User-Centric Time-Distance Representation of Road Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6292)


This paper presents a new algorithm for computing time-distance transformations of a road network based on modified multi-dimensional scaling. The algorithm is designed to perform on a real-world road network, and provides alternative visualisations for travel time cognition and route planning. Several extensions are explored, including user-centric and route-centric road map transformations. Our implementation of the algorithm can be applied to any locality where travel time road network data is available. Here, it is illustrated on road network data for a rural region in Ireland. Limitations of the proposed algorithm are examined, and potential solutions are discussed.


Road networks time-distance cartograms multi-dimensional scaling 


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  1. 1.
    Karlin, O.: Time travel (2005) (Web page),
  2. 2.
  3. 3.
    Böttger, J., Brandes, U., Deussen, O., Ziezold, H.: Map warping for the annotation of metro maps. IEEE Computer Graphics and Applications 28(5), 56–65 (2008)CrossRefGoogle Scholar
  4. 4.
    Spiekermann, K., Wegener, M.: The shrinking continent: new time-space maps of Europe. Environment and Planning B: Planning and Design 21, 653–673 (1994)CrossRefGoogle Scholar
  5. 5.
    Ahmed, N., Miller, H.: Time-space transformations of geographic space for exploring, analyzing and visualizing transportation systems. Journal of Transport Geography 15(1), 2–17 (2007)CrossRefGoogle Scholar
  6. 6.
    Shimizu, E., Inoue, R.: A new algorithm for distance cartogram construction. International Journal of Geographical Information Science 23(11), 1453–1470 (2009)CrossRefGoogle Scholar
  7. 7.
    Dorling, D.: Area Cartograms: Their Use and Creation. Geo Abstracts University of East Anglia, Norwich (1996)Google Scholar
  8. 8.
    Tobler, W.: Thirty five years of computer cartograms. Annals of the Association of American Geographers 94(1), 58–73 (2004)CrossRefGoogle Scholar
  9. 9.
    Gastner, M.T., Newman, M.: Diffusion-based method for producing density equalizing maps. Proceedings of the National Academy of Sciences of the United States of America 101(20), 7499–7504 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Bunge, W.: Theoretical geography. PhD thesis, University of Washington (1960)Google Scholar
  11. 11.
    Tobler, W.: Map transformation of geographic space. PhD thesis, University of Washington (1961)Google Scholar
  12. 12.
    Marchand, B.: Deformation of a transportation space. Annals of the Association of American Geographers 63(4), 507–522 (1973)CrossRefGoogle Scholar
  13. 13.
    Forer, P.: Space through time: a case study with NZ airlines. In: Cripps, E. (ed.) Space-time concepts in urban and regional models, Pion, London, pp. 22–45 (1974)Google Scholar
  14. 14.
    Kruskal, J.B., Wish, M.: Multidimensional scaling. In: Quantitative applications in the social sciences, Sage, Beverly Hills (1978)Google Scholar
  15. 15.
    Denain, J.C., Langlois, P.: Cartographie en anamorphose. Mappemonde 49(1), 16–19 (1998)Google Scholar
  16. 16.
    Yamamoto, D., Ozeki, S., Takahashi, N.: Focus+Glue+Context: an improved fisheye approach for web map services. In: 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, November 4-6 (2009)Google Scholar
  17. 17.
    Sarkar, M., Brown, M.H.: Graphical fisheye views. Commununications of the ACM 37(12), 73–84 (1994)CrossRefGoogle Scholar
  18. 18.
    Guerra, F., Boutoura, C.: An electronic lens on digital tourist city-maps. In: Mapping the 21st century: proceedings of the 20th International Cartographic Conference, Beijing, pp. 1151–1157 (2001)Google Scholar
  19. 19.
    Torgerson, W.S.: Multidimensional scaling: I. Theory and method. Psychometrika 17, 401–419 (1952)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Gower, J.C.: Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3–4), 325–338 (1966)zbMATHMathSciNetGoogle Scholar
  21. 21.
    Kruskal, J.B., Seery, J.B.: Designing network diagrams. In: Proceedings of the First General Conference on Social Graphics, pp. 22–50 (1980)Google Scholar
  22. 22.
    Brandes, U., Pich, C.: Eigensolver methods for progressive multidimensional scaling of large data. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 42–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Kruskal, J.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18(5), 401–409 (1969)CrossRefGoogle Scholar
  25. 25.
    McGee, V.E.: The multidimensional scaling of elastic distances. The British Journal of Mathematical and Statistical Psychology 19, 181–196 (1966)Google Scholar
  26. 26.
    de Leeuw, J.: Applications of convex analysis to multidimensional scaling. In: Barra, J., Brodeau, F., Romier, G., Van Cutsem, B. (eds.) Recent Developments in Statistics, pp. 133–146. North Holland Publishing Company, Amsterdam (1977)Google Scholar
  27. 27.
    Gansner, E.R., Koren, Y., North, S.C.: Graph drawing by stress majorization. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 239–250. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  28. 28.
    Borg, I., Groenen, P.J.F.: Modern multidimensional scaling: theory and applications. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  29. 29.
    Bengio, Y., Paiement, J.F., Vincent, P., Delalleau, O., Le Roux, N., Ouimet, M.: Out-of-sample extensions for lle, Isomap, MDS, eigenmaps, and spectral clustering. In: Advances in NIPS, pp. 177–184. MIT Press, Cambridge (2003)Google Scholar
  30. 30.
    Haykin, S.: Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.National Centre for GeocomputationNational University of IrelandMaynooth
  2. 2.Institute of GeographyUniversity of LausanneSwitzerland

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