Francesca Bruno

Spatial reconstruction of rainfall fields from rain gauge and radar data

Rainfall is a phenomenon difficult to model and predict, for the strong spatial and temporal heterogeneity and the presence of many zero values. We deal with hourly rainfall data provided by rain gauges, sparsely distributed on the ground, and radar data available on a fine grid of pixels. Radar data overcome the problem of sparseness of the rain gauge network, but are not reliable for the assessment of rain amounts. In this work we investigate how to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of Monte Carlo Markov Chain algorithms in a Bayesian hierarchical framework. We use zero-inflated distributions for taking zero-measurements into account. Several models are compared both in terms of data fitting and predictive performances on a set of validation sites. Finally, rainfall fields are reconstructed and standard error estimates at each prediction site are shown via easy-to-read spatial maps.

A simple non-separable, non-stationary spatiotemporal model for ozone

The past two decades have witnessed an increasing interest in the use of space-time models for a wide range of environmental problems. The fundamental tool used to embody both the temporal and spatial components of the phenomenon in question is the covariance model. The empirical estimation of space-time covariance models can prove highly complex if simplifying assumptions are not employed. For this reason, many studies assume both spatiotemporal stationarity, and the separability of spatial and temporal components. This second assumption is often unrealistic from the empirical point of view. This paper proposes the use of a model in which non-separability arises from temporal non-stationarity. The model is used to analyze tropospheric ozone data from the Emilia-Romagna Region of Italy.

Finite population properties of individual predictors based on spatial patterns

In this study a conceptual framework for assessing the statistical properties of a non-stochastic spatial interpolator is developed through the use of design-based finite population inference tools. By considering the observed locations as the result of a probabilistic sampling design, we propose a standardized weighted predictor for spatial data starting from a deterministic interpolator that usually does not provide uncertainty measures. The information regarding the coordinates of the spatial locations is known at the population level and is directly used in constructing the weighting system. Our procedure captures the spatial pattern by means of the Euclidean distances between locations, which are fixed and do not require any further assessment after the sample has been drawn. The predictor for any individual value turns in a ratio of design-based random quantities. We illustrate the predictor design-based statistical properties, i.e. asymptotically p-unbiasedness and p-consistency, for simple random sampling without replacement. An application to a couple of environmental datasets is presented, for assessing predictor performances in correspondence of different population characteristics. A comparison with the equivalent non-spatial predictor is presented.

Body Composition, Muscle Strength, and Physical Function of Patients with Bethlem Myopathy and Ullrich Congenital Muscular Dystrophy

Objective. To determine the contributions of body mass, adiposity, and muscularity to physical function and muscle strength in adult patients with Bethlem myopathy (BM) and Ullrich congenital muscular dystrophy (UCMD). Materials and Methods. Evaluation involved one UCMD and 7 BM patients. Body composition was determined by body mass index (BMI) and dual-energy-X-ray-absorptiometry (DXA), muscle strength by dynamometry, physical function by the distance walked in 6 minutes (6MWD), forced vital capacity (FVC) by a spirometer. Results. Six participants were of normal weight and 2 overweight based on BMI; all were sarcopenic based on appendicular fat free mass index (AFFMI); and 7 were sarcopenic obese based on AFFMI and % fat mass. Average muscle strength was reduced below 50% of normal. The 6MWD was in BM patients 30% less than normal. FVC was reduced in 4 of the BM patients. Muscle strength had a good correlation with the physical function variables. Correlation between muscle strength and BMI was poor; it was very high with AFFMI. AFFMI was the best single explicator of muscle strength and physical function. Conclusion. Muscle mass determined by DXA explains most of the variability of the measures of muscle strength and physical function in patients with BM and UCMD.

Spatio-temporal regression on compositional covariates: modeling vegetation in a gypsum outcrop

Investigating the relationship between vegetation cover and substrate typologies is important for habitat conservation. To study these relationships, common practice in modern ecological surveys is to collect information regarding vegetation cover and substrate typology over fine regular lattices, as derived from digital ground photos. Information on substrate typologies is often available as compositional measures, e.g., the area proportion occupied by a certain substrate. Two primary issues are of interest for ecologists: first, how much substrate typologies differ in terms of relative suitability for vegetation cover and, second, whether suitability varies over time. This paper develops a procedure for managing compositional covariates within a Bayesian hierarchical framework to effectively address the aforementioned issues. A spatio-temporal model is adopted to estimate the temporal pattern characterizing substrate relative suitability for vegetation cover and, at the same time, to account for spatio-temporal correlation. Relative suitability is modeled by time-varying regression coefficients, and spatial, temporal and spatio-temporal random effects are modeled using Gaussian Markov Random Field models.

Non-parametric regression on compositional covariates using Bayesian P-splines

Methods to perform regression on compositional covariates have recently been proposed using isometric log-ratios (ilr) representation of compositional parts. This approach consists of first applying standard regression on ilr coordinates and second, transforming the estimated ilr coefficients into their contrast log-ratio counterparts. This gives easy-to-interpret parameters indicating the relative effect of each compositional part. In this work we present an extension of this framework, where compositional covariate effects are allowed to be smooth in the ilr domain. This is achieved by fitting a smooth function over the multidimensional ilr space, using Bayesian P-splines. Smoothness is achieved by assuming random walk priors on spline coefficients in a hierarchical Bayesian framework. The proposed methodology is applied to spatial data from an ecological survey on a gypsum outcrop located in the Emilia Romagna Region, Italy.

Spatiotemporal Model for Short-Term Predictions of Air Pollution Data

Recently, the interest of many environmental agencies is on short-term air pollution predictions referred at high spatial resolution. This permits citizens and public health decision-makers to be informed with visual and easy access to air-quality assessment. We propose a hierarchical spatiotemporal model to enable use of different sources of information to provide short-term air pollution forecasting. In particular, we combine monitoring data and numerical model output in order to obtain short-term ozone forecasts over the Emilia Romagna region where the orography plays an important role on the air pollution; thus, the elevation is also included in the model. We provide high-resolution spatial forecast maps and uncertainty associated with these predictions. The assessment of the predictive performance of the model is based upon a site-one-out cross-validation experiment.

Economic Growth and Dualism in Italian Regions: A Spatiotemporal Model

The topic of regional economics and business cycles has attracted increasing attention inrecent years. However, an analysis of the dynamics and the features of economic growth in different areas is hampered by the fact that official Italian GDP statistics at the regional level are not updated and have only an annual frequency. For this reason, we considered a high frequency and easily updatable Indicator of Regional Economic Activity that provides information on different dimensions of local business cycles and thus can be useful to policy makers. We propose a spatiotemporal hierarchical model of regional economicgrowth that isolates different sources of variability.

Interpreting Air Quality Indices as Random Quantities

Synthetic indices are a way of condensing complex situations to give one single value. A very common example of this in environmental studies is that of air quality indices; in their construction, statistics is helpful in summarizing multidimensional information. In this work, we are going to consider synthetic air-quality indices as random quantities, and investigate their main properties by comparing the confidence bands of their cumulative distribution functions.

Convergenza economica e polarizzazione nelle regioni dell’UE: il ruolo degli effetti spaziali

The aim of this paper is to assess European Union Cohesion Policy by estimating a conditional β-convergence model for a sample of 196 EU regions over the period 1980-2006, using a spatial econometric perspective. Under the assumption of substantial coincidence of geographical and economic periphery in EU-15, the final model combines the identification of two regimes and spatial dependence. The main findings suggest a positive role of EU Regional Policy on convergence among Objective 1 regions and, at the same time, call for a better consideration of spatial spillover effects in planning Regional Policy.