Abstract
Imperfections, often called ‘uncertainties’, exist in almost every spatio-temporal dataset, especially in historical data. They are of different types (unreliability, inaccuracy…) and concern every data dimension (space, time and theme). Based on previous work, this article proposes a synthesis qualitative classification of imperfection types. This classification has been assessed with domain experts (hydrologists, geophysicians and GIScientists working in a railway company) during an experiment, that gave positive results towards the use of this classification. Participants were also asked to evaluate the seriousness of each imperfection type in an analysis context. This evaluation has allowed to associate a quantitative index to each imperfection type and to visualize a quantity of imperfection attached to each spatial object in a map.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This hypothesis of equal importance for each data dimension needs to be assessed with users. It is currently a work in progress.
References
Arnaud A (2009) Valorisation de l’information dédiée aux événements de territoires à risque. Une application cartographique et géovisualisation de la couronne grenobloise. Université Joseph Fourrier, Grenoble
Arnaud A, Davoine P-A (2011) Approche cartographique et géovisualisation pour la représentation de l’incertitude: Application à l’information dédiée aux risques naturels. Rev Int Géomatique 21:205–224. doi:10.3166/rig.21.205-224
Bodin X (2002) La représentation des incertitudes spatiales de la carte de localisation probable des avalanches
Buttenfield BP (1993) Representing data quality. Cartographica 30:1–7. doi:10.3138/232H-6766-3723-5114
Buttenfield BP (1988) Visualizing the quality of cartographic data. In: Third international geographical information systems symposium (GIS/LIS 88), San Antonio (USA)
Cliburn DC, Feddema JJ, Miller JR, Slocum TA (2002) Design and evaluation of a decision support system in a water balance application. Comput Graph 26:931–949. doi:10.1016/S0097-8493(02)00181-4
Griethe H, Schumann H (2006) The visualization of uncertain data: Methods and problems. In: Simulation and visualization, Magdeburg (Germany)
ISO/Technical Committee 211 (2013) ISO 19157: Information géographique - Qualité des données. http://www.iso.org/iso/fr/home/store/catalogue_tc/catalogue_detail.htm?csnumber=32575. Accessed 13 Jan 2015
Kinkeldey C, Maceachren AM, Riveiro M, Schiewe J (2015) Evaluating the effect of visually represented geodata uncertainty on decision-making: systematic review, lessons learned, and recommendations. Cartogr Geogr Inf Sci. doi:10.1080/15230406.2015.1089792
Lowell KE (1997) Outside-in, inside-out: two methods of generating spatial certainty maps. In: Second annual conference of GeoComputation, Dunedin (New Zealand)
MacEachren AM, Robinson A, Hopper S et al (2005) Visualizing geospatial information uncertainty: what we know and what we need to know. Cartogr Geogr Inf Sci 32:139–160. doi:10.1559/1523040054738936
MacEachren AM, Roth RE, O’Brien J et al (2012) Visual semiotics & uncertainty visualization: an empirical study. IEEE Trans Vis Comput Graph 18:2496–2505. doi:10.1109/TVCG.2012.279
Nardo M, Saisana M, Saltelli A et al (2008) Handbook on constructing composite indicators: methodology and user guide
O’Brien O, Cheshire J (2014) Mapping Geodemographic classification uncertainty: an exploration of visual techniques using compositing operations. In: Workshop “Visually-Supported Reasoning with Uncertainty”, GIScience 2014, Vienna (Austria)
Olston C, Mackinlay JD (2002) Visualizing data with bounded uncertainty. IEEE Symp Inf Vis 2002 INFOVIS 2002 1–8. doi:10.1109/INFVIS.2002.1173145
Pang AT (2008) Visualizing uncertainty in natural hazards. Risk Assess Model Decis Supp 14:261–294. doi:10.1007/978-3-540-71158-2_12
Plew B (2002) The nature of uncertainty in historical geographic information. Trans GIS 6(4):431–456
Pornon H (1992) Les SIG, mise en oeuvre et applications. Hermes Science Publications, Paris (France)
Potter K, Rosen P, Johnson CR (2012) From quantification to visualization: a taxonomy of uncertainty visualization approaches. In: Uncertainty quantification in scientific computing. Springer, pp 226–249
Saisana M, Tarantola S (2002) State-of-the-art report on current methodologies and practices for composite indicator development, Italy
Schneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualization. In: IEEE Workshop on visual languages’96, pp 336–343
Seccia G, Cunty C, Chesneau É, et al (2014) Évaluer des modes de représentation cartographique de l’incertitude: Exemple d’utilisation de méthodes des sciences cognitives. In: Sageo 2014, Grenoble (France), p 5
Skeels M, Lee B, Smith G, Robertson G (2008) Revealing uncertainty for information visualization. In: working conference on advanced visual interfaces—AVI ’08. ACM Press, Napoli (Italy), pp 376–379
Smets P (1997) Imperfect information: imprecision—uncertainty. In: Smets P, Motro A (eds) Uncertainty management in information systems. From needs to solutions. Kluwer Academic Publishers, Berlin, pp 225–254
Snoussi M, Gensel J, Davoine P-A (2012) Extending TimeML and SpatialML languages to handle imperfect spatio-temporal information in the context of natural hazards studies. In: Gensel J, Josselin D, Vandenbroucke D (eds) Proceedings of AGILE’2012 conference. Springer, Avignon (France), pp 117–122
Thomson J, Hetzler E, MacEachren AM et al (2005) A typology for visualizing uncertainty. In: Erbacher RF, Roberts JC, Grohn MT, Borner K (eds) SPIE, visualization and data analysis 2005. SPIE, San Jose (CA, USA), pp 146–157
Veregin H (1989) Error modelling for the map overlay operation. In: Goodchild MF, Gopal S (eds) Accuracy of spatial databases. Taylor and Francis, pp 3–19
Zoghlami A, De Runz C, Akdag H, Pargny D (2012) Through a fuzzy spatiotemporal information system for handling excavation data. In: Gensel J, Josselin D, Vandenbroucke D (eds) International Agile’2012 conference. Springer, Berlin, pp 179–196
Acknowledgments
We would like to thank SNCF Company for their implication in this research project. Many thanks to the twelve railway experts who took a little of their working time to participate in the study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Saint-Marc, C., Villanova-Oliver, M., Davoine, PA., Capoccioni, C.P., Chenier, D. (2016). Representation and Visualization of Imperfect Geohistorical Data About Natural Risks: A Qualitative Classification and Its Experimental Assessment. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-33783-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33782-1
Online ISBN: 978-3-319-33783-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)