Informace o publikaci
3D bivariate visualizations in immersive virtual reality (IVR): the impact of map literacy and visualization method on user performance
| Autoři | |
|---|---|
| Rok publikování | 2026 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Cartography and Geographic Information Science |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://www.tandfonline.com/doi/full/10.1080/15230406.2026.2652392#abstract |
| Doi | https://doi.org/10.1080/15230406.2026.2652392 |
| Klíčová slova | bar graphs; cartographic visualization; Chernoff faces; geovisualization; immersive virtual environment; virtual reality |
| Popis | This study investigates the impact of two bivariate visualization methods–extrinsic (bar graphs) and intrinsic (Chernoff faces)–and the role of map literacy on user performance in value identification tasks within immersive virtual reality (IVR). A between-subject experiment (n = 126) assessed response correctness and time across two groups with differing presumed map literacy: geographers and laypeople. Supportive, though non-conclusive, eye tracking data (dwell times) were also collected to explore cognitive differences. Building on prior research with 2D stimuli, this study extends the investigation into a 3D IVR setting. Participants from diverse backgrounds (cartography, law, arts, etc.) were presented with 3D bivariate visualizations and tasked with identifying single/combined values of two variables. A PICO Neo3 Pro Eye VR headset with integrated eye tracking was used. Results indicate that Chernoff faces were less effective, yielding lower correctness, longer response times, and greater reliance on the legend, regardless of map literacy. Contrary to expectations, higher map literacy did not improve performance. Task complexity affected the methods differently: response time decreased for Chernoff faces but increased for bar graphs when going from one to two variables. Findings encourage further research with stronger map literacy assessment, varied visualizations, and refined eye tracking. |
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