Abstract
Building Back Better in disaster recovery and reconstruction requires the adoption of integrated and context-sensitive approaches to the design and planning of Temporary Housing (TH) sites. However, there is a lack of methods for enabling successful outcomes in housing assistance provision, e.g. via a quantitative evaluation of the social-spatial qualities of the sites, and supporting the negotiation of urban design changes and the development of a coherent end-of-life plan. The paper aims to uncover formal analogies between different TH sites’ layouts by linking Space Syntax and Clustering analysis within an unsupervised machine-learning pipeline, which can consider a virtually unlimited number of configurational qualities and how they vary across different scales. The potential benefits of the proposal are illustrated through its application to the study of 20 TH sites built in Norcia after the 2016-2017 Central Italy earthquakes. The results indicate the proposal enables distinguishing different types of spatial arrangements according to local strategic priorities and suggest the opportunity to extend the study in the future to set up rules of thumb for the design of site layout options. The paper ultimately aims to equip local administrations and contracted professionals with a much-needed tool to develop and rapidly audit proposals for temporary neighbourhoods oriented at enhancing the resilience of disaster-affected towns both in the medium and in the long term.
Highlights
During the last decades, the incidence of disasters that stem from natural hazards has increased considerably (EM-DAT [no date]), threatening the housing security of the many people currently living in areas suffering from inherent socioeconomic and spatial vulnerabilities
This section is divided in two parts: the first (Section 4.1) presents the results of the analysis considering the Temporary Housing (TH) sites layouts in isolation from the rest; the second (Section 4.2) links the results of the analysis to the planning regulations of Norcia and to the morphology of the destroyed city
The indecision regarding the assignments of these two TH sites to one or the other cluster is reflected in the Fuzzy C-Means (FCM) probabilistic values since their relative memberships to clusters 1 and 3 appear somehow comparable (Table 1)
Summary
The incidence of disasters that stem from natural hazards has increased considerably (EM-DAT [no date]), threatening the housing security of the many people currently living in areas suffering from inherent socioeconomic and spatial vulnerabilities. A growing awareness of the scale of the problem and increasing concern about “humanitarian aftershocks” increasing pre-existing vulnerabilities (Alexander 1989; Davis and Alexander 2015; Contreras et al 2017) have recently pushed forward research in the area of post-disaster housing assistance, whose volume of publications is rapidly expanding in multiple directions (Yi and Yang 2014). Research on analytical methods supporting the spatial design of socially adequate temporary housing (TH) neighborhoods which add to the resilience of disaster-affected settlements, and enabling the assessment of different site layouts, is still in its infancy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
