Alessandra Petrucci

Small area estimation for spatial correlation in watershed erosion assessment

This article describes the combination of small area estimator and a simultaneously autoregressive (SAR) model applied to the erosion data collected at the Rathbun Lake Watershed in Iowa (USA). The proposed methodology considers and EBLUP estimator with spatially correlated random area effects taking into account the information provided by neighboring areas. The article discusses the gain obtained from modeling the spatial correlation among small area random effects useful in representing the unexplained variation of the small area target quantities. Moreover the estimator of mean squared error of the proposed estimator is presented.

Small area estimation for semicontinuous skewed spatial data: An application to the grape wine production in Tuscany

Linear-mixed models are frequently used to obtain model-based estimators in small area estimation (SAE) problems. Such models, however, are not suitable when the target variable exhibits a point mass at zero, a highly skewed distribution of the nonzero values and a strong spatial structure. In this paper, a SAE approach for dealing with such variables is suggested. We propose a two-part random effects SAE model that includes a correlation structure on the area random effects that appears in the two parts and incorporates a bivariate smooth function of the geographical coordinates of units. To account for the skewness of the distribution of the positive values of the response variable, a Gamma model is adopted. To fit the model, to get small area estimates and to evaluate their precision, a hierarchical Bayesian approach is used. The study is motivated by a real SAE problem. We focus on estimation of the per-farm average grape wine production in Tuscany, at subregional level, using the Farm Structure Survey data. Results from this real data application and those obtained by a model-based simulation experiment show a satisfactory performance of the suggested SAE approach.

Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data.

The mean of a balanced ranked set sample is more efficient than the mean of a simple random sample of equal size and the precision of ranked set sampling may be increased by using an unbalanced allocation when the population distribution is highly skewed. The aim of this paper is to show the practical benefits of the unequal allocation in estimating simultaneously the means of more skewed variables through real data. In particular, the allocation rule suggested in the literature for a single skewed distribution may be easily applied when more than one skewed variable are of interest and an auxiliary variable correlated with them is available. This method can lead to substantial gains in precision for all the study variables with respect to the simple random sampling, and to the balanced ranked set sampling too.

Quality of Life in Europe: Objective and Subjective Indicators

Here we will present the preliminary results of a geographical study1 among the regions in Europe. It is an exploratory comparative analysis among the regions in Europe that represents the first phase in a larger analysis which aims to study quality of life at the European level.2

Spatial Data Mining for Clustering: An Application to the Florentine Metropolitan Area Using RedCap

The paper presents an original application of the recently proposed RedCap method of spatial clustering and regionalization on the Florentine Metropolitan Area (FMA). Demographic indicators are used as the input of a spatial clustering and regionalization model in order to classify the FMA’s municipalities into a number of demographically homogeneous as well as spatially contiguous zones. In the context of a gradual decentralization of governance activities we believe the FMA is a representative case of study and that the individuation of new spatial areas built considering both the demographic characteristics of the resident population and the spatial dimension of the territory where this population insists could become a useful tool for local governance.

Regional frequency analysis and geoadditive modeling for design storm estimates in the Arno river basin (Italy)

Investigations on extreme hydrological events are often at the basis of environmental studies related to hydrological cycle changes and more in general with climate change. Design storm represents an important variable for its implications on flood risk assessment and territory protection measures definition. A regional frequency analysis for studying and understanding the annual maxima of daily rainfall, based on the index variable method, is implemented here on the Tuscany Region (Central Italy). According to the hierarchical approach on three levels, the studied area is divided into homogeneous regions and subregions; the statistical homogeneity within the regions is verified through several homogeneity tests. Furthermore, the two-component extreme value probability distribution of the extreme rainfall is considered identical within each homogeneous region unless a scale factor, called index rainfall, is given by a multivariate model based on climatic and geomorphological characteristics. A geoadditive model for extremes assuming that the observations follow generalized extreme value distribution whose locations are spatially dependent is also carried out on the catchment area of Arno River, the main river of Tuscany Region that extends for a large part of the region area. The application of the two methods is discussed considering the comparison of the maps of the design storm for daily duration and 50-year return period.

A Two-Part Geoadditive Small Area Model for Geographical Domain Estimation

We are interested in estimating small domain means of a response variable that shows a spatial trend and has a continuous skewed distribution with a large number of values clustered at zero. This kind of variable can occur in many surveys, like business or agricultural surveys: examples are the quantity of crops produced or the amount of land allocated for their production collected by the Farm Structure Survey driven by the Italian Statistical Institute. The small sample size within the areas requires the use of small area model dependent methods to increase the effective area sample size by using census and administrative auxiliary data. To account simultaneously for the excess of zeros, the skewness of the distribution and the possible spatial trend of the data, we present a two-part geoadditive small area model. An application to the estimation of the per-farm average grapevine production in Tuscany at Agrarian Region level shows the satisfactory performance of the model.

Understanding adaptive capacity to extreme events and climate change in urban areas

Urban environments are often the “hotspots” of vulnerability to extreme events in terms of human and material costs. This fact reflect a number of factors, including climate change, rapid urbanization often poorly planned, and, importantly, a general lack of assessment tools and measures that would enhance resilience and adaptation capacities of urban centers. The international and interdisciplinary cooperation are considered in order to improve adaptive capacity and resilience.

Statistical Characterization of the Virtual Water Trade Network

The water that is used in the production process of a product (a supply, commodity or service) is called the “virtual water” contained in the product. If one country (or region, company, individual, etc.) exports a water intensive product to another country, it exports water in virtual form. Virtual water trade as both a policy instrument and practical means to balance the local, national and global water budget has received much attention in recent years. Several studies have been conducted by researchers from various disciplines including engineers, economists and demographers. The aim of this paper is to improve the statistical characterization of the virtual water flow networks by suggesting a statistical modeling approach for examining their stochastic properties.

Spatial Clustering of Multivariate Data Using Weighted MAX-SAT

Incorporating geographical constraints is one of the main challenges of spatial clustering. In this paper we propose a new algorithm for clustering of spatial data using a conjugate Bayesian model and weighted MAX-SAT solvers. The fast and flexible Bayesian model is used to score promising partitions of the data. However, the partition space is huge and it cannot be fully searched, so here we propose an algorithm that naturally incorporates the geographical constraints to guide the search over the space of partitions. We illustrate our proposed method on a simulated dataset of social indexes.