Advertisement

Abstract

Mathematical Morphology (MM) has been introduced in geographical sciences during the years 1970-1980. However it did not find the same echo in the geographer community according the areas of research. Unlike remote sensing where MM tools have been used as early as in the eighties and are nowadays widespread, in the research works resorting to spatial analysis and modelling, MM is much rarer. And yet morphological analyses exactly match the purpose of spatial analysis. This talk aims to demonstrate the relevance of MM in geography and more precisely in spatial analysis. The three applications proposed focus on socio-economic issues: urban zones of influence detection, regional differentiations analysis and spatial modelling. Finally, are highlighted and discussed the major shortcomings which hold up the spread of MM in geography, planning and geomatics.

Keywords

Geography GIS Morphology Modelling Spatial Analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Huff, D.L.: Defining and Estimating a Trade Area. Journal of Marketing 28, 34–38 (1964)CrossRefGoogle Scholar
  2. 2.
    Reilly, W.J.: The Law of Retail Gravitation. Knickerbrocker Press, New York (1931)Google Scholar
  3. 3.
    Frankhauser, P.: The fractal approach. A new tool for the spatial analysis of urban agglomerations. Population. Special issue New methodological Approaches in the Social Sciences, 205–240 (1998)Google Scholar
  4. 4.
    Genre-Grandpierre, C.: La desserte spatiale des réseaux de transport routier: une approche fractale. Flux 38, 56–68 (1999)CrossRefGoogle Scholar
  5. 5.
    Tannier, C., Thomas, I., Vuidel, G., Frankhauser, P.: A fractal approach to identifying urban boundaries. Geographical Analysis 43, 211–227 (2011)CrossRefGoogle Scholar
  6. 6.
    Destival, I.: Mathematical morphology applied to remote sensing. Acta Astronautica 13, 371–385 (1986)CrossRefGoogle Scholar
  7. 7.
    Gallice-Matti, C.: La télédétection pour l’analyse spatiale : application aux espaces périurbains de la région urbaine de Lyon. Lyon 3 University. Thesis (2005)Google Scholar
  8. 8.
    Voiron-Canicio, C.: Les départements de France redessinés. Mappemonde 4, 26–28 (1989)Google Scholar
  9. 9.
    Tobler, W.: A computer movie simulating urban growth in the Detroit region. Economic Geography 46(2), 234–240 (1970)CrossRefGoogle Scholar
  10. 10.
    Voiron-Canicio, C.: Analyse spatiale et analyse d’images. GIP Reclus, collection Espaces modes d’emploi, Montpellier (1995)Google Scholar
  11. 11.
    Voiron-Canicio, C., Doveri, E., Fusco, J.: Spatial archaeology using image analysis and mathematical morphology (2011), http://halshs.archives-ouvertes.fr/halshs-00778526
  12. 12.
    Maignant, G.: Dispersion de polluants et morphologie urbaine. L’Espace Géographique 2, 141–154 (2007)Google Scholar
  13. 13.
    Saint-Amand, P.: L’adéquation d’un système de transport aux systèmes territoriaux méditerranéens: pour une mobilité durable. Modélisations et aide à la décision. Thesis.University of Nice (2010), http://tel.archives-ouvertes.fr/tel-00565919
  14. 14.
    Liziard, S., Voiron-Canicio, C.: The Contribution of Mathematical Morphology in Spatial Analysis of Aggregated Data: Home-Building Evolution in the French Riviera during the Twentieth Century. In: 15th AGILE International Conference on Geographic Information Science, http://agile.gis.geo.tudresden.de/web/index.php/conference/proceedings/proceedings-2012
  15. 15.
    Voiron-Canicio, C.: Selecting places and detecting nested zones of influence by using image analysis. In: VIIth Coloquio de Geografia Cuantitativa, modelos y sistemas de informacion en geografica, Vitoria, pp. 348–356 (1996)Google Scholar
  16. 16.
    Brunet, R.: Atlas Permanent de la Région Languedoc Roussillon (1990)Google Scholar
  17. 17.
    Voiron-Canicio, C.: Predicting the Urban Spread Using Spatio-Morphological Models. In: Murgante, B., Borruso, G., Lapucci, A. (eds.) Geocomputation & Urban Planning. SCI, vol. 176, pp. 223–236. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Power, C., Simms, A., White, R.: Hierarchical Fuzzy Pattern Matching for the Regional Comparison of Land Use Maps. International Journal of GIS 15, 77–100 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christine Voiron-Canicio
    • 1
  1. 1.ESPACEUniversity of Nice – Sophia Antipolis - CNRSFrance

Personalised recommendations