Skip to main content

Anchoring Unsorted E-Sources About Heritage Artefacts in Space and Time

  • Chapter
  • First Online:
Digital Cultural Heritage

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10605))

  • 2068 Accesses

Abstract

Thanks to citizen-side contributions, heritage scientists can now quite often gather large amount of spatio-temporal data about heritage artefacts. In the context of minor heritage collections, which often slip through large-scale heritage programs, accessing such data sets may be a decisive turn in uncovering important clues, or significant relationships in and across collections. In other words, the “citizen science” paradigm seemingly opens a whole new range of opportunities at research level (e.g., enrichment of data, comparative analyses, multidisciplinary annotations) and for collection holders (e.g., networking, “intangible” museums).

Yet, due to the nature of such data sets (e.g., heterogeneity in the wording, in the precision, verifiability issues, contradictions), these opportunities also raise challenges, in particular when wanting to foster cross examinations by heritage scientists. The global objective of our research is to better weigh how the nature of citizen-side contributions can impact the way information can be recorded, formalized, and visualized. In this paper a clear focus is put on the space and time parameters: geo-visualization, and spatio-temporal data visualization. The paper introduces a series of open-source geo-visualization solutions that have been designed for use in the context of information sets harvested from citizen-side e-sources, and that help document minor heritage assets.

The results we present show that hybrid visualizations can act as a basis for comparative reasoning and analysis, but also that the core service we should manage to offer is definitely an infovis one: getting to understand (at last) what we really know (and ignore).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The communes are the lowest level of administrative division in the French Republic.

References

  1. Kienreich, W.: Information and knowledge visualisation: an oblique view. MIA J. 0(1), 7–17 (2006)

    Google Scholar 

  2. Card, S.: Information visualization. In: The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, pp. 509–543. Lawrence Erlbaum Assoc Inc. (2007). https://doi.org/10.1201/9781410615862.ch26

    Chapter  Google Scholar 

  3. Kääb, A., Huggel, C., Fischer, L., Guex, S., Paul, F., Roer, I., Salzmann, N., et al.: Remote sensing of glacier- and permafrost-related hazards in high mountains: an overview. Nat. Hazards Earth Syst. Sci. 5, 527–554 (2005). https://doi.org/10.5194/nhess-5-527-2005

    Article  Google Scholar 

  4. Pritchard, H., Murray, T., Luckman, A., Strozzi, T., Barr, S.: Glacier surge dynamics of Sortebræ, east Greenland, from synthetic aperture radar feature tracking. J. Geophys. Res. 110 (2005). https://doi.org/10.1029/2004jf000233

  5. Lang, O., Rabus, B.T., Dech, S.W.: Velocity map of the Thwaites Glacier catchment, West Antarctica. J. Glaciol. 50, 46–56 (2004). https://doi.org/10.3189/172756504781830268

    Article  Google Scholar 

  6. Star, C.: Jakobshavn Glacier Flow in the Year 2000. NASA Scientific Visualization Studio, SVS Image Server (2006)

    Google Scholar 

  7. Joughin, I., Howat, I.M., Fahnestock, M., Smith, B., Krabill, W., Alley, R.B., et al.: Continued evolution of Jakobshavn Isbrae following its rapid speedup. J. Geophys. Res. 113 (2008). https://doi.org/10.1029/2008jf001023

  8. Robert, S.: Le paysage visible de la Promenade des Anglais à Nice: essai d’une représentation cartographique dynamique. Mappemonde, No. 86 (2007)

    Google Scholar 

  9. Kobben, B., Becker, T., Blok, C.: Webservices for animated mapping: the TimeMapper prototype. In: Peterson, M. (ed.) Lecture Notes in Geoinformation and Cartography, pp. 205–217. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-27485-5_14

    Chapter  Google Scholar 

  10. Geertman, S., de Jong, T., Wessels, C.: Flowmap: a support tool for strategic network analysis. In: Geertman, S., Stillwell, J. (eds.) Planning Support Systems in Practice, pp. 155–175. Springer, Berlin (2003). https://doi.org/10.1007/978-3-540-24795-1_9

    Chapter  Google Scholar 

  11. Konjar, M., Boyandin, I., Lalanne, D., Lisec, A., Drobne, S.: Using flow maps to explore functional regions in Slovenia. In: 2nd International Conference on Information Society and Information Technologies - ISIT 2010 (2010)

    Google Scholar 

  12. Biadgilgn, D.M., Blok, C.A., Huisman, O.: Assessing the cartographic visualization of moving objects. Momona Ethiop. J. Sci. 3(1), 80–104 (2011). https://doi.org/10.4314/mejs.v3i1.63687

    Article  Google Scholar 

  13. Antoni, J.P., Klein, O., Moisy, S.: La discrétisation temporelle. Une méthode de structuration des données pour la cartographie dynamique. Cartes & Géomatique, Revue du Comité Français de Cartographie 213, 27–31 (2012)

    Google Scholar 

  14. Beaude, B., Guillemot, L.: Commuting scales. Cartographie dynamique d’accessibilité temporelle. Mappemonde; no. 105 (2012)

    Google Scholar 

  15. Boyandin, I., Bertini, E., Bak, P., Lalanne, D.: Flowstrates: an approach for visual exploration of temporal origin-destination data. In: Eurographics/IEEE Symposium on Visualization (EuroVis 2011), vol. 30(3), pp. 971–980 (2011). https://doi.org/10.1111/j.1467-8659.2011.01946.x

    Article  Google Scholar 

  16. Interactive mapping of American presidential political votes. http://dsl.richmond.edu/voting/interactive/

  17. Interactive library of regional population dynamics. http://stats.oecd.org/OECDregionalstatistics/#story=0

  18. Banos, A., Lacasa, J.: Spatio-temporal exploration of SARS epidemic. Cybergeo: Eur. J. Geogr. Systèmes, Modélisation, Géostatistiques, document 408 (2007). https://doi.org/10.4000/cybergeo.12803

  19. de Oliveira, M.G., de Souza, B.C.: GeoSTAT – a system for visualization, analysis and clustering of distributed spatiotemporal data. In: Proceedings XIII GEOINFO, 25–27 November 2012, Campos do Jordao, Brazil, pp. 108–119 (2012)

    Google Scholar 

  20. Kapler, T., Wright, W.: Geo time information visualization. Inf. Visual. 4(2), 136–146 (2005). https://doi.org/10.1109/infvis.2004.27

    Article  Google Scholar 

  21. Goovaerts, P.: Three-dimensional visualization, interactive analysis and contextual mapping of space-time Cancer data. In: 13th AGILE International Conference on Geographic Information Science (2010)

    Google Scholar 

  22. Thakur, S., Rhyne, T.-M.: Data Vases: 2D and 3D plots for visualizing multiple time series. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 929–938. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10520-3_89

    Chapter  Google Scholar 

  23. Bell, K.: Visualizing crime – a “Data Rose” Blooms. Dir. Mag. (2011)

    Google Scholar 

  24. Huang, G., et al.: Geovisualizing Data with Ring Maps. Esri ArcUser (2008)

    Google Scholar 

  25. Zhao, L., Forer, P., Harvey, A.S.: Multi-scale and multi-form visualisation of human movement patterns in the context of space, time and activity: from timeline to Ringmap. In: AGILE 2008 Conference, Girona, Spain (2008)

    Google Scholar 

  26. Tominski, C., Schumann, H., Andrienko, G., Andrienko, N.: Stacking-based visualization of trajectory attribute data. In: IEEE Transactions on Visualization and Computer Graphics 18, vol. 12, pp. 2565–2574 (2012). https://doi.org/10.1109/tvcg.2012.265

    Article  Google Scholar 

  27. Slingsby, A., Dykes, J., Wood, J.: Using treemaps for variable selection in spatio-temporal visualisation. Inf. Visual. 7, 210–224 (2008). https://doi.org/10.1057/palgrave.ivs.9500185

    Article  Google Scholar 

  28. The Growth of Newspapers across the U.S.: 1690-2011. http://web.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

  29. MyHistro/the Hundred Years’ war. http://www.myhistro.com/story/the-hundred-years-war/34325/1#!war-during-the-rule-of-charles-v-67299

  30. Visualizing emancipation. http://dsl.richmond.edu/emancipation

  31. World Heritage List interactive map. http://whc.unesco.org/en/interactive-map/

  32. Cultural Heritage Map of Turkey. http://turkiyekulturvarliklari.hrantdink.org/en/

  33. An interactive geo-spatial visualization tool for GLAM (Galleries, Libraries, Archives, Museums). http://glammap.net/

  34. Philippine Inventory of Cultural Properties and Historic Events. http://www.philippineheritagemap.org/map

  35. Blaise, J.-Y., Dudek, I.: Concentric time: enabling context + focus visual analysis of architectural changes. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 632–641. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21916-0_67

    Chapter  Google Scholar 

  36. Kauppinen, T., Mantegarib, G., Paakkarinena, P., Kuittinena, H., Hyvonena, E., Bandinic, S.: Determining relevance of imprecise temporal intervals for cultural heritage information retrieval. Int. J. Hum.-Comput. Stud. 68, 549–560 (2010). https://doi.org/10.1016/j.ijhcs.2010.03.002

    Article  Google Scholar 

  37. Blaise, J.Y., Dudek, I.: Can infovis tools support the analysis of spatio-temporal diffusion patterns in historic architecture? In: 40th Annual Conference of Computer Applications and Quantitative Methods in Archaeology. CAA Series Computer Applications and Quantitative Methods in Archaeology, March 2013, pp. 367–378. Amsterdam University Press, Southampton (2013)

    Google Scholar 

  38. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings IEEE Symposium on Visual Languages, pp. 336–343 (1996). https://doi.org/10.1016/b978-155860915-0/50046-9

  39. Leaflet open-source JavaScript library for mobile-friendly interactive maps. http://leafletjs.com/

Download references

Acknowledgements

This research is funded by the région Provence-Alpes-Côte d’Azur regional authorities, under the program entitled Territographie (www.map.cnrs.fr/territographie), conducted in co-operation with MuCEM (Museum of European and Mediterranean Civilizations). The authors are indebted especially to Édouard de Laubrie from MuCEM for his continuous support and collaboration.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gamze Saygi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Saygi, G., Blaise, JY., Dudek, I. (2018). Anchoring Unsorted E-Sources About Heritage Artefacts in Space and Time. In: Ioannides, M. (eds) Digital Cultural Heritage. Lecture Notes in Computer Science(), vol 10605. Springer, Cham. https://doi.org/10.1007/978-3-319-75826-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75826-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75825-1

  • Online ISBN: 978-3-319-75826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics