Skip to main content
SpringerLink
Log in

Multiple Fabric Assessment: Focus on Method Versatility and Flexibility

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10962))

Included in the following conference series:

Abstract

Metropolitan regions are very complex spaces for geographical analysis, above all due to their strong heterogeneity at the intra-urban level. This paper presents the progresses made by Multiple Fabric Assessment (MFA), a method specifically conceived for describing urban fabrics from the pedestrian perspective. To sum up, standard spatial units are first defined (Proximity Bands) and specific indicators are calculated at this level. Then, patterns amongst space are identified and clustered. The application of MFA method to new case studies (Marseille, Osaka, Rio de Janeiro and Brussels) has brought to highlight several peculiarities related to data availability, intrinsic urban space characteristics and aim of application. This paper collects the experiences gathered from these new case studies, highlighting key aspects that academics and practitioners should deal with, when using MFA. Our results show a versatile and flexible method, able to be adapt itself to any case study if not limited by data availability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In Environmental Psychology literature, the individual perception of urban spaces is influenced by the physical features, the urban design qualities, the individual reactions and their combinations [4]. The first aspect is the most objective one and is easily measurable through geoprocessing algorithms. The others lack coverage, data availability and their subjective natures make them hard to implement.

  2. 2.

    The setback is considered here as the distance between the street edge and the building footprint. If a more detailed layer is available (3D), setback generated by porches, arcades, and storefront could be calculated in order to obtain even more accurate results.

  3. 3.

    Buildings facades alignment might differ according to the side of a street.

  4. 4.

    Other segmentation algorithms in which highly correlated variables will not cause multi-collinearity issues can be envisaged such as Super-Organizing-Maps, Random Forest, etc.

References

  1. Jacobs, J.: The Death and Life of Great American Cities. Random House, New York (1961)

    Google Scholar 

  2. Gehl, J.: Life Between Buildings: Using Public Space. Van Nostrand Reinhold, New York (1987)

    Google Scholar 

  3. Gehl, J.: Cities for People. Island Press, Washington, DC (2010)

    Google Scholar 

  4. Lynch, K.: The Image of the City. MIT Press, Cambridge (1960)

    Google Scholar 

  5. Whyte, W.H.: The Social Life of Small Urban Spaces (1980)

    Google Scholar 

  6. Farr, D.: Sustainable Urbanism: Urban Design with Nature. Wiley, Hoboken (2008)

    Google Scholar 

  7. Carmona, M.: Sustainable urban design-a possible agenda. In: Planning for a Sustainable Future, p. 165 (2001)

    Google Scholar 

  8. Talen, E., Koschinsky, J.: Compact, walkable, diverse neighborhoods: assessing effects on residents. Hous. Policy Debate 24(4), 717–750 (2014)

    Article  Google Scholar 

  9. National Association of City Transportation Officials: Urban Street Design Guide, NACTO Guidelines (2013)

    Google Scholar 

  10. National Association of City Transportation Officials: Transit Street Design Guide, NACTO Guidelines (2015)

    Google Scholar 

  11. National Association of City Transportation Officials: Global Street Design Guide, NACTO Guidelines (2017)

    Google Scholar 

  12. Psenner, A.: A smart researching and planning tool for the neuralgic urban zone: 3D-ZPA. In: REAL CORP 2014–PLAN IT SMART! Clever Solutions for Smart Cities. Proceedings of 19th International Conference on Urban Planning, Regional Development and Information Society, pp. 35–44 (2014)

    Google Scholar 

  13. Caniggia, G., Maffei, G.L.: Lettura dell’edilizia di base, vol. 215. Alinea Editrice, Firenze (2008)

    Google Scholar 

  14. Talen, E.: The geospatial dimension in urban design. J. Urban Des. 16(1), 127–149 (2011)

    Article  Google Scholar 

  15. Berghauser Pont, M.Y., Haupt, P.: Spacematrix, Space, Density and Urban Form. NAi Publishers, Rotterdam (2010)

    Google Scholar 

  16. Frankhauser, P.: La fractalité des structures urbaines. Villes, Anthropos, Paris (1994)

    Google Scholar 

  17. Thomas, I., et al.: Fractal dimension versus density of built-up surfaces in the periphery of Brussels. Pap. Reg. Sci. 86, 287–308 (2007)

    Article  Google Scholar 

  18. Gil, J., et al.: On the discovery of urban typologies: data mining the many dimensions of urban form. Urban Morphol. 16(1), 27 (2012)

    Google Scholar 

  19. Bernabé, A., et al.: Classification automatique des tissus urbains par la méthode des nuées dynamiques. 31e Rencontres AUGC, Cachan, France (2013)

    Google Scholar 

  20. Royall, R.A., Wortmann, T.: Finding the state space of urban regeneration: modeling gentrification as a probabilistic process using k-means clustering and Markov models. In: CUPUM 2015 Proceedings, Paper 275 (2015)

    Google Scholar 

  21. Hillier, B., Hanson, J.: The Social Logic of Space. Press Syndicate of the University of Cambridge, Cambridge (1984)

    Book  Google Scholar 

  22. Hillier, B.: Space is the Machine. Cambridge University Press, Cambridge (1998)

    Google Scholar 

  23. Porta, S., et al.: The network analysis of urban streets: a primal approach. Environ. Plan. B: Plan. Des. 33(5), 705–725 (2006)

    Article  Google Scholar 

  24. Sevtsuk, A.: Location and agglomeration: the distribution of retail and food businesses in dense urban environments. J. Plan. Educ. Res. 34(4), 374–393 (2014)

    Article  Google Scholar 

  25. Araldi, A., Fusco, G.: Urban form from the pedestrian point of view: spatial patterns on a street network. In: INPUT 2016, 14th–15th September 2016, Turin, Italy, pp. 32–37 (2016). https://hal.archives-ouvertes.fr/hal-01417484/document

  26. Araldi, A., Fusco, G.: Decomposing and recomposing urban fabric: the city from the pedestrian point of view. In: Gervasi, O., Murgante, B., Misra, S., Borruso, G., Torre, C.M., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O., Stankova, E., Cuzzocrea, A. (eds.) ICCSA 2017. LNCS, vol. 10407, pp. 365–376. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62401-3_27

    Chapter  Google Scholar 

  27. Fusco, G., Araldi, A.: The nine forms of the French Riviera: classifying urban fabrics from the pedestrian perspective. In: ISUF 2017 XXIV International Conference on City and Territory in the Globalization Age, Proceedings (2017). (in Press)

    Google Scholar 

  28. Fusco, G., Araldi, A.: Significant patterns in urban form: spatial analysis of morphological indicators. Rev. Int. Geomat. 27(4), 455–479 (2017)

    Article  Google Scholar 

  29. Yamada, I., Thill, J.C.: Local indicators of network-constrained clusters in spatial patterns represented by a link attribute. Ann. Assoc. Am. Geogr. 100(2), 269–285 (2010)

    Article  Google Scholar 

  30. Anselin, L.: Local indicators of spatial association. Geogr. Anal. 27(2), 93–115 (1995)

    Article  Google Scholar 

  31. Caniggia, G., Maffei, G.: Lettura dell’Edilizia di Base. Alinea, Firenze (1979)

    Google Scholar 

  32. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  33. Ewing, R., Handy, S.: Measuring the unmeasurable: urban design qualities related to walkability. J. Urban Des. 14(1), 65–84 (2009)

    Article  Google Scholar 

  34. Alexander, C.: A Pattern Language: Towns, Buildings, Construction. Oxford University Press, Oxford (1977)

    Google Scholar 

  35. Lévy, A.: Formes urbaines et significations: revisiter la morphologie urbaine. Espaces et sociétés, no. 3, pp. 25–48 (2005)

    Google Scholar 

  36. Benedikt, M.L.: To take hold of space: isovists and isovist fields. Environ. Plan. B: Plan. Des. 6(1), 47–65 (1979)

    Article  Google Scholar 

  37. Okabe, A., Sugihara, K.: Spatial Analysis Along Networks: Statistical and Computational Methods. Wiley, Hoboken (2012)

    Book  Google Scholar 

  38. Perez, J., Fusco, G., Araldi, A., Fuse, T.: Building typologies for urban fabric classification: Osaka and Marseille case studies. In: International Conference on Spatial Analysis and Modeling, 8th–9th September 2018, Tokyo, p. 13 (2018, submitted)

    Google Scholar 

Download references

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) as well as a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS). This study was supported by Joint Research Program No. 774 at CSIS, UTokyo (Zmap TOWN II 2013/14 Shapefile Osaka prefecture, Digital Road Map Database extended version 2015).

Author information

Authors and Affiliations

  1. Université Côte-d’Azur, CNRS, ESPACE, Nice, France

    Alessandro Araldi & Giovanni Fusco

  2. Regional Planning and Information Laboratory, The University of Tokyo, Tokyo, Japan

    Joan Perez & Takashi Fuse

Authors
  1. Alessandro Araldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joan Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Fusco
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takashi Fuse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandro Araldi .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Araldi, A., Perez, J., Fusco, G., Fuse, T. (2018). Multiple Fabric Assessment: Focus on Method Versatility and Flexibility. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95168-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95167-6

  • Online ISBN: 978-3-319-95168-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation