Geografie 2019, 124, 163-185
https://doi.org/10.37040/geografie2019124020163
Intuitiveness of geospatial uncertainty visualizations: a user study on point symbols
References
1. AIPPERSPACH, R. (2006): Categorization and Toolkit Support for Uncertainty Visualization. Retrieved from http://vis.berkeley.edu/courses/cs294-10-sp06/wiki/images/2/21/AipperspachFinalReport. pdf (5.5.2019).
2. BEARD, M.K., BUTTENFIELD, B.P., CLAPHAM, S.B. (1991): NCGIA Research Initiative 7: Visualization of Spatial Data Quality: Scientific Report for the Specialist Meeting 8–12 June 1991, Castine, Maine. Retrieved from Santa Barbara.
3. BERJAWI, B., DUCHATEAU, F., CHESNEAU, E., FAVETTA, F., SECCIA, G., CUNTY, C., MIQUEL, M., LAURINI, R. (2014): Uncertainty visualization of multi-providers cartographic integration. Journal of Visual Languages and Computing, 25, 6, 995–1002.
<https://doi.org/10.1016/j.jvlc.2014.10.033>
4. BRAND, J., JOHNSON, A.P. (2014): Attention to local and global levels of hierarchical Navon figures affects rapid scene categorization. Frontiers in psychology, 5, 1274.
<https://doi.org/10.3389/fpsyg.2014.01274>
5. BRUS, J. (2014): Vizualizace nejistoty v environmentálních studiích (1st edition ed.). Univerzita Palackého v Olomouci, Olomouc.
6. BRUS, J., PECHANEC, V., MACHAR, I. (2018): Depiction of uncertainty in the visually interpreted land cover data. Ecological Informatics, 47, 10–13.
<https://doi.org/10.1016/j.ecoinf.2017.10.015>
7. BRUS, J., VOŽENÍLEK, V., POPELKA, S. (2013): An Assessment of Quantitative Uncertainty Visualization Methods for Interpolated Meteorological Data. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.): Computational Science and Its Applications – Iccsa 2013, Pt Iv 7974, 166–178.
<https://doi.org/10.1007/978-3-642-39649-6_12>
8. ÇÖLTEKIN, A., FABRIKANT, S.I., LACAYO, M. (2010): Exploring the efficiency of users’ visual analytics strategies based on sequence analysis of eye movement recordings. International Journal of Geographical Information Science, 24, 10, 1559–1575.
<https://doi.org/10.1080/13658816.2010.511718>
9. DOLEŽALOVÁ, J., POPELKA, S. (2016): ScanGraph: A Novel Scanpath Comparison Method Using Visualisation of Graph Cliques. Journal of Eye Movement Research, 9, 4, doi:ARTN 510.16910/jemr.9.4.5.
10. EGETH, H., DAGENBACH, D. (1991): Parallel versus serial processing in visual search: Further evidence from subadditive effects of visual quality. Journal of Experimental Psychology: Human Perception and Performance, 17, 2, 551.
<https://doi.org/10.1037//0096-1523.17.2.551>
11. FINGER, R., BISANTZ, A.M. (2002): Utilizing graphical formats to convey uncertainty in a decision-making task. Beoretical Issues in Ergonomics Science, 3, 1, 1–25.
<https://doi.org/10.1080/14639220110110324>
12. FUHRMANN, S., KOMOGORTSEV, O., TAMIR, D. (2009): Investigating hologram‐based route planning. Transactions in GIS, 13, 177–196.
<https://doi.org/10.1111/j.1467-9671.2009.01158.x>
13. HABER, R.B., MCNABB, D.A. (1990): Visualization idioms: A conceptual model for scientific visualization systems. Visualization in scientific computing, 74, 93.
14. HOŠKOVÁ-MAYEROVÁ, Š. et al. (2013): Spatial Database Quality and the Potential Uncertainty Sources. In: Proto, A.N., Squillante, M., Kacprzyk, J. (eds): Advanced Dynamic Modeling of Economic and Social Systems, Springer, 127–142.
<https://doi.org/10.1007/978-3-642-32903-6_10>
15. INCOUL, A. et al. (2015): Comparing paper and digital topographic maps using eye tracking. In: Brus, J., Vondráková, A., Voženílek, V. (eds): Modern Trends in Cartography, Springer, 339–356.
<https://doi.org/10.1007/978-3-319-07926-4_26>
16. KINKELDEY, C., MACEACHREN, A.M., RIVEIRO, M., SCHIEWE, J. (2017): Evaluating the effect of visually represented geodata uncertainty on decision-making: systematic review, lessons learned, and recommendations. Cartography and Geographic Information Science, 44, 1, 1–21.
<https://doi.org/10.1080/15230406.2015.1089792>
17. KINKELDEY, C., SENARATNE, H. (2018). Representing Uncertainty. Be Geographic Information Science & Technology Body of Knowledge.
<https://doi.org/10.22224/gistbok/2018.2.3>
18. KUBÍČEK, P., ŠAŠINKA, Č., STACHOŇ, Z. (2014): Vybrané kognitivní aspekty vizualizace polohové nejistoty v geografických datech. Geografie, 119, 1, 67–90.
19. KUBÍČEK, P., ŠAŠINKA, Č., STACHOŇ, Z., ŠTĚRBA, Z., APELTAUER, J., URBÁNEK, T. (2017): Cartographic design and usability of visual variables for linear features. The Cartographic Journal, 54, 1, 91–102.
<https://doi.org/10.1080/00087041.2016.1168141>
20. LERMUSIAUX, P.F., CHIU, C.S., GAWARKIEWICZ, G., ABBOT, P., ROBINSON, A.R., MILLER, R.N., HALEY, P.J., LESLIE, W.G., MAJUMDAR, S.J., PANG, A., LEKIEN, F. (2006): Quantifying uncertainties in ocean predictions. Oceanography, 19, 1, 90–103.
<https://doi.org/10.5670/oceanog.2006.93>
21. LI, L. (2017): Spatial data uncertainty. Be Geographic Information Science & Technology Body of Knowledge, 4ᵗh Quarter 2017 Edition.
<https://doi.org/10.22224/gistbok/2017.4.4>
22. LONGLEY, P.A., GOODCHILD, M., MAGUIRE, D.J., RHIND, D.W. (2005): Geographic information systems and science: John Wiley & Sons.
23. MACEACHREN, A.M. (1995): How maps work : representation, visualization, and design. New York: Guilford Press.
24. MACEACHREN, A.M., ROBINSON, A., HOPPER, S., GARDNER, S., MURRAY, R., GAHEGAN, M., HETZLER, E. (2005): Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science, 32, 3, 139–160.
<https://doi.org/10.1559/1523040054738936>
25. MACEACHREN, A.M., ROTH, R.E., O’BRIEN, J., LI, B., SWINGLEY, D., GAHEGAN, M. (2012): Visual Semiotics & Uncertainty Visualization: An Empirical Study. Visualization and Computer Graphics, IEEE Transactions on, 18, 12, 2496–2505.
<https://doi.org/10.1109/TVCG.2012.279>
26. MACHAR, I., VOŽENÍLEK, V., KIRCHNER, K., VLČKOVÁ, V., BUČEK, A. (2017): Biogeographic model of climate conditions for vegetation zones in Czechia. Geografie, 122, 1, 64–82.
27. PANG, A. (2001): Visualizing uncertainty in geo-spatial data. Paper presented at the Workshop on the Intersections between Geospatial Information and Information Technology.
28. PANG, A.T., WITTENBRINK, C.M., LODHA, S.K. (1997): Approaches to uncertainty visualization. Be Visual Computer, 13, 8, 370–390.
<https://doi.org/10.1007/s003710050111>
29. PECHANEC, V., BURIAN, J., KILIANOVÁ, H. (2011): Geospatial analysis of the spatial conflicts of flood hazard in urban planning. Moravian Geographical Reports, 19, 1, 41–49.
30. POPELKA, S., DOLEŽALOVÁ, J., BEITLOVÁ, M. (2018): New features of scangraph: a tool for revealing participants’ strategy from eye-movement data. Paper presented at the Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications.
<https://doi.org/10.1145/3204493.3208334>
31. POTTER, K., ROSEN, P., JOHNSON, C.R. (2012): From quantification to visualization: A taxonomy of uncertainty visualization approaches Uncertainty Quantification in Scientific Computing, Springer, 226–249.
<https://doi.org/10.1007/978-3-642-32677-6_15>
32. SHI, W. (2010): Principles of modeling uncertainties in spatial data and spatial analyses. Boca Raton: CRC Press/Taylor & Francis.
<https://doi.org/10.1201/9781420059281>
33. VEREGIN, H. (1999): Data quality parameters. Geographical information systems, 1, 177–189.
34. WONG, P.C., SHEN, H.W., JOHNSON, C.R., CHEN, C., ROSS, R.B. (2012): The top 10 challenges in extreme-scale visual analytics. Computer Graphics and Applications, IEEE, 32, 4, 63–67.
<https://doi.org/10.1109/MCG.2012.87>
