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The index for evaluation of operational efficiency of limit condition of emergency (I.OPà.CLE), developed by the Italian Department of Civil Protection in 2013, is a probabilistic method aimed to assess the operational efficiency of a municipal contingency plan in case of earthquake occurrence. If the physical elements of this system, assimilated to a network, are not able to resist the earthquake, the whole emergency management can be seriously hindered. The system and the element characteristics are provided by the analysis of the limit condition of emergency (LCE). The method, described in previous papers by the same authors, relies on the formulation of synthetic indices expressing the probability for the emergency system and its relevant components to preserve the operational capability for seismic scenario events with different return periods. The paper describes the results of an experimental application of I.OPà.CLE on a sample of 30 analyses of LCE. Each LCE was analyzed in two different layouts: the original one, as conceived by each municipality or appointed institution, and a minimal one, as obtained by reducing the number of physical components to the essential ones. In total 60 I.OPà.CLE analyses were carried out. The former part of the paper deals with the description of LCE analyses of the sample and their physical components, also by a cross comparison through original and minimal systems. In the second part the results achieved by processing I.OPà.CLE are presented and discussed, so as to draw some final considerations in terms of operational efficiency for the two considered configurations.

Earthquake is a natural calamity that cannot be predicted, and even though one day, hypothetically, this might be possible, its occurrence will not anyhow be avoided. Its unpredictability, together with its inescapability, poses the accent on preparedness and prevention, which represent the sole activities that can be pursued in order to mitigate its catastrophic effects. Within this scenario, human behaviour becomes extremely relevant. Two main actors of seismic risk mitigation are especially important: the population and the policy-makers, the latter in charge of safety and protection of the civil community. Contrary to a rational calculus, which would like people to protect themselves more and more as risk increases, we observe instances of inefficient behaviours, dictated to a large extent by the cognitive strategies with which people deal with when making decisions in the domain of seismic risk. Moreover, this expectation is largely unattended, because of the controversial behaviours and different perspectives of the two actors in dealing with seismic risk. The paper approaches the problem through the tools of behavioural economics, trying to investigate possible cognitive biases and risk misperception which can trigger crowding out either of individuals or policy-makers for undertaking seismic risk reduction measures, with the final goal of supporting and improving the effectiveness of risk reduction strategies designed at governmental level. A particular focus is dedicated to Italy, as being a country with high seismic risk and, on the other hand, significant skills on seismic risk reduction strategies.

Following major seismic events occurred in Italy over the last 50 years, a notable amount of data relevant to post-earthquake damage of ordinary buildings was collected. These data today represent an inestimable scientific heritage, useful for prevision and prevention purposes, including calibration of vulnerability models for seismic risk assessment and formulation of damage scenarios. However, the data sets resulting from different inspection tools developed over the years are not immediately comparable with each other. The need for enhancing the reliability of prevention models and more effectively support strategic decision-making has moved the Italian Civil Protection Department to undertake, since 2014, a specific project with this ambitious goal. Developed with the technological support of Eucentre Foundation, the web-gis platform, named Da.D.O. (Observed Damage Database), is meant to store and to share data from large post-earthquake damage campaigns occurred in the past. Da.D.O. is addressed to Civil Protection Department users, members of the scientific community and Regions, though its access could be further extended in the future to other stakeholders. The paper describes goals, contents and capabilities of the IT platform, which, at present, includes data sets relevant to nine seismic events (or sequences) occurred from 1976 (Friuli earthquake) to 2012 (Emilia-Romagna earthquake).

The severity of damage detected on residential buildings of historical centres damaged by 2009 L'Aquila earthquake along with the necessity to preserve their cultural and architectural heritage led to the introduction of new specific procedures to regulate their reconstruction process. The goal of the present work is to investigate on the reconstruction of damaged residential buildings in the historical centres of municipalities within the so-called "crater" (excepting for L'Aquila), from both procedural and technical-economic point of view. Firstly, the reconstruction model adopted to define the public grant (known as the Integrated Model for the Municipalities of the Crater - MIC) is presented, then the statistics related to the characteristics of historical centers are analysed in terms: i) Building Aggregates (namely AE), ii ) Aggregte Minimum Unit (namely UMI), made by several buildings (ED) iii) Isolated buildings (namely ES). A focus is given on the damage detected on buildings (ED) of the UMI by means of the first level form for post-earthquake damage and safety assessment and short term countermeasures, AeDES. Finally, the costs for repair and seismic strengthening of UMI are reported as well as the type of architectonic assets of buildings and their impact on reconstruction costs.

This paper describes experiments performed using diff erent approaches for spatial data clustering, aiming to assist the delineation of management classes in Precision Agriculture (PA). These approaches were established from the partitional clustering algorithm Fuzzy c-Means (FCM), traditionally used in PA, and from the hierarchical clustering algorithm HACC-Spatial, especially designed for PA. We also performed experiments using diff erent clustering ensembles approaches, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from attribute splitting or using exclusively FCM or HACC-Spatial. The achieved results exhibited some diff erences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. The HACCSpatial algorithm achieved, in general, better results when compared to FCM and ensembles approaches. Regarding the consensus clusterings provided by ensembles, we can point out the attempt to achieve agreement results which most closely matches the original clusterings, decreasing or increasing the stratifi cation of the management classes maps. Made available in DSpace on 2018-11-13T23:58:24Z (GMT). No. of bitstreams: 1 UsingensemblesSperanzaGeoinfo2017.pdf: 934144 bytes, checksum: 576fcfe3ad51c7a83f4a68536ec8f972 (MD5) Previous issue date: 2018-11-13 Título equivalente em português: Utilizando ensembles com abordagens de agrupamento espacial para o delineamento de classes de manejo em agricultura de precisão. Edição especial de papers selecionados que foram apresentados no GEOINFO 2016.