Assessing morphological resilience. Methodological challenges for metropolitan areas

Fusco, Giovanni and Venerandi, Alessandro; Gervasi, Osvaldo and Murgante, Beniamino and Misra, Sanjay and Garau, Chiara and Blecic, Ivan and Taniar, David and Apduhan, Bernady O. and Rocha, Ana Maria A.C. and Tarantino, Eufemia and Torre, Carmelo Maria and Karaca, Yeliz, eds. (2020) Assessing morphological resilience. Methodological challenges for metropolitan areas. In: Computational Science and Its Applications – ICCSA 2020. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer Science and Business Media Deutschland GmbH, ITA, pp. 593-609. ISBN 9783030588205 (https://doi.org/10.1007/978-3-030-58820-5_44)

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Abstract

Morphological resilience to urban change is the capacity of the form of the physical city to adapt to everchanging social, economic, and technical contexts. It echoes the theory of general resilience in ecology and is not linked to catastrophic events. Complexity theory informs principles of resilient systems that can be applied to urban form. This paper shows how morphological resilience can be assessed in a wide metropolitan area using geoprocessing protocols and available geospatial information. More specifically, a two-step methodology is proposed and tested on the French Riviera. First, spatial units of analysis are identified based on the interconnectedness of the street network, which is a major morphological component in itself. Next, a set of morpho-functional quantitative indicators of resilience is computed for such spatial units, accounting for both internal structure and integration within the wider metropolitan area. Thirteen indicators are selected to describe five different proxies of morphological resilience: diversity, connectivity, redundancy, modularity and efficiency. The paper also presents the preliminary results of morphological resilience assessment in the French Riviera. Urban central areas on the coast generally show more resilient characteristics, while more contrasted patterns emerge in the hinterland.