1,221
Views
6
CrossRef citations to date
0
Altmetric
Research Articles

Evaluating the impact of visualization of risk upon emergency route-planning

ORCID Icon, , , ORCID Icon & ORCID Icon
Pages 1022-1050 | Received 14 May 2019, Accepted 02 Dec 2019, Published online: 12 Dec 2019

References

  • Aerts, J.C., Clarke, K.C., and Keuper, A.D., 2003. Testing popular visualization techniques for representing model uncertainty. Cartography and Geographic Information Science, 30 (3), 249–261. doi:10.1559/152304003100011180.
  • Andre, A. and Cutler, H., 1998. Displaying uncertainty in advanced navigation systems. Proceedings of the Human Factors and Ergonomics Society, 42 (1), 31–35. doi:10.1177/154193129804200108.
  • Ash, K., Schumann, R., and Bowser, G., 2014. Tornado warning trade-offs: evaluating choices for visually communicating risk. Weather, Climate and Society, 6 (1), 104–118. doi:10.1175/WCAS-D-13-00021.1.
  • Bardsley, N., et al., 2010. Experimental economics: rethinking the rules. Princeton: Princeton University Press.
  • Bertin, J., 1983. Semiology of graphics: diagrams, networks, maps. Madison: University of Wisconsin Press.
  • Bisantz, A., et al., 1999. Human performance and data fusion based decision aids. In: Proc. 2nd international conference on information Fusion (FUSION’99), 6–8 July. Sunnyvale, CA, 918–925.
  • Bjork, R., 1994. Memory and metamemory considerations in the training of human beings. In: J. Metcalfe and A. Shimamura, eds. Metacognition: knowing about knowing. Cambridge, MA: MIT Press, 185–206.
  • Boukhelifa, N., et al., 2012. Evaluating sketchy lines for the visualization of qualitative uncertainty. Technical report PR-7910, INRIA. doi:10.1094/PDIS-11-11-0999-PDN.
  • Brewer, C.A., 2019. ColorBrewer 2.0. [online] Available from: http://www.ColorBrewer.org [Accessed 14 Mar 2019].
  • Burston, J., Ware, D., and Tomlinson, R., 2015. The real-time needs of emergency managers for tropical cyclone storm tide forecasting: results of a participatory stakeholder engagement process. Natural Hazards, 78 (3), 1653–1668. doi:10.1007/s11069-015-1794-7.
  • Buttenfield, B., 1993. Representing data quality. Cartographica, 2 (2–3), 1–7. doi:10.3138/232H-6766-3723-5114.
  • Cao, Y., Boruff, B.J., and McNeill, I.M., 2016. Is a picture worth a thousand words? Evaluating the effectiveness of maps for delivering wildfire warning information. International Journal of Disaster Risk Reduction, 19, 179–196. doi:10.1016/j.ijdrr.2016.08.012
  • Cheong, L., et al., 2016. Evaluating the impact of visualization of wildfire hazard upon decision-making under uncertainty. International Journal of Geographical Information Science, 30 (7), 1377–1404. doi:10.1080/13658816.2015.1131829.
  • Cliburn, D., et al., 2002. Design and evaluation of a decision support system in a water balance application. Computers and Graphics, 26, 931–949. doi:10.1016/S0097-8493(02)00181-4.
  • Cox, J., House, D., and Lindell, M., 2013. Visualizing uncertainty in predicted hurricane tracks. International Journal for Uncertianty Quantification, 3 (2), 143–156. doi:10.1615/Int.J.UncertaintyQuantification.2012003966.
  • Davis, T. and Keller, C., 1997. Modelling and visualizing multiple spatial uncertainties. Computers and Geosciences, 23 (4), 397–408. doi:10.1016/S0098-3004(97)00012-5.
  • Edwards, L. and Nelson, E., 2012. Visualizing data certainty: A case study using graduated circle maps. Cartographic Perspectives, 38, 19–36.
  • Endsley, M., 1995. Toward a theory of situation awareness in dynamic systems. Human Factors, 37 (1), 32–64. doi:10.1518/001872095779049543.
  • Finger, R. and Bisantz, A., 2002. Utilizing graphical formats to convey uncertainty in a decision-making task. Theoretical Issues in Ergonomics Science, 3 (1), 1–25. doi:10.1080/14639220110110324.
  • Fraser, D., 2010. Cartography and geoinformation for early warning and emergency management. lecture notes in geoinformation and cartography. In: M. Konečný, S. Zlatanova, and T.L. Bandrova, eds. Geographic information and cartography for risk and crisis management: towards better solutions. Berlin: Springer, 319–334.
  • Frick, J. and Hegg, C., 2011. Can end-users’ flood management decision making be improved by information about forecast uncertainty? Atmospheric Research, 100 (2–3), 296–303. doi:10.1016/j.atmosres.2010.12.006.
  • Friedmannová, L., 2010. Designing map keys for crisis management on the regional operational and informational centre level: monitoring transport of dangerous goods via contextual visualisation. lecture notes in geoinformation and cartography. In: M. Konečný, S. Zlatanova, and T.L. Bandrova, eds. Geographic information and cartography for risk and crisis management: towards better solutions. Berlin: Springer, 425–437.
  • Fuhrmann, S., MacEachren, A., and Cai, G., 2008. Geoinformation technologies to support collaborative emergency management. In: H. Chen, et al., eds. Digital government: E-government research, case studies, and implementation. Berlin: Springer, 395–420.
  • Gärling, T. and Golledge, R.G., 2000. Spatial decision-making. In: R. Kitchin and S. Fre- Undschuh, eds. Cognitive mapping: past, present, and future. Psychology Press, chap. 4. Routledge, 44–65.
  • Goda, K. and Song, J., 2016. Uncertainty modeling and visualization for tsunami hazard and risk mapping: a case study for the 2011 Tohoku earthquake. Stochastic Environmental Research and Risk Assessment, 30 (8), 2271–2285. doi:10.1007/s00477-015-1146-x.
  • Greiner, B., 2015. Subject pool recruitment procedures: organizing experiments with ORSEE. Journal of the Economic Science Association, 1 (1), 114–125. doi:10.1007/s40881-015-0004-4.
  • Griffin, A., et al., 2014. Supporting planners’ work with uncertain demographic data. In: Workshop on visually-supported reasoning with uncertainty, GIScience 2014. Vienna, Austria.
  • Harris, P., et al., 2016. Modelling, interpreting and visualizing uncertainties for the North Wyke Farm Platform baseline field surveys. In: Proc. spatial accuracy 2016, Montpellier, France, 18–23.
  • He, G., 2011. A comparative study of color metaphors in English and Chinese. Theory and Practice in Language Studies, 1 (12), 1804–1808. doi:10.4304/tpls.1.12.1804-1808.
  • Healey, C.G., Booth, K.S., and Enns, J.T., 1996. High-speed visual estimation using preattentive processing. ACM Transactions on Computer-Human Interaction (TOCHI), 3 (2), 107–135. doi:10.1145/230562.230563.
  • Hofstra, H., et al., 2009. Multi-user tangible interfaces for effective decision-making in disaster management. In: S. Nayak and S. Zlatanova, eds. Remote sensing and GIS technologies for monitoring and prediction of disasters. Berlin: Springer, 243–266.
  • Howard, D. and MacEachren, A., 1996. Interface design for geographic visualization: tools for representing reliability. Cartography and Geographic Information Systems, 23 (2), 59–77. doi:10.1559/152304096782562109.
  • Hullman, J., et al., 2019. In pursuit of error: a survey of uncertainty visualization evaluation. IEEE Transactions on Visualization and Computer Graphics, 25 (1), 903–913. doi:10.1109/TVCG.2945.
  • Johannsen, I., Fabrikant, S.I., and Evers, M., 2018. How do texture and color communicate uncertainty in climate change map displays? In: A.G. Stephan Winter and M. Sester, eds. Proc. 10th international conference on geographic information science (GIScience 2018), Leibniz international proceedings in informatics. Dagstuhl, Germany: Schloss Dagstuhl – Leibniz Center for Informatics, 37:1–37:6.
  • Kardos, J., Moore, A., and Benwell, G., 2005. The visualisation of uncertainty for spatially referenced census data using hierarchical tessellations. Transactions in GIS, 9 (1), 19–34. doi:10.1111/j.1467-9671.2005.00203.x.
  • Kevany, M.J., 2005. Geo-information for disaster management: lessons from 9/11. In: P. van Oosterom, S. Zlatanova, and E.M. Fendel, eds. Geo-information for disaster management. Berlin: Springer, 443–464.
  • Kim, S., et al., 2007. Visual analytics on mobile devices for emergency response. In: Proc. IEEE symposium on visual analytics science and technology (VAST), Sacramento, CA, 35–42.
  • Kinkeldey, C., et al., 2015. 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. doi:10.1080/15230406.2015.1089792.
  • Kinkeldey, C., MacEachren, A., and Schiewe, J., 2014. How to assess visual communication of uncertainty? A systematic review of geospatial uncertainty visualisation user studies. The Cartographic Journal, 51 (4), 372–386. doi:10.1179/1743277414Y.0000000099.
  • Kirschenbaum, S., et al., 2013. Visualizing uncertainty: the impact on performance. Human Factors, 56 (3), 509–520. doi:10.1177/0018720813498093.
  • Kirschenbaum, S. and Arruda, J., 1994. Effects of graphic and verbal probability information on command decision making. Human Factors, 36 (3), 406–418. doi:10.1177/001872089403600302.
  • Leitner, M. and Buttenfield, B.P., 2000. Guidelines for the display of attribute certainty. Cartography and Geographic Information Science, 27 (1), 3–14. doi:10.1559/152304000783548037.
  • Lickiss, M., et al., 2017. Developing a quick guide on presenting data and uncertainty. Weather, 72 (9), 266–269. doi:10.1002/wea.2017.72.issue-9.
  • MacEachren, A., 1995. How maps work: representation, visualization and design. New York: Guilford Press.
  • MacEachren, A., et al., 2012. Visual semiotics and uncertainty visualization: an empirical study. IEEE Transactions on Visualization and Computer Graphics, 18 (12), 2496–2505. doi:10.1109/TVCG.2012.279.
  • MacEachren, A.M., 1992. Visualizing uncertain information. Cartographic Perspectives, 13 (Fall), 10–19. doi:10.14714/CP13.1000.
  • MacEachren, A.M., et al., 2011. SensePlace2: geoTwitter analytics support for situational awareness. In: Proc. IEEE conference on Visual Analytics Science and Technology (VAST), Providence, RI, 181–190.
  • MacEachren, A.M., Brewer, C.A., and Pickle, L.W., 1998. Visualizing georeferenced data: representing reliability of health statistics. Environment and Planning A, 30 (9), 1547–1561. doi:10.1068/a301547.
  • Mackinlay, J., 1986. Automating the design of graphical presentations of relational information. Acm Transactions On Graphics, 5 (2), 110–141. doi:10.1145/22949.22950.
  • Mason, J., Retchless, D., and Klippel, A., 2014. Dimensions of uncertainty: a visual classification of geospatial uncertainty research. In: Workshop visually-supported reasoning with uncertainty, in conjunction with GIScience 2014, Vienna, Australia.
  • McGranghan, M., 1993. A cartographic view of spatial data quality. Cartographica, 30 (2–3), 8–19. doi:10.3138/310V-0067-7570-6566.
  • Merrill, S., et al., 2019. Decision- making in livestock biosecurity practices amidst environmental and social uncertainty: evidence from an experimental game. PLoS ONE, 14 (4), e0214500. doi:10.1371/journal.pone.0214500.
  • Munzner, T., 2014. Visualization analysis and design. Boca Raton, FL: CRC Press.
  • Pang, A., 2001. Visualizing uncertainty in geo-spatial data. In: Proc. Workshop on the intersections between geospatial information and information technology, Washington, D.C.
  • Pang, A., Wittenbrink, C., and Lodha, S., 1997. Approaches to uncertainty visualization. The Visual Computer, 13, 370–390. doi:10.1007/s003710050111
  • Quispel, A. and Maes, A., 2014. Would you prefer pie or cupcakes? Preferences for data visualization design of professionals and laypeople in graphic design. Journal of Visual Languages, 25, 107–116. doi:10.1016/j.jvlc.2013.11.007
  • Rauschert, I., et al., 2002. Designing a human-centered, multimodal GIS interface to support emergency management. In: Proc. 10th ACM international symposium on advances in geographic information systems (ACMGIS) ACM, McLean, VA, 119–124.
  • Resch, B., Schmidt, D., and Blaschke, T., 2007. Enabling geographic situational awareness in emergency management. In: Proc. 2nd geospatial integration for public safety conference, New Orleans, LA, 15–17.
  • Robinson, A.C., et al., 2017. Geospatial big data and cartography: research challenges and opportunities for making maps that matter. International Journal of Cartography, 3 (sup1), 32–60. doi:10.1080/23729333.2016.1278151.
  • Robinson, A.C., Roth, R.E., and MacEachren, A.M., 2011. Understanding user needs for map symbol standards in emergency management. Journal of Homeland Security and Emergency Management, 8 (1). doi:10.2202/1547-7355.1811.
  • Scholz, R. and Lu, Y., 2014. Uncertainty in geographic data on bivariate maps: an examination of visualization preference and decision making. ISPRS International Journal of Geo-Information, 3 (4), 1180–1197. doi:10.3390/ijgi3041180.
  • Schweizer, D. and Goodchild, M., 1992. Data quality and chloropleth maps: an experiment with the use of color. In: Proc. GIS/LIS ’92 Washington, D.C.: ACSM & ASPRS, 686–689.
  • Seipel, S. and Lim, N.J., 2017. Color map design for visualization in flood risk assessment. International Journal of Geographical Information Science, 31 (11), 2286–2309. doi:10.1080/13658816.2017.1349318.
  • Senaratne, H., et al., 2014. Uncertainty visualization for crisis management in smart grid environments. In: Proc. Eighth international conference on geographic information science (GIScience 2014), Vienna, Austria.
  • Slocum, T., et al., 2008. Thematic cartography and geographic visualization. 3rd ed. Upper Saddle River, NJ: Prentice Hall.
  • Stachoň, Z., Šašinka, V., and Talhofer, V., 2010. Perceptions of various cartographic representations under specific conditions. lecture notes in geoinformation and cartography. In: M. Konečný, S. Zlatanova, and T.L. Bandrova, eds. Geographic information and cartography for risk and crisis management: towards better solutions. Berlin: Springer, 349–360.
  • Stevens, S., 1975. Psychophysics: introduction to its perceptual, neural, and social prospects. Hoboken, NJ: Wiley.
  • Tenneti, R. and Duffy, A., 2005. Identifying requirements for rendering in conceptual design. In: Proc. 15th international conference on engineering design (ICED), Melbourne, Australia, 712.
  • Tomaszewski, B., 2011. Situation awareness and virtual globes: applications for disaster management. Computers & Geosciences, 37 (1), 86–92. doi:10.1016/j.cageo.2010.03.009.
  • Tomaszewski, B., et al., 2011. Supporting geographically-aware web document foraging and sensemaking. Computers, Environment and Urban Systems, 35 (3), 192–207. doi:10.1016/j.compenvurbsys.2011.01.003.
  • Tomaszewski, B. and MacEachren, A., 2012. Geovisual analytics to support crisis management: information foraging for geo-historical context. Information Visualization, 11 (4), 339–359. doi:10.1177/1473871612456122.
  • Van Der Wel, F., Hootsmans, R., and Ormeling, F., 1994. Visualization of data quality. In: A. MacEachren and D. Fraser-Taylor, eds. Visualization in modern cartography. New York: Elsevier, 313–331.
  • Viard, T., Caumon, G., and Levy, B., 2011. Adjacent versus coincident representations of geospatial uncertainty: which promote better decisions? Computers and Geo- Sciences, 37 (4), 511–520. doi:10.1016/j.cageo.2010.08.004.
  • Virrantaus, K., Mäkelä, J., and Demšar, U., 2009. Supporting the development of shared situational awareness for civilian crisis management with geographic information Science: research plan. In: A. Krek, et al., eds. Urban and regional data management. Boca Raton, FL: CRC Press, 229–242.
  • Wilkening, J., 2009. User preferences for map-based decision making under time pressure. In: C. Davies, ed. Proceedings of the doctoral colloquium at the 9th international conference on spatial information theory (COSIT). L’Aber Wrac’h, France, 91–97.
  • Wood, J., et al., 2012. Sketchy rendering for information visualization. IEEE Transactions in Visualization and Computer Graphics, 18, 2749–2758. doi:10.1109/TVCG.2012.262.
  • Worboys, M. and Duckham, M., 2004. GIS: a computing perspective. London: CRC Press.
  • Yue, C.L., Castel, A.D., and Bjork, R.A., 2013. When disfluency is—and is not—a desirable difficulty: the influence of typeface clarity on metacognitive judgments and memory. Memory & Cognition, 41 (2), 229–241. doi:10.3758/s13421-012-0255-8.