GeoInformatica

, Volume 21, Issue 3, pp 599–618 | Cite as

How users perceive transparency in the 3D visualization of cadastre: testing its usability in an online questionnaire

Article

Abstract

Using transparency as a visual variable in 3D geo-visualization offers several advantages since it can encode underlying data and at the same time relieve the occlusion. However, the usability of transparency has yet to be tested with intended users and it might vary from one application domain to another or from one task to another. This research project addresses the usability of transparency in 3D cadastre visualization, more specifically whether it helps users delimit property units (administrative boundaries) with their physical counterparts (e.g., walls) in condominium situation. In this situation, three types of boundaries (simple physical, simple administrative, and linked boundary, which is both physical and administrative) are involved in visualization, and three different transparency levels are used to represent these types. Empirical tests are administered in the form of an online questionnaire for university students in law (notarial law) and land surveying. The results show that, in general, using three different transparency levels is preferable and efficient solution to help users demarcate property units with their physical counterparts, and 63% participants correctly achieved their visual tasks with this design. Also, the transparency settings influence user’s performance. For example, applying very high transparency to simple administrative boundaries as compared to simple physical boundaries improves user certainty. Another important discovery is that many university students in notarial law are inexperienced with 3D visualization, and such inexperience affects the viewer’s certitude when carrying out visual tasks.

Keywords

Transparency Visual variables 3D geo-visualization 3D cadastre Usability 

Notes

Acknowledgements

We express our gratitude to the Natural Sciences and Engineering Research Council of Canada for funding this research program (RGPIN-2009-240822).

References

  1. 1.
    Colby G, Scholl L (1991) Transparency and blur as selective cues for complex visual information. In: Electronic Imaging’91. International Society for Optics and Photonics, San Jose, CA, pp. 114–125Google Scholar
  2. 2.
    Cheung B (2011) Using transparency in visualization. Master thesis. University of AlbertaGoogle Scholar
  3. 3.
    Halik Ł (2012) The analysis of visual variables for use in the cartographic design of point symbols for mobile augmented reality applications. Geodesy and Cartography 61:19–30Google Scholar
  4. 4.
    MacEachren AM (1995) How maps work: representation, visualization, and design. Guilford PressGoogle Scholar
  5. 5.
    Correa CD, Chan Y-H, Ma K-L (2009) A framework for uncertainty-aware visual analytics. 2009 I.E. S Vis Anal. pp 51–58Google Scholar
  6. 6.
    D’Zmura M, Colantoni P, Knoblauch K, Laget B (1997) Color transparency. Perception 26:471–492CrossRefGoogle Scholar
  7. 7.
    Metelli F, Da Pos O, Cavedon A (1985) Balanced and unbalanced, complete and partial transparency. Percept Psychophys 38:354–366CrossRefGoogle Scholar
  8. 8.
    Elmqvist N (2007) Occlusion management in immersive and desktop 3d virtual environments : theory and evaluation. International Journal of Virtual Reality 6:1–13Google Scholar
  9. 9.
    Viola I, Kanitsar A, Groller M (2004) Importance-driven volume rendering. In: Proceedings of the conference on visualization’04. pp 139–146Google Scholar
  10. 10.
    Van Oosterom P, Stoter J, Fendel E (eds) (2001) Registration of properties in strata, international workshop on “3D cadastres.” International Federation of Surveyors, Delft, the NetherlandsGoogle Scholar
  11. 11.
    Van Oosterom P, Fendel E, Stoter J, Streilein A (eds) (2011) Proceedings 2nd international workshop on 3D Cadastres, November, Delft, the NetherlandsGoogle Scholar
  12. 12.
    Van Oosterom P, Guo R, Li, Ying, S, Angsüsser, S (eds) (2012) Proceedings 3rd international workshop 3D Cadastres: Developments and practices, October, Shenzhen, China, ISBN:978–87–92853-01-1 (published by International Federation of Surveyors).Google Scholar
  13. 13.
    Van Oosterom PJM, Fendel E (eds) (2014) Proceedings 4th international workshop on 3D Cadastres., November, Dubai, United Arab Emirates, ISBN 978–87–92853-20-5 (published by International Federation of Surveyors).Google Scholar
  14. 14.
    Stoter J (2004) 3D Cadastre. Ph.D. thesis. Delft University of TechnologyGoogle Scholar
  15. 15.
    Aien A, Rajabifard A, Kalantari M, Williamson I (2011) Aspects of 3D cadastre-A case study in Victoria. In: FIG working week. Marrakech, Morocco, 18–22 May 2011Google Scholar
  16. 16.
    Erba DA (2012) Application of 3D cadastres as a land policy tool. In: Land Lines, the quarterly Journal of the Lincoln Institute of Land Policy, 8–14 April 2012Google Scholar
  17. 17.
    Pouliot J, Roy T, Fouquet-Asselin G, Desgroseilliers J (2010) 3D cadastre in the province of Quebec: a first experiment for the construction of a volumetric representation. In: Kolbe, König, Nagel (eds) advances in 3D geo-information sciences, series: lecture notes in geoin- formation and cartography. Springer, Berlin, pp. 149–162Google Scholar
  18. 18.
    Van Oosterom P (2013) Research and development in 3D cadastres. Comput Environ Urban 40:1–6CrossRefGoogle Scholar
  19. 19.
    Pouliot J, Wang C (2014) Visualization, distribution and delivery of 3D parcels. Position paper 4, 4th international workshop on 3D Cadastres, November, Dubai, United Arab Emirates Availabe from: http://www.gdmc.nl/3dcadastres/literature/3Dcad_2014_41.pdf
  20. 20.
    Guo R, Li L, Ying S, Jung J, Heo J (2013) Developing a 3D cadastre for the administration of urban land use: a case study of Shenzhen, China. Comput Environ Urban 40:46–55CrossRefGoogle Scholar
  21. 21.
    Jeong D, Kim T, Nam D, Li HS, Cho HK (2011) A Review of 3D cadastre pilot project and the policy of 3D NSDI in the Republic of Korea. In: 2nd International Workshop on 3D Cadastres, 2011. pp 311–332Google Scholar
  22. 22.
    Karki S, Thompson R, Mcdougall K, Cumerfort N, Van Oosterom P (2011) ISO land administration domain model and LandXML in the development of digital survey plan lodgement for 3D cadastre in Australia. In: 2nd International Workshop on 3D Cadastres, 2011. pp 65–84Google Scholar
  23. 23.
    Shojaei D, Rajabifard A, Kalantari M, Bishop ID (2012) Development of a 3D ePlan / LandXML visualisation system in Australia. Proceedings of the 3rd International Workshop on 3D Cadastres: Developments and Practices. pp 273–288Google Scholar
  24. 24.
    Vandysheva N, Sapelnikov S, Van Oosterom P, De Vires M, Spiering B, Wouters R, Hoogeven A, Penkov V (2012) The 3D cadastre prototype and pilot in the Russian Federation. In: FIG working week 2012. Rome, Italy, pp. 6–10Google Scholar
  25. 25.
    Wang C, Pouliot J, Hubert F (2012) Visualization principles in 3D cadastre : a first assessment of visual variables. In: Proceedings of the 3rd International Workshop on 3D Cadastres: Developments and Practices. pp 309–324Google Scholar
  26. 26.
    Pouliot J, Wang C, Hubert F, Fuchs V (2014) Empirical assessment of the suitability of visual variables to achieve notarial tasks established from 3D condominium models. Innovations in 3D Geo-Information Sciences. Springer International Publishing. pp 195–210Google Scholar
  27. 27.
    Bertin J (1983) Semiology of graphics: diagrams, networks, maps. University of Wisconsin pressGoogle Scholar
  28. 28.
    Häberling C, Bär H, Hurni L (2008) Proposed cartographic design principles for 3D maps: a contribution to an extended cartographic theory. Cartographica: International Journal for Geographic Information and Geovisualization 43:175–188CrossRefGoogle Scholar
  29. 29.
    Carpendale M (2003) Considering visual variables as a basis for information visualisation Available from: https://cdn.mprog.nl/dataviz/excerpts/w2/Carpendale_Considering_Visual_Variables.pdf
  30. 30.
    Jobst M, Kyprianidis J, Döllner J (2008) Mechanisms on graphical core variables in the design of cartographic 3D city presentations. In: geospatial vision. Springer, Berlin Heidelberg, pp. 45–59Google Scholar
  31. 31.
    Roth RE, Woodruff AW, Johnson ZF (2010) Value-by-alpha maps: an alternative technique to the cartogram. Cartogr J 47:130–140CrossRefGoogle Scholar
  32. 32.
    Herbert G, Chen X (2014) A comparison of usefulness of 2D and 3D representations of urban planning. Cartogr Geogr Inf Sci 42:22–32CrossRefGoogle Scholar
  33. 33.
    Elmqvist N, Assarsson U, Tsigas P (2009) Dynamic transparency for 3D visualization: design and evaluation. Int J Virt Real 1(8):65–78Google Scholar
  34. 34.
    Chan M-Y, Wu Y, Mak W-H, Chen W, Qu H (2009) Perception-based transparency optimization for direct volume rendering. IEEE T Vis Comput Gr 15:1283–1290CrossRefGoogle Scholar
  35. 35.
    Singh M, Anderson BL (2002) Toward a perceptual theory of transparency. Psychol Rev 109:492–519CrossRefGoogle Scholar
  36. 36.
    Motoyoshi I (2010) Highlight-shading relationship as a cue for the perception of translucent and transparent materials. J Vis 10:6CrossRefGoogle Scholar
  37. 37.
    Hillstrom AP, Wakefield H, Scholey H (2013) The effect of transparency on recognition of overlapping objects. J Exp Psychol Appl 19:158–170CrossRefGoogle Scholar
  38. 38.
    Rogowitz B, Treinish L, Bryson S (1996) How not to lie with visualization. Comput Phys:1–16Google Scholar
  39. 39.
    Haklay M (2010) How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ Plann B 37:682–703CrossRefGoogle Scholar
  40. 40.
    Aien A, Kalantari M, Rajabifard A, Williamson I, Wallace J (2013) Towards integration of 3D legal and physical objects in cadastral data models. Land Use Policy 35:140–154CrossRefGoogle Scholar
  41. 41.
    Shojaei D, Kalantari M, Bishop I, Rajabifard A, Aien A (2013) Visualization requirements for 3D cadastral systems. Comput Environ Urban 41:39–54CrossRefGoogle Scholar
  42. 42.
    Green M (1998) Toward a perceptual science of multidimensional data visualization: Bertin and beyond. ERGO/GERO Human Factors Science:1–30Google Scholar
  43. 43.
    Wallach D, Scholz SC (2012) User-Centered Design: Why and how to put users first in software development. Software for People, Management for Professionals 11–39Google Scholar
  44. 44.
    Lazar J, Feng DJH, Hochheiser DH (2009) Research methods in human-computer interaction. WileyGoogle Scholar
  45. 45.
    ISO 9241–11 (1998) Ergonomic requirements for office work with visual display terminals (VDTs)-Part 11: Guidance on usability. The international organization for standardization (ISO)Google Scholar
  46. 46.
    Stone M, Bartram L (2008) Alpha, contrast and the perception of visual metadata. Color and Imaging ConferenceGoogle Scholar
  47. 47.
    Porter T, Duff T (1984) Compositing digital images. ACM SIGGRAPH Computer Graphics 18:253–259CrossRefGoogle Scholar
  48. 48.
    Wilkening J, Fabrikant S (2013) How users interact with a 3D geo-browser under time pressure. Cartogr Geogr Inf Sci 40:40–52CrossRefGoogle Scholar
  49. 49.
    Hegarty M, Canham M, Fabrikant S (2010) Thinking about the weather: how display salience and knowledge affect performance in a graphic inference task. Journal of experimental psychology. Learning, memory, and cognition 36:37–53CrossRefGoogle Scholar
  50. 50.
    Andrienko N, Andrienko G, Voss H, Bernardo F, Hipolito J, Kretchmer U (2002) Testing the usability of interactive maps in CommonGIS. Cartogr Geogr Inf Sci 29(4):325–342CrossRefGoogle Scholar
  51. 51.
    Bates D, Maechler M, Bolker B, Walker S (2013) lme4: linear mixed-effects models using Eigen and S4. R package versionGoogle Scholar
  52. 52.
    Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biometrical J 50:346–363CrossRefGoogle Scholar
  53. 53.
    Hosmer DW, Lemeshow S (2000) Applied logistic regression. WileyGoogle Scholar
  54. 54.
    Wulff SS (2007) SAS for mixed models. Am Stat 61(2):184–185CrossRefGoogle Scholar
  55. 55.
    Pinheiro J, Bates D, DebRoy S, Sarkar D, Core R (2007) Linear and nonlinear mixed effects models. R package version 3:57Google Scholar
  56. 56.
    Slinker BK, Glantz SA (2008) Statistical primer for cardiovascular research. World Wide Web-Internet And Web Information Systems pp:1732–1737Google Scholar
  57. 57.
    D’Zmura M, Rinner O, Gegenfurtner KR (2000) The colors seen behind transparent filters. Perception 29:911–926CrossRefGoogle Scholar
  58. 58.
    Fleming RW, Jakel F, Maloney LT (2011) Visual perception of thick transparent materials. Psychol Sci 22:812–820CrossRefGoogle Scholar
  59. 59.
    Anderson BL (2003) The role of occlusion in the perception of depth, lightness, and opacity. Psychol Rev 110:785–801CrossRefGoogle Scholar
  60. 60.
    Jennings BK, Miller GA (1990) On color transparency. Phys Lett B 236:209–213CrossRefGoogle Scholar
  61. 61.
    Anderson BL, Singh M, Meng J (2006) The perceived transmittance of inhomogeneous surfaces and media. Vis Res 46:1982–1995CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Resources and Environmental EngineeringAnhui UniversityHefeiChina
  2. 2.Department of Geomatics SciencesUniversité LavalQCCanada

Personalised recommendations