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Multiple Fabric Assessment: Focus on Method Versatility and Flexibility

  • Alessandro Araldi
  • Joan Perez
  • Giovanni Fusco
  • Takashi Fuse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10962)

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.

Keywords

Urban fabric Multiple Fabric Assessment Clustering Geoprocessing 

Notes

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).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alessandro Araldi
    • 1
  • Joan Perez
    • 2
  • Giovanni Fusco
    • 1
  • Takashi Fuse
    • 2
  1. 1.Université Côte-d’Azur, CNRS, ESPACENiceFrance
  2. 2.Regional Planning and Information LaboratoryThe University of TokyoTokyoJapan

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