Towards Urban Fabrics Characterization Based on Buildings Footprints

  • Rachid Hamaina
  • Thomas Leduc
  • Guillaume Moreau
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Urban fabric characterization is very useful in urban design, planning, modeling and simulation. It is traditionally considered as a descriptive task mainly based on visual inspection of urban plans. Cartographic databases and geographic information systems (GIS) capabilities make possible the analytical formalization of this issue. This paper proposes a renewed approach to characterize urban fabrics using buildings’ footprints data. This characterization method handles both architectural form and urban open space morphology since urban space can be intuitively and simply divided into built-up areas (buildings) and non-built-up areas (open spaces). First, we propose to build a mesh of the open space (a morphologic tessellation) and then we formalize relevant urban morphology properties and translate them into a set of indicators (using some common-used indispensable indicators and proposing a new formulation or generalization of a few others). This first step produces a highly dimensional data set for each footprint characterizing both the building and its surrounding open space. This data set is then reduced and classified using a spatial clustering process, the self-organizing maps in this case. Our method only requires buildings’ footprints as input data. It can be applied on huge datasets and is independent from urban contexts. The results show that the classification produced is more faithful to ground truth (highlighting the variety of urban morphologic structures) than traditional descriptive characterizations generally lacking open space properties.


Urban fabric Morphology Buildings Self-organizing maps 


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  1. Bação F., Lobo V., Painho M., 2005, “The self-organizing map, the Geo- SOM, and relevant variants for geosciences”, Computers and Geosciences, 31(2), 155-163.Google Scholar
  2. Benedict M L., 1979, “To take hold of space: isovists and isovist fields”, Environment and Planning B: Planning and Design, 6, 47-65.Google Scholar
  3. Berghauser-Pont I., Haupt P., 2007, “The Spacemate: density and the typomorphology of the urban fabric”, Urbanism laboratory for cities and regions: progress of research issues in urbanism.Google Scholar
  4. Boffet A., Serra S.R., 2001, “Identification of spatial structures within urban blocks for town characterization”, 20th International Cartographic Conference.Google Scholar
  5. Couclelis H., 1992, “People Manipulate Objects (but Cultivate Fields): Beyond the Raster-Vector Debate in GIS”. In Frank, A. U., Campari, I., and Formentini, U., editors, Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning, Lecture Notes in Computer Science, pages 65-77, Pisa, Italy. Springer.Google Scholar
  6. Fisher-Gewirtzman D., Wagner I. A., 2003, “Spatial openness as a practical metric for evaluating built-up environments”, Environment and Planning B: Planning and Design, 30(1), 37-49.Google Scholar
  7. Henriques R., Bação F., Lobo V., 2009, “GeoSOM Suite: A Tool for Spatial Clustering”, Computational Science and Its Applications: ICCSA.Google Scholar
  8. Hillier B., 1987, “La morphologie de l’espace urbain : l’évolution de l’approche syntaxique”, Architecture et Comportement, 3(3), 205-216.Google Scholar
  9. Kohonen T., 2001, “Self-Organizing maps”, third ed, Springer, Berlin-Heidelberg 501 pp.Google Scholar
  10. Krüger E.L., Minella F.O., Rasia F., 2011, “Impact of urban geometry on outdoor thermal comfort and air quality from field measurements in Curitiba, Brazil”, Building and Environment, 46, 621-634.Google Scholar
  11. Landsberg H.E., 1981, “The urban climate”, Academic Press (New York), 275p.Google Scholar
  12. Puissant A. Skupinski G., Lachiche N., Braud A., Perret J., 2010, “Classification des tissus urbains à partir de données vectorielles - application à Strasbourg”, Spatial Analysis and GEOmatics: SAGEO’10, 198-211.Google Scholar
  13. Souza R., Rodrigues D., Mendes J., 2003, “Sky view factors estimation using a 3D-GIS extension”, Eighth International IBPSA Conference.Google Scholar
  14. Stamps A. E., 2005, “Isovists, enclosure, and permeability theory”, Environment and Planning B: Planning and Design, 32(5), 735-762.Google Scholar
  15. Thomas I., Frankhauser P., Keersmaecker M.L., 2007, “Fractal dimension versus density of built-up surfaces in the periphery of Brussels”, Papers in Regional Science, 2(06), 287-308.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rachid Hamaina
    • 1
  • Thomas Leduc
    • 1
  • Guillaume Moreau
    • 1
  1. 1.Lunam Université, Ecole Centrale de Nantes, CERMA: Centre D’Etudes Et RechercheNantes Cedex 2France

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