Spatial models for flood risk assessment

The problem of computing risk measures associated to flood events is extremely important not only from the point of view of civil protection systems but also because of the necessity for the municipalities of insuring against the damages. In this work we propose, in the framework of an integrated strategy, an operating solution which merges in a conditional approach the information usually available in this setup. First we use a Logistic Auto-Logistic (LAM) model for the estimation of the univariate conditional probabilities of flood events. This approach has two fundamental advantages: it allows to incorporate auxiliary information and does not require the target variables to be independent. Then we simulate the joint distribution of floodings by means of the Gibbs Sampler. Finally we propose an algorithm to increase ex post the spatial autocorrelation of the simulated events. The methodology is shown to be effective by means of an application to the estimation of the flood probability of Italian hydrographic regions

Spatial models for flood risk assessment

The problem of computing risk measures associated to flood events is extremely important not only from the point of view of civil protection systems but also because of the necessity for the municipalities of insuring against the damages. In this work we propose, in the framework of an integrated strategy, an operating solution which merges in a conditional approach the information usually available in this setup. First we use a Logistic Auto-Logistic (LAM) model for the estimation of the univariate conditional probabilities of flood events. This approach has two fundamental advantages: it allows to incorporate auxiliary information and does not require the target variables to be indepen- dent. Then we simulate the joint distribution of floodings by means of the Gibbs Sampler. Finally we propose an algorithm to increase ex post the spatial autocorrelation of the simulated events. The methodology is shown to be effective by means of an application to the estimation of the flood probability of Italian hydrographic regions.Flood Risk, Conditional Approach, LAM Model, Pseudo-Maximum Likelihood Estimation, Spatial Autocorrelation, Gibbs Sampler.

R&D, firm size, and product innovation dynamics.

This paper addresses a debated issue in the economics innovation literature, namely the existence of increasing return to R&D expenditures and firm size on innovation output. It further explores how structural characteristics of the firm as well as contextual factors affect the dynamics of product innovation over a relatively long period of time. Taking advantage of an original and unique database comprising innovation data recorded on a monthly base we show that: (i) a negative binomial distribution model is able to predict with great accuracy the probability of having a given number of product announcement sent out in a month; (ii) constant returns to size and R&D expenditure may reasonably characterize the innovation production function of sampled firms; (iii) vertically integrated manufacturers as well as producers operating a larger product portfolio exhibit a higher propensity to introduce new products than their specialized competitors.

Three-dimensional structure of the flow inside the left ventricle of the human heart

The laboratory models of the human heart left ventricle developed in the last decades gave a valuable contribution to the comprehension of the role of the fluid dynamics in the cardiac function and to support the interpretation of the data obtained in vivo. Nevertheless, some questions are still open and new ones stem from the continuous improvements in the diagnostic imaging techniques. Many of these unresolved issues are related to the three-dimensional structure of the left-ventricular flow during the cardiac cycle. In this paper we investigated in detail this aspect using a laboratory model. The ventricle was simulated by a flexible sack varying its volume in time according to a physiologically shaped law. Velocities measured during several cycles on series of parallel planes, taken from two orthogonal points of view, were combined together in order to reconstruct the phase averaged, three-dimensional velocity field. During the diastole, three main steps are recognized in the evolution of the vortical structures: i) straight propagation in the direction of the long axis of a vortex-ring originated from the mitral orifice; ii) asymmetric development of the vortex-ring on an inclined plane; iii) single vortex formation. The analysis of three-dimensional data gives the experimental evidence of the reorganization of the flow in a single vortex persisting until the end of the diastole. This flow pattern seems to optimize the cardiac function since it directs velocity towards the aortic valve just before the systole and minimizes the fraction of blood residing within the ventricle for more cycles

R&D, Firm Size, and Product Innovation Dynamics.

This paper addresses a debated issue in the economics innovation literature, namely the existence of increasing return to R&D expenditures and sirm size on innovation output. It further explores how structural characteristics of the sirm as well as contextual factors affect the dynamics of product innovation over a relatively long period of time. Taking advantage of an original and unique database comprising innovation data recorded on a monthly base we show that: (i) a negative binomial distribution model is able to predict with great accuracy the probability of having a given number of product announcement sent out in a month; (ii) constant returns to size and R&D expenditure may reasonably characterize the innovation production function of sampled sirms; (iii) vertically integrated manufacturers as well as producers operating a larger product portfolio exhibit a higher propensity to introduce new products than their specialized competitors.

A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data

This paper proposes an algorithm for the estimation of the parameters of a Logistic Auto-logistic Model when some values of the target variable are missing at random but the auxiliary information is known for the same areas. First, we derive a Monte Carlo EM algorithm in the setup of maximum pseudo-likelihood estimation; given the analytical intractability of the conditional expectation of the complete pseudo-likelihood function, we implement the E-step by means of Monte Carlo simulation. Second, we give an example using a simulated dataset. Finally, a comparison with the standard non-missing data case shows that the algorithm gives consistent results.Spatial Missing Data, Monte Carlo EM Algorithm, Logistic Auto-logistic Model, Pseudo-Likelihood.

Mappe di probabilità di sito archeologico : un passo avanti

La necessità di disporre di mappe di probabilità di sito archeologico è un tema attualmente al centro di un rinnovato interesse anche grazie alla disponibilità di una moltitudine di informazioni territoriali gestite ed elaborate con l’ausilio della tecnologia GIS. Gli studi che finora si sono occupati del problema, hanno fatto riferimento a modellistiche di tipo regressivo od autoregressivo che, sebbene centrate sulle peculiarità del fenomeno, si sono dimostrate sensibilmente dipendenti dalla presenza di dati anomali nella stima dei parametri e da ipotesi specifiche sui casi in esame. L’intento del presente lavoro è invece quello di proporre una soluzione più robusta e generalizzabile in linea con i recenti sviluppi nel campo della modellistica non parametrica la quale potrebbe dare un forte impulso all’uso congiunto della statistica e della tecnologia GIS. L’informazione geografica ricavabile dai dati fisici e satellitari relativi all’area di studio rappresenta infatti, grazie al dettaglio che la caratterizza, una fonte ausiliaria insostituibile per spiegare la presenza o meno di un sito in una data posizione della mappa. L’approccio proposto è stato sviluppato ed applicato per l’area test di Cures Sabini ottenendo dei risultati abbastanza incoraggianti, soprattutto se letti in termini della estrema facilità di interpretazione dei risultati da parte di non statistici e della riscontrata possibilità di rendere automatico il processo di produzione delle mappe di probabilità. Questa evidenza permetterebbe ad operatori del settore non professionalmente preparati da un punto di vista metodologico, di estendere il modello a situazioni anche molto diverse da quella analizzata