This study compares participants’ performance in extracting information from three-dimensional pie charts with individually extruded sectors in a single map frame against two-dimensional pie and bar charts in adjacent map frames. Specifically, we examine the response accuracy and response times of 181 adults who were asked to (1) identify the highest magnitude, (2) estimate a proportion, (3) perform both at the same time, and (4) solve a map-related task using the two chart setups. For each task, charts were shown on backgrounds with increasing visual complexity: a blank, a borders-only, and a choropleth map. Furthermore, we tested whether participants’ performance improved through additional practice with the two chart types. We did not observe any differences in participants’ aggregated response accuracy or response times between the tested three-dimensional and two-dimensional chart types for the (1) highest magnitude task and (2) proportion task. However, participants solved the (3) combination task with two-dimensional pie and bar charts on a blank background more accurately and were faster in fulfilling the (4) spatial task with three-dimensional pie charts. The first difference, however, leveled for participants who gained more practice and who accomplished the combination task on maps with higher visual complexity, whereas the second difference persisted even for more trained subjects.
WilkinsonL. The grammar of graphics. New York: Springer Science + Business Media, 1999.
2.
SchnabelO. Benutzerdefinierte Diagrammsignaturen in Karten, Konzepte, Formalisierung und Implementationen. PhD Thesis, ETH Zurich, Zurich, 2007.
3.
HeerJBostockMOgievetskyV. A tour through the visualization zoo. Commun ACM2010; 53(6): 59–67.
4.
SchnürerREichenbergerRSieberR, et al. 3D charts—taxonomy and implementation in a virtual globe. Rev Bras Cartogr2015; 67(5): 1055–1065.
5.
ErvinCW. Pie charts in financial communications. Inf Design J2011; 19(3): 205–215.
6.
EliasMBezerianosA. Exploration views: understanding dashboard creation and customization for visualization novices. In: CamposPGrahamNJorgeJ, et al. (eds) Human-computer interaction—INTERACT 2011. Berlin: Springer, 2011, pp. 274–291.
7.
KreuselerM. Visualization of geographically related multidimensional data in virtual 3D scenes. Comput Geosci2000; 26(1): 101–108.
8.
HurniL. Schweizer Weltatlas. Zürich: Schweizerische Konferenz der kantonalen Erziehungsdirektoren (EDK), 2017.
9.
Spence I. No humble pie: the origins and usage of a statistical chart. J Educ Behav Stat2005; 30(4): 353–368.
10.
ClevelandWSMcGillR. Graphical perception: theory, experimentation, and application to the development of graphical methods. J Am Stat Assoc1984; 79(387): 531–554.
11.
SimkinDHastieR. An information-processing analysis of graph perception. J Am Stat Assoc1987; 82(398): 454–465.
12.
SpenceILewandowskyS. Displaying proportions and percentages. Appl Cognitive Psych1991; 5(1): 61–77.
13.
HollandsJGSpenceI. Judgments of change and proportion in graphical perception. Hum Factors1992; 34(3): 313–334.
BurchM. The aesthetics of diagrams. In: Proceedings of the 6th international conference on information visualization theory and applications, Berlin, 2015, pp. 262–267, https://doi.org/10.5220/0005357502620267
16.
SiegristM. The use or misuse of three-dimensional graphs to represent lower-dimensional data. Behav Inform Technol1996; 15(2): 96–100.
17.
HughesBM. Just noticeable differences in 2D and 3D bar charts: a psychophysical analysis of chart readability. Percept Motor Skill2001; 92(2): 495–503.
18.
StewartBMCipollaJMBestLA. Extraneous information and graph comprehension: implications for effective design choices. Campus-Wide Inf Syst2009; 26(3), 191–200.
19.
SchonlauMPetersE. Comprehension of graphs and tables depend on the task: empirical evidence from two web-based studies. Stat Politics Policy2012; 3(2), https://doi.org/10.1515/2151-7509.1054
20.
TufteER. The visual display of quantitative information. 1st ed. Cheshire, CT: Graphics Press, 1983.
21.
BertinJ. Semiology of graphics: diagrams, networks, maps. Madison, WI: University of Wisconsin Press, 1983.
22.
ZacksJLevyETverskyB, et al. Reading bar graphs: effects of extraneous depth cues and graphical context. J Exp Psychol-Appl1998; 4(2): 119–138.
23.
FischerMH. Do irrelevant depth cues affect the comprehension of bar graphs?Appl Cognitive Psych2000; 14(2): 151–162.
24.
RangecroftM. As easy as pie. Behav Inform Technol2003; 22(6): 421–426.
25.
KosaraR. Evidence for area as the primary visual cue in pie charts. In: Proceedings of the IEEE VIS short paper, Vancouver, BC, Canada, 2019, https://doi.org/10.31219/osf.io/fcna4
26.
BrychtováAÇöltekinA. The effect of spatial distance on the discriminability of colors in maps. Cartogr Geogr Inf Sc2017; 44(3): 229–245.
27.
SieberRSerebryakovaMSchnürerR, et al. Atlas of Switzerland goes online and 3D—concept, architecture and visualization methods. In: GartnerGJobstMHuangH (eds) Progress in cartography. Cham: Springer International Publishing, 2016, pp. 171–184.
28.
ÇöltekinABleischSAndrienkoG, et al. Persistent challenges in geovisualization —a community perspective. Int J Cartogr2017; 3(Suppl. 1): 115–139.
29.
ClevelandWSHarrisCSMcGillR. Judgments of circle sizes on statistical maps. J Am Stat Assoc1982; 77(379): 541–547.
30.
KraakM-J. Computer-assisted cartographical three-dimensional imaging techniques. PhD Thesis, Delft University of Technology, Delft, 1988.
31.
LewandowskySHerrmannDJBehrensJT, et al. Perception of clusters in statistical maps. Appl Cognitive Psych1993; 7(6): 533–551.
32.
SeipelSCarvalhoL. Solving combined geospatial tasks using 2D and 3D bar charts. In: 2012 16th international conference on information visualisation, Montpellier, 11–13 July 2012, pp. 157–163. New York: IEEE.
33.
BleischSDykesJNebikerS. Evaluating the effectiveness of representing numeric information through abstract graphics in 3D desktop virtual environments. Cartogr J2008; 45(3): 216–226.
34.
Ben-ChaimDLappanGHouangRT. The effect of instruction on spatial visualization skills of middle school boys and girls. Am Educ Res J1988; 25(1): 51–71.
35.
PinkerS. A theory of graph comprehension. In: FreedleR (ed.) Artificial intelligence and the future of testing. Hillsdale, NJ: Lawrence Erlbaum, 1990, pp. 73–126.
36.
ShahPHoeffnerJ. Review of graph comprehension research: implications for instruction. Educ Psychol Rev2002; 14(1): 47–69.
37.
KrishnamoorthySNorthC. Learnability of interactive coordinated-view visualizations. In: Proceedings of the ninth international conference on information visualisation, London, 6–8 July 2005, pp. 306–311. New York: IEEE.
38.
LeeSKimSKwonBC. VLAT: development of a Visualization Literacy Assessment Test. IEEE T Vis Comput Gr2017; 23(1): 551–560.
WilkinsonL. Graphical methods: presentation. In: BaltesPB (ed.) International encyclopedia of the social & behavioral sciences. Oxford: Pergamon, 2001, pp. 6368–6379.
41.
BrüggerAFabrikantSIÇöltekinA. An empirical evaluation of three elevation change symbolization methods along routes in bicycle maps. Cartogr Geogr Inf Sc2016; 44(5): 436–451.
MacEachrenAM. The role of complexity and symbolization method in thematic map effectiveness. Ann Assoc Am Geogr1982; 72(4): 495–513.
44.
NeiderMBZelinskyGJ. Cutting through the clutter: searching for targets in evolving complex scenes. J Vision2011; 11(14): 1–16.
45.
SchnurSBektaşKÇöltekinA. Measured and perceived visual complexity: a comparative study among three online map providers. Cartogr Geogr Inf Sc2017; 45(3): 238–254.
46.
IshiharaS. Tests for color-blindness. Tokyo, Japan: Kanehara Shuppan, 1917.
47.
SlocumTA. Analyzing the communicative efficiency of two-sectored pie graphs. Cartographica1981; 18(3): 53–65.
48.
KosaraRSkauD. Judgment error in pie chart variations. In: Proceedings of the eurographics / IEEE VGTC conference on visualization: short papers, Groningen, 6–10 June 2016, pp. 91–95. New York: ACM.
European Parliament and the Council. Regulation (EC) on the establishment of a common classification of territorial units for statistics (NUTS) (No. 1059/2003), 2003, http://data.europa.eu/eli/reg/2003/1059/2018-01-18
51.
MeyerJShinarDLeiserD. Multiple factors that determine performance with tables and graphs. Hum Factors1997; 39(2): 268–286.
52.
PiagetJInhelderB. The child’s conception of space. New York: Humanities Press, 1956.
53.
RitziM. Multivariate Diagramme in kartografischen Produkten. Eine Nutzerstudie zur Evaluation von zwei- und dreidimensionalen Diagrammen in Karten. Master thesis, University of Zurich, Zurich, 2016.
54.
American Psychological Association. Ethical principles of psychologists and code of conduct, 2017, http://www.apa.org/ethics/code/
55.
LawrenceRJ. The lognormal as event-time distribution. In: CrowELShimizuK (eds) Lognormal distributions: theory and applications, Vol. 88. 1st ed. New York: Routledge, 1988, p. 387.
56.
NeiderMBZelinskyGJ. Searching for camouflaged targets: effects of target-background similarity on visual search. Vision Res2006; 46(14): 2217–2235.
57.
MackiewiczJ. Perceptions of clarity and attractiveness in PowerPoint graph slides. Tech Commun2007; 54(2): 145–156.
58.
McMasterRBSheaKS. Generalization in digital cartography. Washington, DC: Association of American Geographers, 1992.
59.
HollandsJGSpenceI. Judging proportion with graphs: the summation model. Appl Cognitive Psych1998; 12(2): 173–190.
60.
LoboM-JPietrigaEAppertC. An evaluation of interactive map comparison techniques. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, Seoul, Republic of Korea, 18–23 April 2015, pp. 3573–3582. New York: ACM.
61.
ShepardSMetzlerD. Mental rotation: effects of dimensionality of objects and type of task. J Exp Psychol Human1988; 14(1): 3–11.
