Built Environment Typologies Prone to Risk: A Cluster Analysis of Open Spaces in Italian Cities

Planning for preparedness, in terms of multi-hazard disasters, involves testing the relevant abilities to mitigate damage and build resilience, through the assessment of deterministic disaster scenarios. Among risk-prone assets, open spaces (OSs) play a significant role in the characterization of the built environment (BE) and represent the relevant urban portion on which to develop multi-risk scenarios. The aim of this paper is to elaborate ideal scenarios—namely, Built Environment Typologies (BETs)—for simulation-based risk assessment actions, considering the safety and resilience of BEs in emergency conditions. The investigation is conducted through the GIS data collection of the common characteristics of OSs (i.e., squares), identified through five parameters considered significant in the scientific literature. These data were processed through a non-hierarchical cluster analysis. The results of the cluster analysis identified five groups of OSs, characterized by specific morphological, functional, and physical characteristics. Combining the outcomes of the cluster analysis with a critical analysis, nine final BETs were identified. The resulting BETs were linked to characteristic risk combinations, according to the analysed parameters. Thus, the multi-risk scenarios identified through the statistical analysis lay the basis for future risk assessments of BEs, based on the peculiar characteristics of Italian towns.

[1]  C. Cartalis,et al.  PROGRESS IN URBAN GREENERY MITIGATION SCIENCE – ASSESSMENT METHODOLOGIES ADVANCED TECHNOLOGIES AND IMPACT ON CITIES , 2018, JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT.

[2]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[3]  F. Nicol,et al.  Urban environment influence on natural ventilation potential , 2006 .

[4]  Ayyoob Sharifi,et al.  Resilient urban forms: A review of literature on streets and street networks , 2019, Building and Environment.

[5]  Enrico Ronchi,et al.  A dynamic approach for the impact of a toxic gas dispersion hazard considering human behaviour and dispersion modelling. , 2016, Journal of hazardous materials.

[6]  F. Salata,et al.  On the impact of innovative materials on outdoor thermal comfort of pedestrians in historical urban canyons , 2018 .

[7]  Kenneth G. Manton,et al.  Cluster Analysis: Overview , 2005 .

[8]  Marcello Arosio,et al.  The whole is greater than the sum of its parts: a holistic graph-based assessment approach for natural hazard risk of complex systems , 2020, Natural Hazards and Earth System Sciences.

[9]  Nur Faraidah Muhammad Di,et al.  The multiple outliers detection using agglomerative hierarchical methods in circular regression model , 2017 .

[10]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[11]  D. Koren,et al.  The Potential of Open Space for Enhancing Urban Seismic Resilience: A literature Review , 2019, Sustainability.

[12]  Robert S. Chen,et al.  Natural Disaster Hotspots: A Global Risk Analysis , 2005 .

[13]  V. Achilli,et al.  A GIS TOOL FOR THE MANAGEMENT OF SEISMIC EMERGENCIES IN HISTORICAL CENTERS: HOW TO CHOOSE THE OPTIMAL ROUTES FOR CIVIL PROTECTION INTERVENTIONS , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[14]  Yun Wu,et al.  A Planning Support Tool for Layout Integral Optimization of Urban Blue–Green Infrastructure , 2020, Sustainability.

[15]  Junyan Yang,et al.  Modeling urban intersection form: Measurements, patterns, and distributions , 2020 .

[16]  Nirajan Shiwakoti,et al.  A review on the performance of an obstacle near an exit on pedestrian crowd evacuation , 2019, Safety Science.

[17]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[18]  Michele Morganti,et al.  Urban morphology indicators for solar energy analysis , 2017 .

[19]  Gilbert F. White,et al.  Knowing better and losing even more: the use of knowledge in hazards management , 2001 .

[20]  Xuefang Li,et al.  Experimental modelling of urban flooding: A review , 2019, Journal of Hydrology.

[21]  E. Quagliarini,et al.  How urban layout and pedestrian evacuation behaviours can influence flood risk assessment in riverine historic built environments , 2021, Sustainable Cities and Society.

[22]  M. Bebbington,et al.  Multihazards Scenario Generator: A Network‐Based Simulation of Natural Disasters , 2021, Risk analysis : an official publication of the Society for Risk Analysis.

[23]  Robert D. Brown,et al.  Designing public open space to support seismic resilience: A systematic review , 2019, International Journal of Disaster Risk Reduction.

[24]  Margreth Keiler,et al.  Challenges of analyzing multi-hazard risk: a review , 2012, Natural Hazards.

[25]  Marco Minghini,et al.  A Review of OpenStreetMap Data , 2017 .

[26]  Enrico Quagliarini,et al.  Agent-based model for earthquake pedestrians’ evacuation in urban outdoor scenarios: Behavioural patterns definition and evacuation paths choice , 2014 .

[27]  Gian Paolo Cimellaro,et al.  Simulating earthquake evacuation using human behavior models , 2017 .

[28]  Enrico Quagliarini,et al.  Flexible Workflow for Determining Critical Hazard and Exposure Scenarios for Assessing SLODs Risk in Urban Built Environments , 2021, Sustainability.

[29]  Gabriele Bernardini,et al.  Integrating human behaviour and building vulnerability for the assessment and mitigation of seismic risk in historic centres: Proposal of a holistic human-centred simulation-based approach , 2020 .

[30]  Monika Kuffer,et al.  Open spaces and risk perception in post-earthquake Kathmandu city , 2018 .

[31]  D. García-Castellanos,et al.  Poles of inaccessibility: A calculation algorithm for the remotest places on earth , 2007 .

[32]  Enrico Ronchi,et al.  The Process of Verification and Validation of Building Fire Evacuation Models , 2013 .

[33]  Joel C. Gill,et al.  Hazard interactions and interaction networks (cascades) within multi-hazardmethodologies , 2016 .

[34]  G. Salvalai,et al.  SLow Onset Disaster Events Factors in Italian Built Environment Archetypes , 2020 .

[35]  Hazlina Selamat,et al.  Optimized exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimization , 2020 .

[36]  Peter F. Fisher,et al.  Geographic Information Systems and Surfaces , 2007 .

[37]  A. Scolobig,et al.  From multi-risk assessment to multi-risk governance. recommendations for future directions , 2015 .

[38]  Jaiteg Singh,et al.  Systematic Literature Review of Data Quality Within OpenStreetMap , 2017, 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS).

[39]  Nadejda Komendantova,et al.  Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: feedback from civil protection stakeholders , 2014 .

[40]  Ayyoob Sharifi,et al.  Urban form resilience: A meso-scale analysis , 2019, Cities.

[41]  Robert G. Bell,et al.  Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modelling , 2011 .

[42]  Stefan Wiemer,et al.  The quantification of low-probability–high-consequences events: part I. A generic multi-risk approach , 2014, Natural Hazards.

[43]  M. Morganti Spatial Metrics to Investigate the Impact of Urban Form on Microclimate and Building Energy Performance: An Essential Overview , 2021 .

[44]  Enrico Quagliarini,et al.  How to create seismic risk scenarios in historic built environment using rapid data collection and managing , 2021 .

[45]  Valentina Gallina,et al.  A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment. , 2016, Journal of environmental management.

[46]  F. Luino,et al.  A clustering classification of catchment anthropogenic modification and relationships with floods. , 2020, The Science of the total environment.

[47]  Vikas Agrawal,et al.  An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster , 2014, Expert Syst. Appl..

[48]  Gülen Çağdaş,et al.  Fuzzy logic in agent-based modeling of user movement in urban space: Definition and application to a case study of a square , 2020 .

[49]  Ying Zhou,et al.  The impact of urban street canyons on population exposure to traffic-related primary pollutants , 2008 .

[50]  Virgilio Ciancio,et al.  High albedo materials to counteract heat waves in cities: An assessment of meteorology, buildings energy needs and pedestrian thermal comfort , 2019, Building and Environment.