1,722,662 research outputs found
A Public–Private Insurance Model for Disaster Risk Management: An Application to Italy
Full text linkThis paper proposes a public–private insurance model for earthquakes and floods in Italy in which the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing an insurance scheme intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business aims at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual are estimated through risk-modeling. In order to evaluate the insurer’s loss profile, spatial correlation among insured assets is included. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies. This evidence is stronger for earthquake risks, but it is considerable for floods too. We found that jointly managing the two perils alleviates the burden on public capitals by lowering the amount of capitals required and by keeping the probability of additional capital injections into the insurance reserves relatively low
Natural Risk Assessment of Italian Municipalities for Residential Insurance
No full textIn this work, we propose a catastrophe modeling approach to flood and earthquake risk assessment for residential buildings in Italy. This work aims at supporting governors in the definition of a natural risk management strategy. To detect the critical areas of the territory, we compute expected losses per square meter, per municipality, and per structural typology. Our approach allows us to identify the areas where the exposure strongly affects the risk due to the high inhabited density or the presence of fragile buildings. This information is of major relevance for disaster risk reduction. We find that earthquakes in Italy generate annual expected losses approximately equal to 6234.67 million Euros, while flood expected losses amount to about 875.90 million Euros per year. Although earthquakes produce the highest expected losses at the national level, flood losses per square meter often exceed the corresponding earthquake ones
Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area
No full textIn this paper, we present a robust spatiotemporal statistical methodology that is capable of accurately forecasting traffic in the flood-prone area of the Mandolossa in the Province of Brescia (Italy). An innovative combination of two sources of mobile phone data is proposed to obtain an extremely accurate representation of the flows of people passing by the streets directly linked to the risky area. Three types of flows have been considered: outflows (from the flood-prone area to the neighborhood), inflows (from the neighborhood to the flood-prone area), and internal flows (within the flood-prone area). The three flows are assumed to be dependent on each other and are modeled using a vector autoregressive approach. We found evidence of both weekly and daily seasonal components in the time series. To capture the seasonality, a dynamic harmonic regression component has been included, where the optimal number of Fourier bases in the periodic functions has been chosen according to a criterion based on the Akaike Information Criteria. On the other side, the set of autoregressive parameters has been defined in such a way as to represent the time period necessary for the mobile phone company to observe, process, and release the data. The forecasting ability of the model has been assessed using blocked k-folds cross-validation along with the mean absolute percentage error and the hit rate. Though the model performs better for non-summer days, we found that it satisfactorily forecasts both the number and the level of people moving
LHCb distributed data analysis
Full text linkThis paper describes the LHCb distributed data analysis system. For data analysis, the LHCb Collaboration will exploit the WLCG distributed computing resources spread all over the world. Two main software tools will be used: DIRAC and GANGA. DIRAC is mainly used to easily access distributed computing resources, while GANGA is a user interface that allows users to submit analysis jobs in a data-driven mode
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Statistical indicators based on mobile phone and street maps data for risk management in small urban areas
No full textThe use of new sources of big data collected at a high-frequency rate in conjunction with administrative data is critical to developing indicators of the exposure to risks of small urban areas. Correctly accounting for the crowding of people and for their movements is crucial to mitigate the effect of natural disasters, while guaranteeing the quality of life in a “smart city” approach. We use two different types of mobile phone data to estimate people crowding and traffic intensity. We analyze the temporal dynamics of crowding and traffic using a Model-Based Functional Cluster Analysis, and their spatial dynamics using the T-mode Principal Component Analysis. Then, we propose five indicators useful for risk management in small urban areas: two composite indicators based on cutting-edge mobile phone dynamic data and three indicators based on open-source street map static data. A case study for the flood-prone area of the Mandolossa (the western outskirts of the city of Brescia, Italy) is presented. We present a multi-dimensional description of the territory based on the proposed indicators at the level of small areas defined by the Italian National Statistical Institute as “Sezioni di Censimento” and “Aree di Censimento”
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