The aim of this study was to compare the performance of two bivariate visualizations by measuring response correctness (error rate) and response time, and to identify the differences in cognitive processes involved in map-reading tasks by using eye-tracking methods. The present study is based on our previous research and the hypothesis that the use of different visualization methods may lead to significant cognitive-processing differences. We applied extrinsic and intrinsic visualizations in the study. Participants in the experiment were presented maps which depicted two variables (soil moisture and soil depth) and asked to identify the areas which displayed either a single condition (e.g., "find an area with low soil depth") or both conditions (e.g., "find an area with high soil moisture and low soil depth"). The research sample was composed of 31 social sciences and humanities university students. The experiment was performed under laboratory conditions, and Hypothesis software was used for data collection. Eye-tracking data were collected for 23 of the participants. An SMI RED-m eye-tracker was used to determine whether either of the two visualization methods was more efficient for solving the given map-reading tasks. Our results showed that with the intrinsic visualization method, the participants spent significantly more time with the map legend. This result suggests that extrinsic and intrinsic visualizations induce different cognitive processes. The intrinsic method was observed to generally require more time and led to higher error rates. In summary, the extrinsic method was found to be more efficient than the intrinsic method, although the difference was less pronounced in the tasks which contained two variables, which proved to be better suited to intrinsic visualization.

Department of Geography Faculty of Science Masaryk University Brno Czech Republic

Department of Geoinformatics Faculty of Science Palacký University Olomouc Olomouc Czech Republic

Department of Information and Library Studies Faculty of Arts Masaryk University Brno Czech Republic

See more in PubMed

Montello DR. Cognitive map-design research in the twentieth century: theoretical and empirical approaches. Cartogr Geogr Inf Sci. 2002; 29: 283–304. 10.1559/152304002782008503 DOI

Montello DR., Freundschuh S. Cognition of geographic information. In Usery LY, McMaster RB, editors. A research agenda for geographic information science, Florida: CRC Press; 2004. pp. 61–91.

Kolacny A. Cartographic information: A fundamental concept and term in modern cartography. Cartogr J. 1969; 6: 47–49. 10.1179/caj.1969.6.1.47 DOI

Staněk K, Friedmannová L, Kubíček P, Konečný M. Selected issues of cartographic communication optimization for emergency centers. Int J Digit Earth. 2010; 3: 316–339. 10.1080/17538947.2010.484511 DOI

Konečný M, Kubíček P., Stachoň Z., Šašinka Č. The usability of selected base maps for crises management: users’ perspectives. Appl. Geomat. 2011; 3: 189–198. 10.1007/s12518-011-0053-1 DOI

Šašinka Č, Stachoň Z, Kubíček P, Tamm S, Matas A, Kukaňová M. The Impact of Global/Local Bias on Task-Solving in Map-Related Tasks Employing Extrinsic and Intrinsic Visualization of Risk Uncertainty Maps. Cartogr J. 2019; 56: 175–191. 10.1080/00087041.2017.1414018 DOI

Lokka I, Çöltekin A. Simulating Navigation with Virtual 3D Geovisualizations—A focus on memory related factors. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2016; XLI-B2: 671–673. 10.5194/isprs-archives-XLI-B2-671-2016 DOI

Ooms K, De Maeyer P, Fack V, Van Assche E., Witlox F. Interpreting maps through the eyes of expert and novice users. Int. J. Geogr. Inf. Sci. 2012; 26: 1773–1788. 10.1080/13658816.2011.642801 DOI

Ooms K, De Maeyer P, Fack V. Study of the attentive behavior of novice and expert map users using eye tracking. Cartogr. Geogr. Inf. Sci. 2014; 41: 37–54. 10.1080/15230406.2013.860255 DOI

Roth RE. Cartographic Interaction Primitives: Framework and Synthesis. Cartogr J. 2012; 49: 376–395. 10.1179/1743277412Y.0000000019 DOI

Rautenbach V, Coetzee S, Çöltekin C. Development and evaluation of a specialized task taxonomy for spatial planning–A map literacy experiment with topographic maps. ISPRS J Photogramm Remote Sens. 2017; 127: 16–26. 10.1016/j.isprsjprs.2016.06.013 DOI

Innes L. Maths for map users. Proceedings of the 21st International Cartographic Conference. 2003; pp. 727–738. Available: https://icaci.org/files/documents/ICC_proceedings/ICC2003/Papers/096.pdf.

Rinner C, Ferber S. The effects of map reading expertise and map type on eye movements in map comparison tasks. Abstract and poster presentation at the Conference on Spatial Information Theory. 2005; pp. 14–18. Available: https://digital.library.ryerson.ca/islandora/object/RULA%3A79.

Ooms K, De Maeyer P, Dupont L, Van Der Veken N, Van de Weghe N, Verplaetse S. Education in cartography: what is the status of young people’s map-reading skills? Cartogr Geogr Inf Sci. 2016; 43: 134–15. 10.1080/15230406.2015.1021713 DOI

Koç H, Demir S. Developing valid and reliable map literacy scale. Rev Int Geograph Educ Online. 2014; 4: 120–137.

Clarke D. Are you functionally map literate? Proceedings of the 21st International Cartographic Conference. 2003; pp. 10–16. Available: http://lazarus.elte.hu/cet/publications/088.pdf.

Lobben AK. Tasks, Strategies, and Cognitive Processes Associated with Navigational Map Reading: A Review Perspective. Prof Geogr. 2014; 56: 270–281. 10.1111/j.0033-0124.2004.05602010.x DOI

Fabrikant SI, Lobben AK. Introduction: Cognitive Issues in Geographic Information Visualization. Cartographica The International Journal for Geographic Information and Geovisualization. 2009; 44:139–143. 10.3138/carto.44.3.139 DOI

Slocum TA, Blok C, Jiang B, Koussoulakou A, Montello DR, Fuhrmann S, Hedley NR. Cognitive and usability issues in geovisualization. Cartogr Geogr Inf Sci. 2001; 28, 61–75. 10.1559/152304001782173998 DOI

MacEachren AM, Kraak M-J. Research Challenges in Geovisualization. Cartogr Geogr Inf Sci. 2001; 28: 3–12. 10.1559/152304001782173970 DOI

Olson JM. Spectrally Encoded Two-Variable Maps. Ann Am Assoc Geogr. 1981; 71: 259–276. 10.1111/j.1467-8306.1981.tb01352.x DOI

Larkin JH, Simon HA. Why a diagram is (sometimes) worth ten thousand words. Cogn Sci. 1987; 11: 65–99. 10.1016/S0364-0213(87)80026-5 DOI

MacEachren AM. How Maps Work: Representation, Visualization, and Design. 1st ed. New York: Guilford Press; 1995.

Robinson AH, Morrison JL, Muehrcke PC, Kimerling AJ, Guptill SC. Elements of Cartography. 6th ed. New York: John Wiley & Sons; 2009.

Slocum TA, McMaster RB, Kessler FC, Howard HH. Thematic cartography and geovisualization. 3rd ed. New jersey: Pearson Prentice Hall; 2008.

Gershon N. Visualization of an imperfect world. IEEE Comput Graph. 1998; 18: 43–45. 10.1109/38.689662 DOI

D’Zmura M. Color in visual search. Vision Res. 1991; 31: 951–966. 10.1016/0042-6989(91)90203-h PubMed DOI

Lindsey DT, Brown AM, Reijnen E, Rich AN, Kuzmova YI, Wolfe JM. Color channels, not color appearance or color categories, guide visual search for desaturated color targets. Psychol Sci. 2010; 21: 1208–1214. 10.1177/0956797610379861 PubMed DOI PMC

Itti L, Kof CH. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res. 2000; 40: 1489–1506. 10.1016/s0042-6989(99)00163-7 PubMed DOI

Bertin J. Sémiologie graphique. 2nd ed. Paris: Gauthier-Villars; 1973.

Lavie N., Tsal Y. Perceptual load as a major determinant of the locus of selection in visual attention. Percept Psychophys. 1994; 56: 183–197. 10.3758/bf03213897 PubMed DOI

Granholm E, Asarnow RF, Sarkin AJ, Dykes KL. Pupillary responses index cognitive resource limitations. Psychophysiology. 1996; 33: 457–461. 10.1111/j.1469-8986.1996.tb01071.x PubMed DOI

Brünken R, Steinbacher S, Plass JL, Leutner D. Assessment of cognitive load in multimedia learning using dual-task methodology. Exp Psychol. 2002; 49: 109–119. 10.1027//1618-3169.49.2.109 PubMed DOI

Nelson ES. Using Selective Attention Theory to Design Bivariate Point Symbols. Cartogr Perspect. 1999; 32: 6–28. 10.14714/CP32.625 DOI

Treisman AM, Gelade G. A feature-integration theory of attention. Cogn Psychol. 1980; 12: 97–136. 10.1016/0010-0285(80)90005-5 PubMed DOI

Wolfe JM, Cave KR, Franzel SL. Guided search: an alternative to the feature integration model for visual search. J Exp Psychol Hum Percept Perform. 1989; 15: 419–433. 10.1037//0096-1523.15.3.419 PubMed DOI

MacEachren AM. Visualizing Uncertain Information. Cartogr Perspect. 1992; 13: 10–19. 10.14714/CP13.1000 DOI

Nelson ES. Designing Effective Bivariate Symbols: The Influence of Perceptual Grouping Processes. Cartogr Geogr Inf Sci. 2000; 27: 261–278. 10.1559/152304000783547786 DOI

Nelson ES. The Impact of Bivariate Symbol Design on Task Performance in a Map Setting. Cartographica. 2002; 37: 61–78. 10.3138/V743-K505-5510-66Q5 DOI

Kubíček P, Šašinka Č, Stachoň Z, Štěrba Z, Apeltaur J, Urbánek T. Cartographic Design and Usability of Visual Variables for Linear Features. Cartogr J. 2017; 54: 91–102. 10.1080/00087041.2016.1168141 DOI

Kubíček P, Šašinka Č, Stachoň Z. Uncertainty Visualization Testing. Proceedings of the 4th conference on Cartography and GIS. 2012; pp. 247–256. Available: https://www.researchgate.net/publication/292155643_UNCERTAINTY_VISUALIZATION_TESTING.

Brus J, Kučera M, Popelka S. Intuitiveness of geospatial uncertainty visualizations: a user study on point symbols. Geografie. 2019; 124:163–85. 10.37040/geografie2019124020163 DOI

Elmer M. Symbol Considerations for Bivariate Thematic Maps. M.Sc. Thesis, University of Wisconsin–Madison. 2012. Available from: http://resources.maphugger.com/melmer_webedition.pdf.

Kunz M, Grêt-Regamey A, Jurni L. Visualizing natural hazard data and uncertainties—Customization through a web-based cartographic information system. Int arch photogramm remote sens spat inf sci. 2010; 38: 1–7. Available from: https://www.isprs.org/proceedings/XXXVIII/part4/files/Kunz.pdf.

Lakoff G., Johnson M. The metaphorical structure of the human conceptual system. Cognitive Sci. 2018; 4: 195–208.

Harold J., Lorenzoni I., Shipley T. F., Coventry K. R. Cognitive and psychological science insights to improve climate change data visualization. In Nature Climate Change. 2016; 6(12): 1080–1089. 10.1038/nclimate3162 DOI

Tukey JW. Exploratory data analysis. Reading: Addison-Wesley; 1977.

Behrens JT. Principles and procedures of exploratory data analysis. Psychol Methods. 1997; 2: 131–160. 10.1037/1082-989X.2.2.131 DOI

Madan A, Kumar S. (2012). Usability evaluation methods: a literature review. Int. J. Eng. Sci. Technol. 2012; 4: 590–599.

ISO 9241–11:2018 Ergonomics of human-system interaction—Part 11: Usability: Definitions and concepts. 2018 [cited 19 November 2020]. In: ISO Online Browsing Platform [Internet]. Available: https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en.

Quesenbery W. The Five Dimensions of Usability. In Albers MJ, Mazur MB, editors. Content and Complexity: Information Design in Technical Communication. New York: Routlege; 2013. pp. 81–102.

King AJ, Bol N, Cummins RG, John KK. Improving Visual Behavior Research in Communication Science: An Overview, Review, and Reporting Recommendations for Using Eye-Tracking Methods. Commun Methods Meas. 2019; 13: 149–177. 10.1080/19312458.2018.1558194 DOI

Popelka S, Stachoň Z, Šašinka Č, Doležalová J. EyeTribe Tracker Data Accuracy Evaluation and Its Interconnection with Hypothesis Software for Cartographic Purposes. Comput Intell Neurosci. 2016; 9172506. 10.1155/2016/9172506 PubMed DOI PMC

Brychtova A, Coltekin A. An Empirical User Study for Measuring the Influence of Colour Distance and Font Size in Map Reading Using Eye Tracking. Cartogr J. 2016: 202–212. 10.1179/1743277414Y.0000000103 DOI

Keil J, Edler D, Kuchinke L, Dickmann F. Effects of visual map complexity on the attentional processing of landmarks. PLoS One. 2020; 15: e0229575. 10.1371/journal.pone.0229575 PubMed DOI PMC

Krassanakis V, Cybulski P. A review on eye movement analysis in map reading process: the status of the last decade. Geodesy Cartogr. 2019; 68: 191–209. 10.24425/gac.2019.126088 DOI

Brewer CA, Hatchard GW, Harrower MA. ColorBrewer in Print: A Catalog of Color Schemes for Maps. Cartogr Geogr Inf Sci. 2003; 30: 5–32. 10.1559/152304003100010929 DOI

OpenStreetMap contributors. Planet dump [Data file from 20180404]. 2018. Available: https://planet.openstreetmap.org.

Šašinka Č, Morong K, Stachoň Z. The Hypothesis Platform: An Online Tool for Experimental Research into Work with Maps and Behavior in Electronic Environments. ISPRS International Journal of Geo-Information. 2017; 6(12):407. 10.3390/ijgi6120407 DOI

Tomczak M, Tomczak E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends Sport Sci. 2014; 1: 19–25.

Mangiafico S. rcompanion: Functions to Support Extension Education Program Evaluation. R package version 2.3.25. 2020. Available: https://cran.r-project.org/package=rcompanion.

Sherman R. multicon: Multivariate Constructs. R package version 1.6. 2015. Available: https://cran.r-project.org/package=multicon.

Rosenthal R. Parametric measures of effect size. In Cooper H, Hedges LV, editors. The handbook of research synthesis. New York: Russell Sage Foundation; 1994. pp. 231–244.

Faul F, Erdfelder E, Buchner A, Lang A.-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods. 2009; 41: 1149–1160. 10.3758/BRM.41.4.1149 PubMed DOI

Brewer CA. Review of Colour Terms and Simultaneous Contrast Research for Cartography. Cartographica. 1992; 29: 20–30. 10.3138/80ML-3K54-0204-6172 DOI

Nothdurf HC. Salience from feature contrast: additivity across dimensions. Vision Res. 2000; 40: 1183–1201. 10.1016/s0042-6989(00)00031-6 PubMed DOI

Choudhury AMR. 5—Unusual visual phenomena and colour blindness. In: Choudhury AMR. Principles of Colour and Appearance Measurement. Object Appearance, Colour Perception and Instrumental Measurement. 1st ed. Cambridge: Woodhead Publishing; 2014. pp. 185–220. 10.1533/9780857099242.185 DOI

Van Zandt T. Analysis of Response Time Distributions. In Pashler H, Wixted J, editors. Stevens’ Handbook of Experimental Psychology. New Jersey: John Wiley & Sons Inc.; 2002. pp. 461–516.

Whelan R. Effective analysis of reaction time data. Psychol Rec. 2008; 58: 475–482. 10.1007/BF03395630 DOI

Ratcliff R. Methods for dealing with reaction time outliers. Psychol Bull. 1993; 114: 510–532. 10.1037/0033-2909.114.3.510 PubMed DOI

Chang K-T, Antes JR. Sex and Cultural Differences in Map Reading. American Cartographer. 1987; 14: 29–42, 10.1559/152304087783875345 DOI

Lacko D, Šašinka Č, Čeněk J, Stachoň Z, Lu W-L. Cross-Cultural Differences in Cognitive Style, Individualism/Collectivism and Map Reading between Central European and East Asian University Students. Stud Psychol (Bratisl). 2020; 62: 23–43. 10.31577/sp.2020.01.789 DOI

Stachoň Z, Šašinka Č, Čeněk J, Štěrba Z, Angsuesser S, Fabrikant SI, et al.. Cross-cultural differences in figure–ground perception of cartographic stimuli. Cartogr Geogr Inf Sci. 2019; 46: 82–94. 10.1080/15230406.2018.1470575 DOI

Anderson JR. Representational Types: A Tricode Proposal. Technical Report 82–1. Washington, D.C.: Office of Naval Research, 1982. Available from: https://apps.dtic.mil/dtic/tr/fulltext/u2/a116887.pdf

Alaçam Ö, Dalcı M. A Usability Study of WebMaps with Eye Tracking Tool: The Effects of Iconic Representation of Information. Human-Computer Interaction New Trends Lecture Notes in Computer Science. 2009; 12–21. 10.1007/978-3-642-02574-7_2 DOI

Fuchs S, Spachinger K, Dorner W, Rochman J, Serrhini K. Evaluating cartographic design in flood risk mapping. Environmental Hazards. 2009;8: 52–70. 10.3763/ehaz.2009.0007 DOI

Çöltekin A, Brychtová A, Griffin AL, Robinson AC, Imhof M, Pettit C. Perceptual complexity of soil-landscape maps: a user evaluation of color organization in legend designs using eye tracking. International Journal of Digital Earth. 2016;10: 560–581. 10.1080/17538947.2016.1234007 DOI

Çöltekin A, Heil B, Garlandini S, Fabrikant SI. Evaluating the Effectiveness of Interactive Map Interface Designs: A Case Study Integrating Usability Metrics with Eye-Movement Analysis. Cartography and Geographic Information Science. 2009;36: 5–17. 10.1559/152304009787340197 DOI