Assessing the cognition of movement trajectory visualizations: interpreting speed and direction

References

• Ahearn, S. C., Dodge, S., Simcharoen, A., Xavier, G., & Smith, J. L. D. (2017). A context- sensitive correlated random walk: A new simulation model for movement. International Journal of Geographical Information Science, 31(5), 867–883. https://doi.org/10.1080/13658816.2016.1224887
   Web of Science ®Google Scholar
• Battersby, S. E., & Goldsberry, K. P. (2010). Considerations in design of transition behaviors for dynamic thematic maps. Cartographic Perspectives, 65, 16–32. https://doi.org/10.14714/CP65.127
   Google Scholar
• Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). Quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. https://doi.org/10.21105/joss.00774
   Google Scholar
• Bertin, J. (1983). Semiology of graphics: Diagrams, networks, maps. University of Wisconsin Press. ( Original work published in 1967)
   Google Scholar
• Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
   PubMed Web of Science ®Google Scholar
• Coluccia, E., & Louse, G. (2004). Gender differences in spatial orientation: A review. Journal of Environmental Psychology, 24(3), 329–340. https://doi.org/10.1016/j.jenvp.2004.08.006
   Web of Science ®Google Scholar
• Dahmani, L., & Bohbot, V. D. (2020). Habitual use of GPS negatively impacts spatial memory during self-guided navigation. Scientific reports, 10, 1–14. https://doi.org/10.1038/s41598-020-62877-0
   Google Scholar
• Davies, C., Fabrikant, S. I., & Hegarty, M. (2015). Toward empirically verified cartographic displays. In R. R. Hoffman, P. A. Hancock, M. W. Scerbo, R. Parasuraman, & J. L. Szalma (Eds.), The Cambridge handbook of applied perception research (pp. 711–730). Cambridge University Press.
   Google Scholar
• Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D. & Weibel, R. (2015). Analysis and visualisation of movement: An interdisciplinary review. Movement Ecology, 3(1), 1–24. https://doi.org/10.1186/s40462-015-0032-y
   PubMedGoogle Scholar
• DiBiase, D., MacEachren, A. M., Krygier, J. B., & Reeves, C. (1992). Animation and the role of map design in scientific visualization. Cartography and Geographic Information Systems, 19(4), 201–214. https://doi.org/10.1559/152304092783721295
   Web of Science ®Google Scholar
• Dodge, S. (2021). A data science framework for movement. Geographical Analysis, 53, 92–112. https://doi.org/10.1111/gean.12212
   Web of Science ®Google Scholar
• Dodge, S., & Noi, E. (2021). Mapping trajectories and flows: Facilitating a human-centered approach to movement data analytics. Cartography and Geographic Information Science, 48(4), 353–375. https://doi.org/10.1080/15230406.2021.1913763
   Web of Science ®Google Scholar
• Dodge, S., Toka, M., & Bae, C. J. (2021). DynamoVis 1.0: An exploratory data visualization software for mapping movement in relation to internal and external factors. Movement Ecology, 9(1), 55. https://doi.org/10.1186/s40462-021-00291-5
   PubMed Web of Science ®Google Scholar
• Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3–4), 240–252. https://doi.org/10.1057/PALGRAVE.IVS.9500182
   Web of Science ®Google Scholar
• Dong, W., Wang, S., Chen, Y., & Meng, L. (2018). Using eye tracking to evaluate the usability of flow maps. ISPRS International Journal of Geo-Information, 7(7), 281. https://doi.org/10.3390/ijgi7070281
   Web of Science ®Google Scholar
• Fabrikant, S. I., Hespanha, S. R., & Hegarty, M. (2010). Cognitively inspired and perceptually salient graphic displays for efficient spatial inference making. Annals of the Association of American Geographers, 100(1), 13–29. https://doi.org/10.1080/00045600903362378
   Web of Science ®Google Scholar
• Fairbairn, D., Andrienko, G., Andrienko, N., Buziek, G., & Dykes, J. (2001). Representation and its relationship with cartographic visualization. Cartography and Geographic Information Science, 28(1), 13–28. https://doi.org/10.1559/152304001782174005
   Google Scholar
• Fish, C., Goldsberry, K. P., & Battersby, S. (2011). Change blindness in animated choropleth maps: An empirical study. Cartography and Geographic Information Science, 38(4), 350–362. https://doi.org/10.1559/15230406384350
   Web of Science ®Google Scholar
• Frank, A. U. (1998). Different types of 'times' in GIS. In M. J. Egenhofer & R. G. Golledge (Eds.), Spatial and Temporal Reasoning in GIS (pp. 40–61). Oxford University Press.
   Google Scholar
• Fukaya, S., & Misue, K. (2018). Visualization techniques representing effects of coordination of vessels’ movements. In 22nd International Conference Information Visualisation (Vol. IV, pp. 12–18). IEEE Computer Society.
   Google Scholar
• Goldsberry, K., & Battersby, S. (2009). Issues of change detection in animated choropleth maps. Cartographica: The International Journal for Geographic Information and Geovisualization, 44(3), 201–215. https://doi.org/10.3138/carto.44.3.201
   Google Scholar
• Graser, A., Widhalm, P., & Dragaschnig, M. (2020). The M3 massive movement model: A distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science, 34(12), 2517–2540. https://doi.org/10.1080/13658816.2020.1776293
   Web of Science ®Google Scholar
• Griffin, A. L., MacEachren, A. M., Hardisty, F., Steiner, E., & Li, B. (2006). A comparison of animated maps with static small-multiple maps for visually identifying space-time clusters. Annals of the Association of American Geographers, 96(4), 740–753. https://doi.org/10.1111/j.1467-8306.2006.00514.x
   Web of Science ®Google Scholar
• Griffin, A. L., White, T., Fish, C., Tomio, B., Huang, H., Sluter, C. R., Bravo, J. V. M., Fabrikant, S. I., Bleisch, S., Yamada, M. & Picanço, P. (2017). Designing across map use contexts: A research agenda. International Journal of Cartography, 3(sup1), 90–114. https://doi.org/10.1080/23729333.2017.1315988
   Google Scholar
• Gu, Y., Kraak, M.-J., & Engelhardt, Y. (2018). Revisiting flow maps: A classification and a 3D alternative to visual clutter. Proceedings of the ICA, 1, 1–7. https://doi.org/10.5194/ica-proc-1-51-2018
   Google Scholar
• Harrower, M. (2007). The cognitive limits of animated maps. Cartographica: The International Journal for Geographic Information and Geovisualization, 42(4), 349–357. https://doi.org/10.3138/carto.42.4.349
   Google Scholar
• Harrower, M., & Fabrikant, S. (2008). The role of map animation for geographic visualization. In M. Dodge, M. McDerby, & M. Turner (Eds.), Geographic visualization (pp. 49–65). Wiley.
   Google Scholar
• Hegarty, M., Montello, D. R., Richardson, A. E., Ishikawa, T., & Lovelace, K. (2006). Spatial abilities at different scales: Individual differences in aptitude-test performance and spatial- layout learning. Intelligence, 34(2), 151–176. https://doi.org/10.1016/j.intell.2005.09.005
   Web of Science ®Google Scholar
• Hegarty, M., Richardson, A. E., Montello, D. R., Lovelace, K., & Subbiah, I. (2002). Development of a self-report measure of environmental spatial ability. Intelligence, 30(5), 425–447. https://doi.org/10.1016/S0160-2896(02)00116-2
   Web of Science ®Google Scholar
• Hegarty, M., Smallman, H. S., & Stull, A. T. (2012). Choosing and using geospatial displays: Effects of design on performance and metacognition. Journal of Experimental Psychology Applied, 18(1), 1–17. https://doi.org/10.1037/a0026625
   PubMed Web of Science ®Google Scholar
• Jenny, B., Stephen, D. M., Muehlenhaus, I., Marston, B. E., Sharma, R., Zhang, E., & Jenny, H. (2018). Design principles for origin-destination flow maps. Cartography and Geographic Information Science, 45(1), 62–75. https://doi.org/10.1080/15230406.2016.1262280
   Web of Science ®Google Scholar
• Kraak, M. J. (2014). Mapping time: Illustrated by Minard’s map of Napoleon’s Russian Campaign of 1812. Esri Press.
   Google Scholar
• Laube, P., van Kreveld, M., & Imfeld, S. (2005). Finding REMO — Detecting relative motion patterns in geospatial lifelines. In Developments in spatial data handling (pp. 201–215). Springer.
   Google Scholar
• Lautenschütz, A. -K. (2012). Map readers’ assessment of path elements and context to identify movement behaviour in visualisations. The Cartographic Journal, 49(4), 337–349. https://doi.org/10.1179/1743277412Y.0000000029
   Web of Science ®Google Scholar
• MacEachren, A. (1995). How Maps Work: Representation, Visualization and Design. Guilford Press.
   Google Scholar
• Mayer, R. E., Hegarty, M., Mayer, S., & Campbell, J. (2005). When static media promote active learning: Annotated illustrations versus narrated animations in multimedia instruction. Journal of Experimental Psychology Applied, 11(4), 256–265. https://doi.org/10.1037/1076-898X.11.4.256
   PubMed Web of Science ®Google Scholar
• Miller, H. J. (2005). A measurement theory for time geography. Geographical Analysis, 37(1), 17–45. https://doi.org/10.1111/j.1538-4632.2005.00575.x
   Web of Science ®Google Scholar
• Montello, D. R., Fabrikant, S. I., & Davies, C. (2018). Cognitive perspectives on cartography and other geographic information visualizations. In D. R. Montello (Ed.), Handbook of Behavioral and Cognitive Geography (pp. 177–196). Edward Elgar Publishing.
   Google Scholar
• Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052–19059. https://doi.org/10.1073/pnas.0800375105
   PubMed Web of Science ®Google Scholar
• Noi, E., Rudolph, A., & Dodge, S. (2022). Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: A multi-source analytical framework. International Journal of Geographical Information Science, 36(3), 585–616. https://doi.org/10.1080/13658816.2021.2005796
   Web of Science ®Google Scholar
• Padilla, L. M., Creem-Regehr, S. H., Hegarty, M., & Stefanucci, J. K. (2018). Decision making with visualizations: A cognitive framework across disciplines. Cognitive Research: Principles and Implications, 3(1–25), 1. https://doi.org/10.1186/s41235-018-0120-9
   PubMedGoogle Scholar
• Pezanowski, S., Mitra, P., & MacEachren, A. M. (2022). Exploring descriptions of movement through geovisual analytics. KN - Journal of Cartography and Geographic Information, 72(1), 5–27. https://doi.org/10.1007/s42489-022-00098-3
   PubMedGoogle Scholar
• Roth, R. E. (2017). Visual variables. In D. Richardson, N. Castree, M. F. Goodchild, A. Kobayashi, W. Liu, & R. A. Marston (Eds.), International encyclopedia of geography: People, the earth, environment and technology (pp. 1–11). Wiley.
   Google Scholar
• Roth, R. E., Çöltekin, A., Delazari, L., Filho, H. F., Griffin, A., Hall, A., Korpi J, Lokka I, Mendonça A, Ooms K., & van Elzakker, C. P. (2017). User studies in cartography: Opportunities for empirical research on interactive maps and visualizations. International Journal of Cartography, 3(sup1), 61–89. https://doi.org/10.1080/23729333.2017.1288534
   Google Scholar
• Tufte, E. R. (2001). The visual display of quantitative information. Graphics Press.
   Google Scholar
• Ware, C. (2013a). Appendix C - the perceptual evaluation of visualization techniques and systems. In C. Ware (Ed.), Information visualization (3rd ed., pp. 431–443). Morgan Kaufmann.
   Google Scholar
• Ware, C. (2013b). Chapter nine - Images, narrative, and gestures for explanation. In C. Ware (Ed.), Information Visualization (3rd ed., pp. 325–343). Morgan Kaufmann.
   Google Scholar
• Ware, C. (2013c). Chapter one - foundations for an applied science of data visualization. In C. Ware (Ed.), Information visualization (3rd ed., pp. 1–30). Morgan Kaufmann.
   Google Scholar
• White, T. (2017). Symbolization and the visual variables. In J. P. Wilson (Ed.), The Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2017 Edition).
   Google Scholar
• Yuan, Y., & Raubal, M. (2012). Extracting dynamic urban mobility patterns from mobile phone data. GIScience 2012, 7478, 354–367.
   Google Scholar