Mining Spatial Gradual Patterns: Application to Measurement of Potentially Avoidable Hospitalizations

  • Tu Ngo
  • Vera Georgescu
  • Anne LaurentEmail author
  • Thérèse Libourel
  • Grégoire Mercier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10706)


Gradual patterns aim at automatically extracting co-variations between variables of data sets in the form of “the more/the less” such as “the more experience, the higher salary”. This data mining method has been applied more and more in finding knowledge recently. However, gradual patterns are still not applicable on spatial data while such information have strong presence in many application domains. For instance, in our work we consider the issue of potentially avoidable hospitalizations. Their determinants have been studied to improve the quality, efficiency, and equity of health care delivery. Although the statistical methods such as regression method can find the associations between the increased potentially avoidable hospitalizations with its determinants such as lower density of ambulatory care nurses, there is still a challenge to identify how the geographical areas follow or not the tendencies. Therefore, in this paper, we propose to extend gradual patterns to the management of spatial data. Our work is twofold. First we propose a methodology for extracting gradual patterns at several hierarchical levels. In addition, we introduce a methodology for visualizing this knowledge. For this purpose, we rely on spatial maps for allowing decision makers to easily notice how the areas follow or not the gradual patterns. Our work is applied to the measure of the potentially avoidable hospitalizations to prove its interest.


Data mining Gradual patterns Spatial maps Cartography visualization Potentially avoidable hospitalizations 



We would like to thank University of Science and Technology of Hanoi (USTH) and the DIM department from the CHU of Montpellier for funding this work.


  1. 1.
    Mercier, G., Georgescu, V., Bousquet, J.: Geographic variation in potentially avoidable hospitalizations in France. Health Aff. 34, 836–843 (2015)CrossRefGoogle Scholar
  2. 2.
    Laurent, A., Lesot, M.-J., Rifqi, M.: GRAANK: exploiting rank correlations for extracting gradual itemsets. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS (LNAI), vol. 5822, pp. 382–393. Springer, Heidelberg (2009). CrossRefGoogle Scholar
  3. 3.
    Di-Jorio, L., Laurent, A., Teisseire, M.: Mining frequent gradual itemsets from large databases. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 297–308. Springer, Heidelberg (2009). CrossRefGoogle Scholar
  4. 4.
    Aryadinata, Y.S., Lin, Y., Barcellos, C., Laurent, A., Libourel, T.: Mining epidemiological dengue fever data from Brazil: a gradual pattern based geographical information system. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014. CCIS, vol. 443, pp. 414–423. Springer, Cham (2014). Google Scholar
  5. 5.
    Nelson, G.S.: Reporting healthcare data: understanding rates and adjustments. In: PharmaSUG 2014 Conference Proceedings, San Diego, United States (2014)Google Scholar
  6. 6.
    Goldberg, M., Coeuret-Pellicer, M., Ribet, C., Zins, M.: Epidemiological studies based on medical and administrative databases : a potential strength in France. Med. Sci. 28(4), 430–434 (2012)Google Scholar
  7. 7.
    McHugh, R., Roche, S., Bédard, Y.: Towards a SOLAP-based public participation GIS. J. Environ. Manage. 90(6), 2041–2054 (2008)CrossRefGoogle Scholar
  8. 8.
    Rivest, S., Bédard, Y., Proulx, M.-J., Nadeau, M., Hubert, F., Pastor, J.: SOLAP technology: merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data. J. Int. Soc. Photogrammetry Remote Sens. (ISPRS) 60(1), 17–33 (2005)CrossRefGoogle Scholar
  9. 9.
    Bernier, E., Gosselin, P., Badard, T., Bédard, Y.: Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application. Int. J. Health Geographics. 8(18) (2009)Google Scholar
  10. 10.
    Magnuson, L.: Data Visualization: A Guide to Visual Storytelling for Libraries. Rowman and Littlefield publishers, Maryland (2016)Google Scholar
  11. 11.
    Auget, J.-L., Balakrishnan, N., Mesbah, M., Molenberghs, G.: Advances in statistical methods for the health sciences: applications to cancer and AIDS studies, genome sequence analysis, and survival analysis. Statistics for Industry and Technology. Birkhuser, Basel (2007). CrossRefzbMATHGoogle Scholar
  12. 12.
    Ott, L.R., Longnecker, M.: An Introduction to Statistical Methods and Data Analysis. Duxbury, Australia (2001)Google Scholar
  13. 13.
    Zhang, L., Guo, Q., Jiao, L.: Design and implementation of decision-making support system for thematic map cartography. Int. Arch. Photogrammetry, Remote Sens. Spat. Inf. Sci. 37 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tu Ngo
    • 1
    • 2
  • Vera Georgescu
    • 2
  • Anne Laurent
    • 3
    Email author
  • Thérèse Libourel
    • 4
  • Grégoire Mercier
    • 2
  1. 1.Department of Information and Communication TechnologyUniversity of Science and Technology of HanoiHanoiVietnam
  2. 2.Economic Evaluation UnitUniversity Hospital of MontpellierMontpellierFrance
  3. 3.LIRMMUniversity of MontpellierMontpellierFrance
  4. 4.Espace-DevUniversity of MontpellierMontpellierFrance

Personalised recommendations