Abstract
Urban fabric is a fundamental small-scale component of urban form with important relations to social, economic or environmental phenomena. It is the result of interplay between buildings, parcels and street segments. Quantitative analysis of urban fabric on a large scale has often privileged selected morphological aspects, linked to micro-climatic or energy consumption issues. The planning approach has privileged aerial rather than pedestrian point of view. This paper proposes a methodology for the recognition and characterization of urban fabric taking a different stance. After the definition of a new network-based partition of urban space based on the pedestrian point of view, we describe an innovative computational method: firstly, urban fabric is broken down in its components, and shape-perception indicators are computed through geoprocessing techniques. Secondly, spatial patterns on the street network are identified with geostatistical analysis. Finally, Bayesian clustering is carried out for the re-composition, identification of urban fabric types and qualification of sub-spaces within the city.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hillier, B.: Space is the Machine. Cambridge University Press, Cambridge (1998)
Naik, N., et al.: Do people shape cities, or do cities shape people? The co-evolution of physical, social and economic change in five major cities. NBER Working Paper No. 21620 (2015)
Berghauser Pont, M.Y., Haupt, P.: SPACEMATRIX, Space, Density and Urban Form. NAi Publishers, Rotterdam (2010)
Thomas, I., et al.: Fractal dimension versus density of built-up surfaces in the periphery of Brussels. Papers Reg. Sci. 86, 287–308 (2007)
Porta, S., et al.: The network analysis of urban streets: a primal approach. Environ. Plann. B Plann. Des. 33(5), 705–725 (2006)
Conzen, M.R.G.: Alnwick, Northumberland: A Study in Town-Plan Analysis, vol. 27. Institute of British Geographers Publication, George Philip, London (1960)
Caniggia, G., Maffei, G.: Lettura dell’edilizia di base. Firenze, Alinea (1979)
Puissant, A., et al.: Classification des tissus urbains à partir de données vectorielles – Application à Strasbourg. In: SAGEO 2010 Proceedings, Toulouse, pp. 198–211 (2010)
Bernabé, A., et al.: Classification automatique des tissus urbains par la méthode des nuées dynamiques. 31e Rencontres AUGC, Cachan, France (2013). http://augc2013.ens-cachan.fr/Data/Articles/Contribution1150.pdf
Giannopoulou, M., et al.: Using GIS to record and analyse historical urban areas. J. Land Use Mob. Environ. 4, 43–47 (2014). Tema
Panerai, P., et al.: Forms urbains: de l’îlot à la barre, Ed. Parenthèses, Marseilles (1977)
Lynch, K.: The Image of the City. MIT Press, Cambridge (1960)
Graff, P.: Une ville d’exception. Nice dans l’effervescence du 20e siècle. Serre, Nice (2013)
Fusco, G.: Beyond the built-up form/ mobility relationship: spatial affordance and lifestyles. Comput. Environ. Urban Syst. 60, 50–66 (2016)
Long, N., Kergomard, C.: Classification morphologique du tissu urbain pour des applications climatologiques. Le cas de Marseille. Revue Int. de Géomatique 15(14), 487–512 (2005)
Hamaina, R., Leduc, T., Moreau, G.: A new method to characterize density adapted to a coarse city model. In: Popovich, V., Claramunt, C., Schrenk, M., Korolenko, K. (eds.) Information Fusion and Geographic Information Systems (IF AND GIS 2013). LNGC, pp. 249–263. Springer, Heidelberg (2014). doi:10.1007/978-3-642-31833-7_16
Okabe, A., Sugihara, K.: Spatial Analysis along Networks: Statistical and Computational Methods. John Wiley & Sons, Ltd., Chichester (2012)
Borie, A., Denieul, F.: Methode d’analyse morphologique des tissus urbains traditionnels, UNESCO (1984). http://unesdoc.unesco.org/images/0006/000623/062310fb.pdf
Lévy, A.: Urban morphology and the problem of the modern urban fabric: some questions for research. Urban Morphol. 3(2), 79–85 (1999)
Diday, E.: Une nouvelle méthode en classification automatique et reconnaissance des formes la méthode des nuées dynamiques. Revue de Stat. Appliquée 19(2), 19–33 (1971)
Gil, J., Beirão, J.N., Montenegro, N., Duarte, J.: On the discovery of urban typologies: data mining the many dimensions of urban form. Urban Morphol. 16(1), 27–40 (2012)
Anselin, L.: Local indicators of spatial association. Geog. Anal. 27(2), 93–115 (1995)
Yamada, I., Thill, J.C.: Local indicators of network-constrained clusters in spatial patterns represented by a link attribute. Ann. Ass. Am. Geog. 100(2), 269–285 (2010)
Araldi, A., Fusco, G.: Urban form from the pedestrian point of view: spatial patterns on a street network. In: Proceedings of INPUT 2016, INPUT 2016, Turin, Italy, 14th–15th September 2016, pp. 32–37 (2016)
Fusco, G., Araldi, A.: Urban fabric with Bayesian LINCS: empirical evidences from the French Riviera. In: SAGEO 2016, Nice, France, 7th–8th December 2016
Assunção, R.M., Reis, E.A.: A new proposal to adjust Moran’s I for population density. Stat. Med. 18, 2147–2162 (1999)
Fusco, G., Perez, J.: Spatial analysis of the Indian subcontinent: the complexity investigated through neural networks. In: Proceedings of CUPUM 2015, CUPUM 2015, Cambridge, MA, United States, July 2015, vol. 287, pp. 1–20 (2015)
Acknowledgements
This research was carried out thanks to a research grant of the Nice-Côte d’Azur Chamber of Commerce and Industry (CIFRE agreement with UMR ESPACE).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Araldi, A., Fusco, G. (2017). Decomposing and Recomposing Urban Fabric: The City from the Pedestrian Point of View. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-62401-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62400-6
Online ISBN: 978-3-319-62401-3
eBook Packages: Computer ScienceComputer Science (R0)