Fertility evolution in Italy has shown a deep drop in 1995, and up to now the fertility rate is considered among the lowest in the world. The empirical distribution of the age-specific fertility rates is characterized by a decreasing tendency of the maximum fertility rate and a simultaneous increase of the corresponding mother’s age. This tendency has been stimulating recent contributions in modelling and forecasting. The aim of this paper is to propose a dynamic model for describing and predicting the evolution of the Italian age-specific fertility rates over time. In particular, a well-documented model, such as a Gamma function, slightly modified in order to include time-varying stochastic parameters, is used to describe the systematic and macroscopic variations of the age-specific fertility rates over time, while a nonparametric geostatistical model is applied to describe the correlated residuals at microscopic level. Finally, predictions for the variable under study are provided and main empirical evidences of the temporal evolution for different mother’s ages are discussed.

Several studies have demonstrated that skilled human capital is a key resource for the economic growth of a territory, since it helps to increase productivity, competitiveness and sustainability over time. The aim of this paper is to model the probability of working for university graduates three years after degree, taking into account the effectiveness and coherence of a degree with respect to the labour market. Hence, first of all, a multilevel binary logit model for measuring the probability of working will be discussed. Then, a multilevel multinomial model suitable to predict the probability of the possible job status, such as unemployed/unsteady employed/steady employed, will be further proposed. The ISTAT microdata regarding the Italian survey on the graduates' employment conditions, will be used.

The sector of yachting and, in particular, nautical tourism, has reported in recent decades, a strong upward trend, which would constitute one of the leading sectors of the international economy. On the basis of this assumption there exists the need to implement forms of strategic planning, to strengthen the ability of a port area to retain regular users, attract new customer segments and raise the standard of quality for pleasure boating services. In the literature, there are currently no specific tools and methodologies for the study of the quality of services provided inMarinas. In this paper, an ad hoc analogical model for the harbour management is proposed, in order to interpret, detect and estimate the qualitative aspects of a boating performance. The reliability of the analogical model for the Pleasure Boater-Satisfaction (acronym PBS) evaluation is tested through a sample survey, carried out in the period August-September 2010 on a sample of 394 permanent and in transit boaters in 21 ports located in the Apulia region.

Multivariate analysis has been applied in order to assess the variables that influence behaviour and attitudes of visitors of a food and wine event. Stepwise logistic regression is also used and the most parsimonious set of predictors that are most effective in predicting the choice of purchase are considered

In the recent years, the interest in the quality measurement of a specific health care service has increased. In particular, patient satisfaction ratings are used as health care quality markers and then as profitable competitive tools for health care management. On the other hand, perceived quality of health care services has a great influence on patient behaviors. In this context, statistical tools and techniques for assessing patients satisfaction in hospitals and other health care organizations are very useful. In this paper, a case study on the service quality provided to the long-term cancer patients and the relationship between doctors and patients has been discussed. In particular, a questionnaire has been submitted to a sample of long-term cancer patients, who follow a therapy at some hospitals belonging to the district of Lecce (Apulia Region). Several dimensions of perceived service quality including tangible aspects, reliability, empathy (doctor-patient human relations) and hospital organization have been considered. Statistical methodologies for customer satisfaction have been used in order to identify the service quality factors that are important to patients. Moreover, multivariate statistical analysis has been performed in order to identify the service critical aspects to be improved and to determine significant associations between the selected dimensions and patients satisfaction.

Nel presente capitolo saranno analizzati i decessi per tumori maligni nel periodo 1981- 2012, focalizzando l’attenzione dapprima sull’intera area del Grande Salento e, successivamente, sulle Province che lo compongono. Inoltre, sar`a condotta l’analisi statistica descrittiva del tasso di mortalità, rilevandone l’evoluzione temporale per l’intero periodo considerato. Successivamente, si fornir`a uno studio approfondito sulla mortalit`a per neoplasie all’apparato respiratorio e suoi annessi, che rappresenta una problematica sempre pi`u allarmante e significativamente correlata con differenti fattori di rischio (quali ad esempio, l’inquinamento atmosferico, le esposizioni sul luogo di lavoro o nel luogo di residenza). Tale analisi sar`a realizzata a scopi previsivi, avvalendosi di appropriate tecniche geostatistiche per il dominio temporale e spaziale. Le elaborazioni statistiche saranno effettuate sui dati forniti, a livello disaggregato, dall’ISTAT.

In the recent years, the rapid increase in mobile phone and Internet usage, has determined various benefits to their users, such as social relations and new ways of communication (virtual interactions by email, chat or instant messaging). On the other hand, an abuse of these technologies might cause adverse health effects (i.e. social isolation and other forms of psychological disorders), especially on young people, which is considered to be at high risk for pathological and addictive technologies use. In this paper, a case study on Internet and mobile phone habits among adolescents, is discussed. In particular, a questionnaire has been submitted to a sample of students, with ages ranging from 11 to 13 years old, attending some schools belonging to the districts of Lecce and Brindisi (Apulia Region). A cluster analysis combined with a binary logistic regression has been applied, in order to: a) outline the profile of teenagers that overuse Internet and mobile phone, b) assess the relationships among behaviours and attitudes of teenagers, with reference to the above-mentioned technologies.

Tests on properties of space-time covariance functions. Tests on symmetry, separability and for assessing different forms of non-separability are available. Moreover tests on some classes of covariance functions, such that the classes of product-sum models, Gneiting models and integrated product models have been provided.

Sono anni che si discute sul cambiamento climatico, insieme alle tematiche riguardanti la disponibilità di acqua, la qualità ambientale (in particolare, la qualità dell’aria), l’energia rinnovabile e le dinamiche degli ecosistemi. Questi problemi, anche se sono di grande interesse, sono spesso scollegati dalla Fisica, dalla Chimica e dalla Matematica che risultano essere gli strumenti fondamentali per il loro studio e la loro comprensione. Questo volume è destinato a fornire una descrizione sintetica dei processi di base e dei fenomeni naturali che si registrano nell’area dello Ionio, così come alle tendenze climatiche e/o alle modifiche che sono state rilevate nei parametri critici, quale la pioggia. Pertanto, sono affrontati, a livello introduttivo, i temi delle dinamiche atmosferiche, meteorologiche e climatiche, delle dinamiche oceanografiche e della tettonica nell’area geografica del Mar Ionio (in particolare, nelle Isole Ioniche e nella Penisola della Puglia).

Non-separable models are receiving a lot of attention, since they are more flexible to handle empirical covariance functions showed up in applications. When phenomena can not be described by physical laws and their space-time covariance models can not be obtained as solutions of partial differential equations, it is advisable to choose an appropriate class of spacetime covariance models for the given data set, on the basis of the main characteristics, such as full symmetry, separability, behavior at the origin, anisotropy aspects, as well as type of non separability and asymptotic behavior of the empirical covariance function. In particular, a particular attention will be turned on the type of non separability of a space-time covariance function and variability along space and time exhibited by the spatial and temporal marginal covariances. Moreover, a technique for testing some classes of covariance functions, as well as applications to the classes of Rodrigues and Diggle models (Rodrigues and Diggle, 2010), product-sum models (De Iaco et al., 2001), Gneiting models (Gneiting, 2002), integrated product models (De Iaco et al., 2002; Ma, 2003) and Cressie-Huang models (Cressie and Huang, 1999) are also provided. A case study on an environmental variable is presented.

The book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial information in support of decision systems. Over the last few years there has been a considerable interest in these tools and in the role they can play in spatial planning and environmental modelling. One of the earliest and most famous definition of spatial planning was “a geographical expression to the economic, social, cultural and ecological policies of society”: borrowing from this point of view, this text shows how an interdisciplinary approach is an effective way to an harmonious integration of national policies with regional and local analysis. A wide range of spatial models and techniques is, also, covered: spatial data mining, point processes analysis, nearest neighbor statistics and cluster detection, Fuzzy Regression model and local indicators of spatial association; all of these tools provide the policy-maker with a valuable support to policy development. Chapter 4 Abstract: The environmental risk assessment involves the analysis of complex phenomena. Different kinds of information, such as environmental, socio-economic, political and institutional data, are usually collected. In this chapter, spatio-temporal geostatistical analysis is combined with the use of a Geographic Information System (GIS): the integration between geostatistical tools and GIS enables the identification and the visualization of alternative scenarios regarding a phenomenon under study and supports the environmental risk management. A case study on environmental data measured at different monitoring stations in the southern part of Apulia Region (South of Italy), called Grande Salento, is discussed. Sample data concerning daily averages of PM10, Wind Speed and Atmospheric Temperature, are used for stochastic prediction, through space–time indicator kriging. Kriging results are implemented in a GIS and a 3D representation of the spatio-temporal probability maps is proposed.

In questo volume si propongono i risultati di un’accurata indagine - finanziata nell’ambito del Programma delle Attività Culturali per il triennio 2013/2015 della Regione Puglia - svolta nel 2014 dal Gruppo di Ricerca di Statistica del Dipartimento di Scienze dell’Economia dell’Università del Salento. Attraverso la somministrazione di un questionario rivolto a cittadini pugliesi di varie fasce d’età e la successiva elaborazione dei risultati, sono stati analizzati i comportamenti e gli orientamenti dei salentini in tema di fruizione di concerti, preferenze rispetto al genere musicale, acquisto di dischi e merchandising, partecipazione a festival di breve e lunga durata, disponibilità a spostarsi fuori provincia per assistere ai vari eventi (live, raduni, fiere di settore, happening vari ...). Inoltre, sono stati evidenziati i punti di forza e criticità relativi alla fruizione dell’offerta musicale dal vivo nel nostro territorio, allo scopo di identificare le problematiche che ne ostacolano il consumo.

Il Mercatino del Gusto, manifestazione enogastronomica organizzata a Maglie (in Provincia di Lecce) dall’1 al 5 agosto 2012 e giunta alla XIII edizione, costituisce per la Regione Puglia un’occasione per i numerosi visitatori di scoprire i prodotti della tradizione alimentare pugliese ed apprezzare le bellezze architettoniche locali, nonchè una risorsa per lo sviluppo sia economico che socio-culturale del territorio. Nel presente lavoro, vengono analizzati i principali risultati conseguiti nell’ambito dell’indagine campionaria condotta al fine di valutare le opinioni, le abitudini ed il livello di soddisfazione dei visitatori e degli espositori presenti alla manifestazione. Inoltre, sono individuati gli strumenti di pubblicizzazione dell’evento ed i punti di forza e di debolezza della manifestazione, allo scopo di fornire, agli amministratori locali, gli elementi per porre in essere scelte volte a migliorare le successive edizioni dell’evento, nonché programmare interventi pubblici volti a rivalutare il territorio pugliese e promuovere le aziende che realizzano prodotti tipici di elevata qualità.

Assessing environmental quality usually requires the observation of two or more correlated variables, which are measured in several points of the study area. Sometimes, the characteristic of interest is sparsely sampled over the area, then it is convenient to incorporate some auxiliary variables, correlated with the variable of interest, into the estimation procedure. Indeed they carry relevant information for the variable being estimated, especially if they are more densely available over the domain. In this paper three different spatial interpolation approaches have been used in order to obtain spatial predictions of the variable of interest characterized by a severe lack of data.

The quality assessment is a relevant issue in the strategic management of public health sector. The goal of this analysis is to investigate about the perception of the health quality system for long-term cancer patients. The data of interest have been collected during a survey conducted to long-term cancer patients who follow an oncological therapy in a Public Hospital. Exploratory Factorial Analysis (EFA) is developed and a Structural Equation Model (SEM) is proposed. The model describes the service quality, as perceived by the patients, which is influenced by four important factors such as tangible aspects, reliability, empathy (doctor-patient human relations) and hospital organization.

An environmental data set often concerns different correlated variables measured at some locations of the study area and for several time points. In this case, the data set presents a multivariate spatio-temporal structure; therefore appropriate modeling techniques which take into account the spatio-temporal relationships among the variables are needed. The space-time LCM (ST-LCM) based on admissible spatiotemporal models may successfully capture the spatio-temporal behaviour of the phenomena under study and can be used for prediction purposes. After a brief presentation of the spatio-temporal multivariate geostatistical framework, a case study is proposed and the following aspects are considered: 1. estimating the spatio-temporal interrelationships among the variables of interest and, consequently, identifying the basic hidden components in space and in time which characterize the same variables; the simultaneous diagonalization-based method is applied to several matrix variograms in order to detect the basic independent components which contribute to define the multivariate correlation structure of the observed variables (De Iaco et al., 2013); 2. modeling the spatio-temporal correlation among the variables under study by using the ST-LCM (De Iaco et al., 2005); in this step, the basic models at the selected scales of spatio-temporal variability have been properly chosen after the inspection of the non separability index computed for the basic components (De Iaco and Posa, 2013); 3. spatio-temporal cokriging performed by a modified version of GSLib routine to obtain prediction, over the study area, for the variable of interest. Note that the ST-LCM used in this paper is based on mixture models, i.e. the ST-LCM has been fitted by selecting different classes of spatio-temporal correlation measures, related to different scales of spatiotemporal variability.

This article provides a review of recent advances in modeling spatio-temporal multivariate data. It focuses then on the linear coregionalization model (LCM) which is still widely used in geostatistics and on the choice of it starting from data. Advantages and drawbacks are highlighted and different tests for checking the LCM hypothesis are briefly discussed.

Particulate matter (PM) is an air pollutant comes from vehicular traffic, industrial activities and street dust, or from the atmosphere, by transformation of the gaseous emissions. In recent years the interest in the health effects of this pollutant have increased, since high concentration levels in urban area have been measured. Several studies suggest an association between fine particulate air pollution and the increase of the mortality rate. In particular, PM up to 10 micrometers in size (PM10) could cause negative health effects such as respiratory illness or cardiovascular problems. Hence, the analysis of temporal evolution of this pollutant could be useful in decision-making process for environmental policy. Typically, in time series analysis, the Box-Jenkins methodology is widely applied and the autocorrelation function (ACF) is used as a standard exploratory tool to identify the model structure . In this context, the use of geostatistical techniques could also be convenient, nevertheless these techniques are usually applied to analyze, through the variogram, spatial relationships among sample data measured at some locations in a domain and to predict the corresponding spatial phenomena.

Box-Jenkins methodology (1976) is commonly applied for time series analysis. Using this approach, sample autocorrelation and partial functions (ACF and PACF, respectively) are conventionally inspected in order to identify the most appropriate model which describes the temporal evolution of the process under study. The fitted model is subsequently used for prediction purposes. Opposite to the above ACF and PACF based-method, the variogram represents the basic tool in Linear Geostatistics to face a variety of inferential problems (Chilés and Delfiner, 1999; Journel and Huijbregts, 1981; Matheron, 1963). In this context, detection of a parametric model for the process under study gives way to the estimation and modeling of the variogram in order to perform predictions of the analyzed variable at unsampled points. This paper aims to illustrate the importance and convenience of variogram-based exploratory and prediction techniques to perform a complete analysis of a time series, even in presence of a periodic behaviour. In particular an extensive case study regarding the time series of PM10 daily concentrations registered at a monitoring station located in an area with high risk of particle pollution, is faced through the following steps: a) identification of trends and periodicity exhibited by data, b) estimation of missing values, c) predictions of the PM10 concentrations at time points following the last available observation, d) estimation of the distribution function. Regarding the computational aspects, a modified version of the GSLib kriging routine (Deutsch and Journel, 1998) has been used to define appropriate temporal search neighborhoods for interpolation and prediction purposes.

Complex formalism is often convenient to describe, in a compact and unified way, a vectorial data set in two dimensions, such as wind field, electromagnetic field, as well as measurements out-coming from any two dimensional vectorial field. This representation is rarely considered in Geostatistics, although interesting applications can be found in environmental sciences and meteorology. In such a case, some practical issues related to the prediction step have to be faced. In this paper, some essential aspects on complex formalism, such as fitting a complex covariance function and predicting a complex-valued random field through an ad-hoc GSLib routine, are given. An environmental application to wind data has been furnished.

In questo lavoro, si intende ripercorrere i processi valutativi che hanno riguardato la ricerca italiana dell’Area 13, i criteri utilizzati e le ripercussioni sui contributi derivanti da specifici interessi di ricerca. Si conclude con una proposta di nuovi criteri di valutazione in grado di supportare processi valutativi trasparenti, basati su principi noti ex-ante, volti a valorizzare gli svariati interessi di ricerca, quelli di ampio respiro e quelli di nicchia.

Radon is known to be the main contributor to natural background radiation exposure and the second leading cause of lung cancer after smoking. Thus, radon prediction maps are strategic tools to support decisions regarding environmental and human health protection. In this paper, the convenience of using multivariate geostatistical methods to study the spatial distribution of radon soil concentration and assess high risk areas has been highlighted. A case study on sample data concerning radon-222 concentrations and covariates derived from a geographical information system (i.e. permeability, lithology, fault and polje) in Lecce district (Southern Italy) has been discussed. Geostatistical techniques, such as indicator-cokriging and indicator kriging for conditional probability analysis, have been applied in order to classify areas according to different radon levels and to provide probability maps of radon risk. Moreover, geostatistical bootstrap for quantifying spatial uncertainty has been performed.

Radon (Rn) is a potentially toxic gas in soil which may affect human health. Assessing Rn levels in soil gas usually requires enormous efforts in terms of time and costs, since the sampling protocol is very complex. In most cases, the variable under study is sparsely sampled over the domain and this could affect the reliability of the spatial predictions. For this reason, it is useful to incorporate, into the estimation procedure, some auxiliary variables, correlated with the in soil gas Rn concentrations (primary variable) and more densely available over the domain. On the basis of this latter aspect, it is even better if the covariates are derived from a geographical information system (GIS). In this article, the Rn sampling protocol used during a measurement campaign planned over a risk area is described and the process of deriving GIS covariates considered as secondary information for predicting the primary variable is clarified. Then, multivariate modeling and prediction of the Rn concentrations over the domain of interest are discussed and a comparative study regarding the performance of the prediction procedures is presented. Rn prone areas are also analyzed with respect to urban and school density. All these aspects can clearly support decisions on environmental and human safeguard.

Il presente volume è il risultato finale di un'attività di ricerca svolta dal Gruppo di Statistica dell’Università del Salento, nell’ambito del Progetto “Sistema Informativo Statistico per le Aree Mercatali”, approvato dal Consorzio Universitario Interprovinciale Salentino (C.U.I.S.), co-finanziato dalla Camera di Commercio di Lecce, dal Consorzio Operatori su Aree Pubbliche di Lecce e dal Dipartimento di Scienze dell’Economia dell’Università del Salento. L’analisi statistica e l’utilizzo di un sistema informativo territoriale a supporto delle politiche di gestione strategica del territorio, quale il GIS (Geographic information system) e il WebGis, rappresentano il giusto approccio verso una modernizzazione dell’attività amministrativa locale.

Un sistema informativo geografico, pi`u comunemente noto con l’espressione anglosassone Geographic Information System (GIS), ha lo scopo di acquisire, gestire e analizzare dati in un contesto spaziale. Esso rappresenta un valido supporto alla pianificazione territoriale e alla gestione di informazioni quali-quantitative complesse caratterizzate da una componente geografica. L’esigenza di condividere ed utilizzare l’informazione geografica ed il crescente sviluppo delle tecnologie di comunicazione, hanno determinato negli ultimi anni l’evoluzione e l’utilizzo dei WebGIS, ovvero di GIS implementati sul web. Tale strumento rende semplice ed immediata la consultazione delle informazioni archiviate nel GIS, anche da parte di utenti non specializzati nell’utilizzo di tecnologie informatiche. In questo capitolo, vengono presentati gli avanzamenti del GIS integrato in un WebGIS per il monitoraggio ambientale, gi`a proposto a livello prototipale nell’ambito del Progetto Sviluppi della Geostatistica multivariata per l’analisi dei dati ambientali nello spazio e nello spazio-tempo realizzato nell’anno 2011. Inoltre, dopo aver identificato gli episodi di trasporto di polvere africana, mediante l’analisi delle traiettorie all’indietro delle masse d’aria provenienti dalle regioni desertiche ed aver verificato l’esistenza di scenari favorevoli per il trasporto di polveri, avvalendosi delle mappe aerosol e delle immagini satellitari, si fornirà una quantificazione del contributo netto dovuto agli episodi di polveri africane.

In many environmental sciences, the available information concern several correlated variables observed at some locations of the domain of interest and over a certain period of time. In this context, multivariate spatial-temporal data might exhibit an spatial anisotropy and a temporal trend. Then appropriate modeling and prediction techniques for multivariate spatial-temporal data are necessary. In this paper, a case study with an anisotropic space-time coregionalization model is discussed. Some critical steps of the fitting procedure are highlighted

Nelle scienze ambientali l'evoluzione spazio-temporale di un fenomeno è spesso il risultato del comportamento simultaneo di diverse variabili correlate tra loro. In tale contesto è opportuno ricorrere ad adeguati modelli stocastici e ad appropriate tecniche di analisi geostatistica multivariata nello spazio e nello spazio-tempo per garantire previsioni attendibili. Nel volume sono introdotti i concetti fondamentali della Geostatistica multivariata spazio-temporale, i modelli di coregionalizzazione lineare, le tecniche di previsione spazio-temporale multivariata, nonché una nuova procedura di adattamento del modello di coregionalizzazione lineare spazio-temporale. Inoltre viene dedicata un’ampia discussione allo studio dell'inquinamento atmosferico nel Grande Salento. Infine, le potenzialità dei GIS per analisi di tipo ambientale e il legame di tali sistemi con la Geostatistica sono dimostrate mediante l’implementazione di un GIS per la rete di monitoraggio ambientale nel Grande Salento.

In many environmental sciences, several correlated variables are observed at some locations of the domain of interest and over a certain period of time. In this context, appropriate modeling and prediction techniques for multivariate space–time data as well as interactive software packages are necessary. In this paper, a new automatic procedure for fitting the space–time linear coregionalization model (ST-LCM) using the product–sum variogram model is discussed. This procedure, based on the simultaneous diagonalization of the sample matrix variograms, allows the identification of the ST-LCM parameters in a very flexible way. The fitting process is analytically described by a main flow chart and all steps are specified by four subprocedures. An application of this procedure is illustrated through a case study concerning the daily concentrations of three air pollutants measured in an urban area. Then the fitted space–time coregionalization model is applied to predict the variable of interest using a recent GSLib routine, named “COK2ST.”

La misurazione della qualità dei servizi, sia nel settore dei servizi privati che, recentemente, in quello dei servizi pubblici, rappresenta uno strumento fondamentale al fine di favorire e garantire lo sviluppo sostenibile di un territorio. Nel presente lavoro, dopo alcuni cenni sui modelli ServQual e ServPerf, quali strumenti di rilevazione della qualità di un servizio, saranno presentati i risultati di un’indagine campionaria effettuata in un Comune della Provincia di Lecce, riguardante la qualità del servizio di raccolta differenziata dei rifiuti. Successivamente, saranno discussi gli strumenti statistici utilizzati per misurare il livello di soddisfazione degli intervistati per il servizio in esame; infine sarà presentato un WebGis per la gestione dei rifiuti urbani.

Traditional simulation methods that are based on some form of kriging are not sensitive to the presence of strings of connectivity of low or high values. They are particularly inappropriate in many earth sciences applications, where the geological structures to be simulated are curvilinear. In such cases, techniques allowing the reproduction of multiple-point statistics are required. The aim of this paper is to point out the advantages of integrating such multiple-statistics in a model in order to allow shape reproduction, as well as heterogeneity structures, of complex geological patterns to emerge. A comparison between a traditional variogram-based simulation algorithm, such as the sequential indicator simulation, and a multiple-point statistics algorithm (e.g., the single normal equation simulation) is presented. In particular, it is shown that the spatial distribution of limestone with meandering channels in Lecce, Italy is better reproduced by using the latter algorithm. The strengths of this study are, first, the use of a training image that is not a fluvial system and, more importantly, the quantitative comparison between the two algorithms. The paper focuses on different metrics that facilitate the comparison of the methods used for limestone spatial distribution simulation: both objective measures of similarity of facies realizations and high-order spatial cumulants based on different third- and fourth-order spatial templates are considered.