William S. Cleveland
1697:Henry Norris Russell
1682:Howard G. Funkhouser
1626:Florence Nightingale
1591:Francis Amasa Walker
1487:Statistical graphics
1409:Volume visualization
1384:Social visualization
1269:at Wikimedia Commons
1216:Guoping Qiu (2007).
811:, Z ∈ {V, H, CV, CH}
701:defined as follows:
513:Data representations
203:Organizational chart
198:Object-role modeling
115:Node–link approaches
2104:Information science
2067:in computer science
1859:Sheelagh Carpendale
1794:George G. Robertson
1631:Karl Wilhelm Pohlke
1566:André-Michel Guerry
1442:Graph of a function
1437:Engineering drawing
1195:Visual Data Mining:
1030:Theresa-Marie Rhyne
837:or from hypotheses
714:functionality with
567:Grammar of Graphics
97:Schema (psychology)
39:Information mapping
2205:Types of analytics
2144:Volume cartography
1908:Early 21st century
1804:Catherine Plaisant
1799:Bruce H. McCormick
1753:Mary Eleanor Spear
1743:Arthur H. Robinson
1677:Arthur Lyon Bowley
1650:Early 20th century
1497:Technical drawings
1369:Molecular graphics
1344:Flow visualization
1334:Data visualization
1008:Related scientists
961:Interaction design
208:Pathfinder network
72:Information design
57:Data visualization
2180:Computer graphics
2162:
2161:
2139:Visual perception
2089:Graphic organizer
2062:Computer graphics
2033:
2032:
2015:Martin Wattenberg
1990:Hanspeter Pfister
1945:Martin Krzywinski
1869:Jock D. Mackinlay
1849:Thomas A. DeFanti
1772:Late 20th century
1692:Ejnar Hertzsprung
1394:Technical drawing
1265:Media related to
1015:Cecilia R. Aragon
946:Critical thinking
638:, i ∈ (1, ..., m)
553:Jock D. Mackinlay
364:visual interfaces
345:
344:
44:Topics and fields
16:(Redirected from
2217:
2149:Volume rendering
2134:Visual analytics
2129:Spatial analysis
2109:Misleading graph
1960:David McCandless
1935:Gordon Kindlmann
1899:Alfred Inselberg
1894:Leland Wilkinson
1829:Michael Friendly
1763:Howard T. Fisher
1726:Mid 20th century
1667:W. E. B. Du Bois
1571:William Playfair
1561:Adolphe Quetelet
1535:Joseph Priestley
1518:Pre-19th century
1515:
1482:Skeletal formula
1349:Geovisualization
1324:Chemical imaging
1297:
1290:
1283:
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1267:Visual analytics
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1134:Visual Analytics
1129:
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1108:
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1080:
1057:Related software
1001:Visual reasoning
996:Traffic analysis
956:Google Analytics
929:Related subjects
898: : S → V, V
600:
588:
563:Leland Wilkinson
547:Languages of Art
348:Visual analytics
337:
330:
323:
274:Geovisualization
259:Design rationale
218:Semantic network
148:Conceptual graph
102:Visual analytics
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27:
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18:Visual Analytics
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2154:Information art
2094:Imaging science
2039:
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2010:Fernanda Viégas
2005:Moritz Stefaner
1930:Jessica Hullman
1903:
1874:Alan MacEachren
1824:Ben Shneiderman
1767:
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1364:Medical imaging
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1111:James J. Thomas
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951:Decision-making
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138:Concept lattice
107:Visual language
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240:
238:Tree structure
235:
230:
225:
220:
215:
210:
205:
200:
195:
190:
185:
180:
175:
170:
165:
160:
155:
150:
145:
140:
135:
130:
125:
117:
116:
112:
111:
110:
109:
104:
99:
94:
89:
84:
79:
74:
69:
64:
59:
54:
46:
45:
41:
40:
36:
35:
24:
14:
13:
10:
9:
6:
4:
3:
2:
2222:
2211:
2208:
2206:
2203:
2201:
2198:
2196:
2193:
2191:
2188:
2186:
2183:
2181:
2178:
2176:
2173:
2172:
2170:
2155:
2152:
2150:
2147:
2145:
2142:
2140:
2137:
2135:
2132:
2130:
2127:
2125:
2122:
2120:
2117:
2115:
2112:
2110:
2107:
2105:
2102:
2100:
2097:
2095:
2092:
2090:
2087:
2085:
2082:
2080:
2079:Graph drawing
2077:
2075:
2072:
2068:
2065:
2064:
2063:
2060:
2058:
2055:
2053:
2050:
2048:
2045:
2044:
2042:
2036:
2026:
2023:
2021:
2018:
2016:
2013:
2011:
2008:
2006:
2003:
2001:
2000:Claudio Silva
1998:
1996:
1993:
1991:
1988:
1986:
1983:
1981:
1978:
1976:
1973:
1971:
1968:
1966:
1965:Mauro Martino
1963:
1961:
1958:
1956:
1953:
1951:
1948:
1946:
1943:
1941:
1938:
1936:
1933:
1931:
1928:
1926:
1923:
1921:
1918:
1916:
1913:
1912:
1910:
1906:
1900:
1897:
1895:
1892:
1890:
1889:Michael Maltz
1887:
1885:
1882:
1880:
1877:
1875:
1872:
1870:
1867:
1865:
1862:
1860:
1857:
1855:
1854:George Furnas
1852:
1850:
1847:
1845:
1842:
1840:
1837:
1835:
1834:Howard Wainer
1832:
1830:
1827:
1825:
1822:
1820:
1817:
1815:
1812:
1810:
1807:
1805:
1802:
1800:
1797:
1795:
1792:
1790:
1787:
1785:
1782:
1780:
1777:
1776:
1774:
1770:
1764:
1761:
1759:
1756:
1754:
1751:
1749:
1746:
1744:
1741:
1739:
1738:Rudolf Modley
1736:
1734:
1731:
1730:
1728:
1724:
1718:
1715:
1713:
1710:
1708:
1705:
1703:
1702:Max O. Lorenz
1700:
1698:
1695:
1693:
1690:
1688:
1685:
1683:
1680:
1678:
1675:
1673:
1670:
1668:
1665:
1663:
1660:
1658:
1655:
1654:
1652:
1648:
1642:
1639:
1637:
1634:
1632:
1629:
1627:
1624:
1622:
1619:
1617:
1614:
1612:
1611:Charles Booth
1609:
1607:
1604:
1602:
1599:
1597:
1594:
1592:
1589:
1587:
1586:Luigi Perozzo
1584:
1582:
1579:
1577:
1576:August Kekulé
1574:
1572:
1569:
1567:
1564:
1562:
1559:
1557:
1556:Charles Dupin
1554:
1553:
1551:
1547:
1541:
1540:Gaspard Monge
1538:
1536:
1533:
1531:
1528:
1526:
1525:Edmond Halley
1523:
1522:
1520:
1516:
1513:
1509:
1503:
1500:
1498:
1495:
1493:
1490:
1488:
1485:
1483:
1480:
1478:
1475:
1473:
1470:
1468:
1465:
1463:
1460:
1458:
1455:
1453:
1450:
1448:
1445:
1443:
1440:
1438:
1435:
1433:
1430:
1428:
1425:
1424:
1422:
1416:
1410:
1407:
1405:
1402:
1400:
1397:
1395:
1392:
1390:
1387:
1385:
1382:
1380:
1377:
1375:
1372:
1370:
1367:
1365:
1362:
1360:
1357:
1355:
1352:
1350:
1347:
1345:
1342:
1340:
1337:
1335:
1332:
1330:
1329:Crime mapping
1327:
1325:
1322:
1320:
1317:
1316:
1314:
1310:
1305:
1304:Visualization
1298:
1293:
1291:
1286:
1284:
1279:
1278:
1275:
1268:
1263:
1259:
1258:
1254:
1249:
1246:
1243:
1240:
1237:
1233:
1231:
1227:
1225:
1221:
1219:
1215:
1213:
1209:
1208:
1204:
1196:
1192:
1186:
1183:
1177:
1174:
1167:
1164:
1158:
1155:
1151:
1150:
1143:
1140:
1136:
1135:
1128:
1126:
1122:
1118:
1117:
1112:
1107:
1105:
1103:
1101:
1099:
1097:
1095:
1093:
1089:
1085:
1079:
1076:
1069:
1064:
1061:
1060:
1056:
1051:
1048:
1046:
1043:
1041:
1038:
1036:
1033:
1031:
1028:
1026:
1023:
1021:
1018:
1016:
1013:
1012:
1007:
1002:
999:
997:
994:
992:
989:
987:
984:
982:
979:
977:
974:
972:
971:Interactivity
969:
967:
964:
962:
959:
957:
954:
952:
949:
947:
944:
942:
939:
937:
934:
933:
928:
923:
921:
920:
916:
913:
911:
891:
875:
846:
844:
843: : H → I
836:
835: : V → I
828:
827: : H → H
820:
819: : V → V
812:
803:
801:
800: : V → H
793:
792: : S → H
785:
777:
775:
774: : H → V
767:
766: : S → V
759:
751:
749:
742:
735:
728:
721:
713:
709:
702:
700:
680:
676:
673:
668:
666:
663:
659:
655:
643:
639:
631:
620:
616:
615:heterogeneous
608:
599:
594:
587:
582:
580:
573:
568:
564:
561:
558:
554:
551:
548:
544:
541:
538:
537:
532:
529:
528:
527:
521:
519:
512:
510:
503:
500:
497:
494:
491:
490:
489:
482:
477:
473:
470:
467:
464:
463:
462:
456:
451:
449:
443:
440:
437:
434:
431:
428:
427:
426:
419:
415:
412:
409:
408:
407:
405:
401:
396:
393:
389:
385:
381:
376:
369:
367:
365:
361:
357:
353:
349:
338:
333:
331:
326:
324:
319:
318:
316:
315:
310:
307:
305:
302:
300:
297:
295:
292:
290:
287:
285:
282:
280:
277:
275:
272:
270:
267:
265:
262:
260:
257:
256:
255:
254:
249:
244:
241:
239:
236:
234:
231:
229:
226:
224:
221:
219:
216:
214:
211:
209:
206:
204:
201:
199:
196:
194:
191:
189:
186:
184:
181:
179:
176:
174:
171:
169:
166:
164:
163:Graph drawing
161:
159:
156:
154:
153:Decision tree
151:
149:
146:
144:
141:
139:
136:
134:
133:Cognitive map
131:
129:
126:
124:
121:
120:
119:
118:
113:
108:
105:
103:
100:
98:
95:
93:
90:
88:
85:
83:
80:
78:
75:
73:
70:
68:
65:
63:
60:
58:
55:
53:
50:
49:
48:
47:
42:
37:
33:
29:
28:
19:
2185:Infographics
2133:
2114:Neuroimaging
2074:CPK coloring
2057:Color coding
1995:Hans Rosling
1975:Miriah Meyer
1940:Aaron Koblin
1925:Jeffrey Heer
1819:Edward Tufte
1814:Pat Hanrahan
1784:Nigel Holmes
1662:Otto Neurath
1601:Oliver Byrne
1549:19th century
1244:(2010) (pdf)
1235:
1229:
1223:
1217:
1211:
1197:2008, S. 82.
1194:
1190:
1185:
1176:
1166:
1157:
1147:
1142:
1133:
1115:
1083:
1078:
918:
917:
914:
893:
877:
849:
847:
838:
830:
822:
814:
806:
804:
795:
787:
784:, Y ∈ {S, V}
779:
778:
769:
761:
758:, W ∈ {S, H}
753:
752:
744:
737:
730:
723:
715:
704:
703:
682:
678:
674:
669:
664:
657:
653:
640:consists of
633:
622:
612:
577:
566:
559:(APT) (1986)
556:
546:
534:
525:
516:
507:
486:
460:
447:
423:
397:
377:
373:
347:
346:
299:Semantic Web
123:Argument map
101:
82:Mental model
67:Infographics
2200:Cartography
2047:Cartography
1985:Ade Olufeko
1955:Manuel Lima
1884:Kwan-Liu Ma
1809:Stuart Card
1779:Borden Dent
1717:Erwin Raisz
1672:Henry Gantt
1045:John Stasko
936:Cartography
418:data mining
304:Treemapping
213:Radial tree
143:Concept map
2169:Categories
1970:John Maeda
1748:John Tukey
1712:Harry Beck
1707:Fritz Kahn
1457:Photograph
1070:References
1050:Jim Thomas
677:, whereas
662:hypotheses
642:attributes
392:perceptual
183:Issue tree
158:Dendrogram
128:Cladistics
2052:Chartjunk
2020:Bang Wong
1915:Polo Chau
1621:John Snow
1596:John Venn
1477:Schematic
1462:Pictogram
1063:imc FAMOS
384:cognitive
360:reasoning
233:Topic map
223:Sociogram
178:Issue map
173:Hypertext
2210:Big data
2038:Related
1447:Ideogram
924:See also
906:, ..., f
886:, ..., f
870:, ..., S
675:F: S → I
648:, ..., A
627:, ..., S
370:Overview
251:See also
228:Timeline
193:Mind map
1920:Ben Fry
1432:Diagram
609:Process
2040:topics
1511:People
1418:Image
1312:Fields
1171:Press.
683:f ∈ {D
569:(1998)
549:(1977)
539:(1967)
452:Topics
390:, and
388:design
243:ZigZag
1492:Table
1427:Chart
1420:types
623:S = S
457:Scope
1467:Plot
902:= {f
882:= {f
402:and
354:and
284:Olog
1452:Map
874:)))
854:= D
695:, U
691:, H
687:, V
565:'s
555:'s
545:'s
533:'s
2171::
1193:.
1124:^
1091:^
908:vs
904:v1
888:sq
884:s1
866:(S
862:(D
858:(D
845:.
841:CH
833:CV
802:.
776:.
740:SL
650:ik
646:i1
386:,
366:.
1296:e
1289:t
1282:v
910:}
900:S
896:S
894:V
890:}
880:S
878:H
872:n
868:1
864:C
860:I
856:T
852:P
850:D
839:U
831:U
825:H
823:U
817:V
815:U
809:Z
807:U
798:V
796:H
790:S
788:H
782:Y
780:H
772:H
770:V
764:S
762:V
756:W
754:V
747:I
745:D
738:D
733:C
731:D
726:T
724:D
718:W
716:D
707:W
705:D
699:}
697:Z
693:Y
689:X
685:W
679:F
665:H
658:V
654:I
644:A
636:i
634:S
629:m
625:1
336:e
329:t
322:v
20:)
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