Modeling the spatial dynamics of economic models

The advances that have been characterizing spatial econometrics in recent years are mostly theoretical and have not found an extensive empirical application yet. In this work we aim at supplying a review of the main tools of spatial econometrics and to show an empirical application for one of the most recently introduced estimators. Despite the numerous alternatives that the econometric theory provides for the treatment of spatial (and spatiotemporal) data, empirical analyses are still limited by the lack of availability of the correspondent routines in statistical and econometric software. Spatiotemporal modeling represents one of the most recent developments in spatial econometric theory and the finite sample properties of the estimators that have been proposed are currently being tested in the literature. We provide a comparison between some estimators (a quasi-maximum likelihood, QML, estimator and some GMM-type estimators) for a fixed effects dynamic panel data model under certain conditions, by means of a Monte Carlo simulation analysis. We focus on different settings, which are characterized either by fully stable or quasi-unit root series. We also investigate the extent of the bias that is caused by a non-spatial estimation of a model when the data are characterized by different degrees of spatial dependence. Finally, we provide an empirical application of a QML estimator for a time-space dynamic model which includes a temporal, a spatial and a spatiotemporal lag of the dependent variable. This is done by choosing a relevant and prolific field of analysis, in which spatial econometrics has only found limited space so far, in order to explore the value-added of considering the spatial dimension of the data. In particular, we study the determinants of cropland value in Midwestern U.S.A. in the years 1971-2009, by taking the present value model (PVM) as the theoretical framework of analysis.

The firm. Techno-organizational changes, industrial relations and performances. An enquiry on Reggio Emilia local industrial system

It is not unknown that the evolution of firm theories has been developed along a path paved by an increasing awareness of the organizational structure importance. From the early “neoclassical” conceptualizations that intended the firm as a rational actor whose aim is to produce that amount of output, given the inputs at its disposal and in accordance to technological or environmental constraints, which maximizes the revenue (see Boulding, 1942 for a past mid century state of the art discussion) to the knowledge based theory of the firm (Nonaka & Takeuchi, 1995; Nonaka & Toyama, 2005), which recognizes in the firm a knnowledge creating entity, with specific organizational capabilities (Teece, 1996; Teece & Pisano, 1998) that allow to sustaine competitive advantages. Tracing back a map of the theory of the firm evolution, taking into account the several perspectives adopted in the history of thought, would take the length of many books. Because of that a more fruitful strategy is circumscribing the focus of the description of the literature evolution to one flow connected to a crucial question about the nature of firm’s behaviour and about the determinants of competitive advantages. In so doing I adopt a perspective that allows me to consider the organizational structure of the firm as an element according to which the different theories can be discriminated. The approach adopted starts by considering the drawbacks of the standard neoclassical theory of the firm. Discussing the most influential theoretical approaches I end up with a close examination of the knowledge based perspective of the firm. Within this perspective the firm is considered as a knowledge creating entity that produce and mange knowledge (Nonaka, Toyama, & Nagata, 2000; Nonaka & Toyama, 2005). In a knowledge intensive organization, knowledge is clearly embedded for the most part in the human capital of the individuals that compose such an organization. In a knowledge based organization, the management, in order to cope with knowledge intensive productions, ought to develop and accumulate capabilities that shape the organizational forms in a way that relies on “cross-functional processes, extensive delayering and empowerment” (Foss 2005, p.12). This mechanism contributes to determine the absorptive capacity of the firm towards specific technologies and, in so doing, it also shape the technological trajectories along which the firm moves. After having recognized the growing importance of the firm’s organizational structure in the theoretical literature concerning the firm theory, the subsequent point of the analysis is that of providing an overview of the changes that have been occurred at micro level to the firm’s organization of production. The economic actors have to deal with challenges posed by processes of internationalisation and globalization, increased and increasing competitive pressure of less developed countries on low value added production activities, changes in technologies and increased environmental turbulence and volatility. As a consequence, it has been widely recognized that the main organizational models of production that fitted well in the 20th century are now partially inadequate and processes aiming to reorganize production activities have been widespread across several economies in recent years. Recently, the emergence of a “new” form of production organization has been proposed both by scholars, practitioners and institutions: the most prominent characteristic of such a model is its recognition of the importance of employees commitment and involvement. As a consequence it is characterized by a strong accent on the human resource management and on those practices that aim to widen the autonomy and responsibility of the workers as well as increasing their commitment to the organization (Osterman, 1994; 2000; Lynch, 2007). This “model” of production organization is by many defined as High Performance Work System (HPWS). Despite the increasing diffusion of workplace practices that may be inscribed within the concept of HPWS in western countries’ companies, it is an hazard, to some extent, to speak about the emergence of a “new organizational paradigm”. The discussion about organizational changes and the diffusion of HPWP the focus cannot abstract from a discussion about the industrial relations systems, with a particular accent on the employment relationships, because of their relevance, in the same way as production organization, in determining two major outcomes of the firm: innovation and economic performances. The argument is treated starting from the issue of the Social Dialogue at macro level, both in an European perspective and Italian perspective. The model of interaction between the social parties has repercussions, at micro level, on the employment relationships, that is to say on the relations between union delegates and management or workers and management. Finding economic and social policies capable of sustaining growth and employment within a knowledge based scenario is likely to constitute the major challenge for the next generation of social pacts, which are the main social dialogue outcomes. As Acocella and Leoni (2007) put forward the social pacts may constitute an instrument to trade wage moderation for high intensity in ICT, organizational and human capital investments. Empirical evidence, especially focused on the micro level, about the positive relation between economic growth and new organizational designs coupled with ICT adoption and non adversarial industrial relations is growing. Partnership among social parties may become an instrument to enhance firm competitiveness. The outcome of the discussion is the integration of organizational changes and industrial relations elements within a unified framework: the HPWS. Such a choice may help in disentangling the potential existence of complementarities between these two aspects of the firm internal structure on economic and innovative performance. With the third chapter starts the more original part of the thesis. The data utilized in order to disentangle the relations between HPWS practices, innovation and economic performance refer to the manufacturing firms of the Reggio Emilia province with more than 50 employees. The data have been collected through face to face interviews both to management (199 respondents) and to union representatives (181 respondents). Coupled with the cross section datasets a further data source is constituted by longitudinal balance sheets (1994-2004). Collecting reliable data that in turn provide reliable results needs always a great effort to which are connected uncertain results. Data at micro level are often subjected to a trade off: the wider is the geographical context to which the population surveyed belong the lesser is the amount of information usually collected (low level of resolution); the narrower is the focus on specific geographical context, the higher is the amount of information usually collected (high level of resolution). For the Italian case the evidence about the diffusion of HPWP and their effects on firm performances is still scanty and usually limited to local level studies (Cristini, et al., 2003). The thesis is also devoted to the deepening of an argument of particular interest: the existence of complementarities between the HPWS practices. It has been widely shown by empirical evidence that when HPWP are adopted in bundles they are more likely to impact on firm’s performances than when adopted in isolation (Ichniowski, Prennushi, Shaw, 1997). Is it true also for the local production system of Reggio Emilia? The empirical analysis has the precise aim of providing evidence on the relations between the HPWS dimensions and the innovative and economic performances of the firm. As far as the first line of analysis is concerned it must to be stressed the fundamental role that innovation plays in the economy (Geroski & Machin, 1993; Stoneman & Kwoon 1994, 1996; OECD, 2005; EC, 2002). On this point the evidence goes from the traditional innovations, usually approximated by R&D investment expenditure or number of patents, to the introduction and adoption of ICT, in the recent years (Brynjolfsson & Hitt, 2000). If innovation is important then it is critical to analyse its determinants. In this work it is hypothesised that organizational changes and firm level industrial relations/employment relations aspects that can be put under the heading of HPWS, influence the propensity to innovate in product, process and quality of the firm. The general argument may goes as follow: changes in production management and work organization reconfigure the absorptive capacity of the firm towards specific technologies and, in so doing, they shape the technological trajectories along which the firm moves; cooperative industrial relations may lead to smother adoption of innovations, because not contrasted by unions. From the first empirical chapter emerges that the different types of innovations seem to respond in different ways to the HPWS variables. The underlying processes of product, process and quality innovations are likely to answer to different firm’s strategies and needs. Nevertheless, it is possible to extract some general results in terms of the most influencing HPWS factors on innovative performance. The main three aspects are training coverage, employees involvement and the diffusion of bonuses. These variables show persistent and significant relations with all the three innovation types. The same do the components having such variables at their inside. In sum the aspects of the HPWS influence the propensity to innovate of the firm. At the same time, emerges a quite neat (although not always strong) evidence of complementarities presence between HPWS practices. In terns of the complementarity issue it can be said that some specific complementarities exist. Training activities, when adopted and managed in bundles, are related to the propensity to innovate. Having a sound skill base may be an element that enhances the firm’s capacity to innovate. It may enhance both the capacity to absorbe exogenous innovation and the capacity to endogenously develop innovations. The presence and diffusion of bonuses and the employees involvement also spur innovative propensity. The former because of their incentive nature and the latter because direct workers participation may increase workers commitment to the organizationa and thus their willingness to support and suggest inovations. The other line of analysis provides results on the relation between HPWS and economic performances of the firm. There have been a bulk of international empirical studies on the relation between organizational changes and economic performance (Black & Lynch 2001; Zwick 2004; Janod & Saint-Martin 2004; Huselid 1995; Huselid & Becker 1996; Cappelli & Neumark 2001), while the works aiming to capture the relations between economic performance and unions or industrial relations aspects are quite scant (Addison & Belfield, 2001; Pencavel, 2003; Machin & Stewart, 1990; Addison, 2005). In the empirical analysis the integration of the two main areas of the HPWS represent a scarcely exploited approach in the panorama of both national and international empirical studies. As remarked by Addison “although most analysis of workers representation and employee involvement/high performance work practices have been conducted in isolation – while sometimes including the other as controls – research is beginning to consider their interactions” (Addison, 2005, p.407). The analysis conducted exploiting temporal lags between dependent and covariates, possibility given by the merger of cross section and panel data, provides evidence in favour of the existence of HPWS practices impact on firm’s economic performance, differently measured. Although it does not seem to emerge robust evidence on the existence of complementarities among HPWS aspects on performances there is evidence of a general positive influence of the single practices. The results are quite sensible to the time lags, inducing to hypothesize that time varying heterogeneity is an important factor in determining the impact of organizational changes on economic performance. The implications of the analysis can be of help both to management and local level policy makers. Although the results are not simply extendible to other local production systems it may be argued that for contexts similar to the Reggio Emilia province, characterized by the presence of small and medium enterprises organized in districts and by a deep rooted unionism, with strong supporting institutions, the results and the implications here obtained can also fit well. However, a hope for future researches on the subject treated in the present work is that of collecting good quality information over wider geographical areas, possibly at national level, and repeated in time. Only in this way it is possible to solve the Gordian knot about the linkages between innovation, performance, high performance work practices and industrial relations.

Modelli non lineari statici e dinamici su dati microeconomici: analisi delle condizioni finanziarie delle famiglie italiane

The thesis studies the economic and financial conditions of Italian households, by using microeconomic data of the Survey on Household Income and Wealth (SHIW) over the period 1998-2006. It develops along two lines of enquiry. First it studies the determinants of households holdings of assets and liabilities and estimates their correlation degree. After a review of the literature, it estimates two non-linear multivariate models on the interactions between assets and liabilities with repeated cross-sections. Second, it analyses households financial difficulties. It defines a quantitative measure of financial distress and tests, by means of non-linear dynamic probit models, whether the probability of experiencing financial difficulties is persistent over time. Chapter 1 provides a critical review of the theoretical and empirical literature on the estimation of assets and liabilities holdings, on their interactions and on households net wealth. The review stresses the fact that a large part of the literature explain households debt holdings as a function, among others, of net wealth, an assumption that runs into possible endogeneity problems. Chapter 2 defines two non-linear multivariate models to study the interactions between assets and liabilities held by Italian households. Estimation refers to a pooling of cross-sections of SHIW. The first model is a bivariate tobit that estimates factors affecting assets and liabilities and their degree of correlation with results coherent with theoretical expectations. To tackle the presence of non normality and heteroskedasticity in the error term, generating non consistent tobit estimators, semi-parametric estimates are provided that confirm the results of the tobit model. The second model is a quadrivariate probit on three different assets (safe, risky and real) and total liabilities; the results show the expected patterns of interdependence suggested by theoretical considerations. Chapter 3 reviews the methodologies for estimating non-linear dynamic panel data models, drawing attention to the problems to be dealt with to obtain consistent estimators. Specific attention is given to the initial condition problem raised by the inclusion of the lagged dependent variable in the set of explanatory variables. The advantage of using dynamic panel data models lies in the fact that they allow to simultaneously account for true state dependence, via the lagged variable, and unobserved heterogeneity via individual effects specification. Chapter 4 applies the models reviewed in Chapter 3 to analyse financial difficulties of Italian households, by using information on net wealth as provided in the panel component of the SHIW. The aim is to test whether households persistently experience financial difficulties over time. A thorough discussion is provided of the alternative approaches proposed by the literature (subjective/qualitative indicators versus quantitative indexes) to identify households in financial distress. Households in financial difficulties are identified as those holding amounts of net wealth lower than the value corresponding to the first quartile of net wealth distribution. Estimation is conducted via four different methods: the pooled probit model, the random effects probit model with exogenous initial conditions, the Heckman model and the recently developed Wooldridge model. Results obtained from all estimators accept the null hypothesis of true state dependence and show that, according with the literature, less sophisticated models, namely the pooled and exogenous models, over-estimate such persistence.

Patterns of regional differentiation in post-Soviet Russia

This doctoral thesis aims at contributing to the literature on transition economies focusing on the Russian Federations and in particular on regional income convergence and fertility patterns. The first two chapter deal with the issue of income convergence across regions. Chapter 1 provides an historical-institutional analysis of the period between the late years of the Soviet Union and the last decade of economic growth and a presentation of the sample with a description of gross regional product composition, agrarian or industrial vocation, labor. Chapter 2 contributes to the literature on exploratory spatial data analysis with a application to a panel of 77 regions in the period 1994-2008. It provides an analysis of spatial patterns and it extends the theoretical framework of growth regressions controlling for spatial correlation and heterogeneity. Chapter 3 analyses the national demographic patterns since 1960 and provides a review of the policies on maternity leave and family benefits. Data sources are the Statistical Yearbooks of USSR, the Statistical Yearbooks of the Russian Soviet Federative Socialist Republic and the Demographic Yearbooks of Russia. Chapter 4 analyses the demographic patterns in light of the theoretical framework of the Becker model, the Second Demographic Transition and an economic-crisis argument. With national data from 1960, the theoretically issue of the pro or countercyclical relation between income and fertility is graphically analyzed and discussed, together with female employment and education. With regional data after 1994 different panel data models are tested. Individual level data from the Russian Longitudinal Monitoring Survey are employed using the logit model. Chapter 5 employs data from the Generations and Gender Survey by UNECE to focus on postponement and second births intentions. Postponement is studied through cohort analysis of mean maternal age at first birth, while the methodology used for second birth intentions is the ordered logit model.

Essays on the Empirical Analysis of Economic and Political Development in Sub-Saharan Africa

In Sub-Saharan Africa, non-democratic events, like civil wars and coup d'etat, destroy economic development. This study investigates both domestic and spatial effects on the likelihood of civil wars and coup d'etat. To civil wars, an increase of income growth is one of common research conclusions to stop wars. This study adds a concern on ethnic fractionalization. IV-2SLS is applied to overcome causality problem. The findings document that income growth is significant to reduce number and degree of violence in high ethnic fractionalized countries, otherwise they are trade-off. Income growth reduces amount of wars, but increases its violent level, in the countries with few large ethnic groups. Promoting growth should consider ethnic composition. This study also investigates the clustering and contagion of civil wars using spatial panel data models. Onset, incidence and end of civil conflicts spread across the network of neighboring countries while peace, the end of conflicts, diffuse only with the nearest neighbor. There is an evidence of indirect links from neighboring income growth, without too much inequality, to reduce the likelihood of civil wars. To coup d'etat, this study revisits its diffusion for both all types of coups and only successful ones. The results find an existence of both domestic and spatial determinants in different periods. Domestic income growth plays major role to reduce the likelihood of coup before cold war ends, while spatial effects do negative afterward. Results on probability to succeed coup are similar. After cold war ends, international organisations seriously promote democracy with pressure against coup d'etat, and it seems to be effective. In sum, this study indicates the role of domestic ethnic fractionalization and the spread of neighboring effects to the likelihood of non-democratic events in a country. Policy implementation should concern these factors.

Shareholder protection and stock market development

The dissertation contains five parts: An introduction, three major chapters, and a short conclusion. The First Chapter starts from a survey and discussion of the studies on corporate law and financial development literature. The commonly used methods in these cross-sectional analyses are biased as legal origins are no longer valid instruments. Hence, the model uncertainty becomes a salient problem. The Bayesian Model Averaging algorithm is applied to test the robustness of empirical results in Djankov et al. (2008). The analysis finds that their constructed legal index is not robustly correlated with most of the various stock market outcome variables. The second Chapter looks into the effects of minority shareholders protection in corporate governance regime on entrepreneurs' ex ante incentives to undertake IPO. Most of the current literature focuses on the beneficial part of minority shareholder protection on valuation, while overlooks its private costs on entrepreneur's control. As a result, the entrepreneur trade-offs the costs of monitoring with the benefits of cheap sources of finance when minority shareholder protection improves. The theoretical predictions are empirically tested using panel data and GMM-sys estimator. The third Chapter investigates the corporate law and corporate governance reform in China. The corporate law in China regards shareholder control as the means to the ends of pursuing the interests of stakeholders, which is inefficient. The Chapter combines the recent development of theories of the firm, i.e., the team production theory and the property rights theory, to solve such problem. The enlightened shareholder value, which emphasizes on the long term valuation of the firm, should be adopted as objectives of listed firms. In addition, a move from the mandatory division of power between shareholder meeting and board meeting to the default regime, is proposed.

Topics in Local Economic Development

This thesis contributes to the current debate in literature about local economic development by considering two different topics: quality of institutions, and the role of clusters in innovation and productivity growth. The research is built upon three papers. The first paper deals with the analysis of the effect of administrative continuity on administrative efficiency. The analysis underlines the importance of different typologies of social capital. Findings reveal a positive impact on administrative efficiency (AE) by administrative continuity (AC) when it is coupled by bridging and linking social capital. On the contrary, bonding social capital influences negatively the effect by AC on AE. The second paper investigates the spatial interaction in levels of quality of government (QoG) among European regions. Notwithstanding the largely recognised role by institutions in the design of regional policies, no study has been conducted about the mechanisms of interaction and diffusion of QoG at regional level. This research wants to overcome this knowledge gap in literature. Findings reveal a heterogeneity in spatial interaction among groups of regions, i.e. ‘leader regions’ (Northern regions) and ‘lagging regions’ (Southern regions), when considering different mechanisms of interaction (learning / imitating competition and pure competition). Moreover, the effect of wealth on the levels of QoG is nonlinear. Finally, the third paper analyses the relation among specialization and productivity within the agricultural sector. In literature, the study of clusters dynamics has long neglected agriculture. The analysis describes the changes in sectorial specialization for eight main crop groups in Italian regions (NUTS 3), assessing the existence of spatial autocorrelations by using an exploratory data analysis. Furthermore, the effect of specialization on productivity is analysed within the main crop groups using a spatial panel data model. Findings reveal a marked tendency to specialization in the Italian agriculture, and a heterogeneous effect by specialization on productivity.

Modelling spillover effects in spatial stochastic frontier analysis

In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Numerical optimization,modeling and system evaluation of a thermophotovoltaic hybrid panel

Photovoltaic (PV) solar panels generally produce electricity in the 6% to 16% efficiency range, the rest being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PVT) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. It is thus interesting to study the PVT system globally from different point of views in order to evaluate advantages and disadvantages of this technology and its possible uses. In particular in Chapter II, the development of the PVT absorber numerical optimization by a genetic algorithm has been carried out analyzing different internal channel profiles in order to find a right compromise between performance and technical and economical feasibility. Therefore in Chapter III ,thanks to a mobile structure built into the university lab, it has been compared experimentally electrical and thermal output power from PVT panels with separated photovoltaic and solar thermal productions. Collecting a lot of experimental data based on different seasonal conditions (ambient temperature,irradiation, wind...),the aim of this mobile structure has been to evaluate average both thermal and electrical increasing and decreasing efficiency values obtained respect to separate productions through the year. In Chapter IV , new PVT and solar thermal equation based models in steady state conditions have been developed by software Dymola that uses Modelica language. This permits ,in a simplified way respect to previous system modelling softwares, to model and evaluate different concepts about PVT panel regarding its structure before prototyping and measuring it. Chapter V concerns instead the definition of PVT boundary conditions into a HVAC system . This was made trough year simulations by software Polysun in order to finally assess the best solar assisted integrated structure thanks to F_save(solar saving energy)factor. Finally, Chapter VI presents the conclusion and the perspectives of this PhD work.

Kinematics of the Sicily and Calabria Subduction System from Elastic Block Modeling of GPS Data

We use data from about 700 GPS stations in the EuroMediterranen region to investigate the present-day behavior of the the Calabrian subduction zone within the Mediterranean-scale plates kinematics and to perform local scale studies about the strain accumulation on active structures. We focus attenction on the Messina Straits and Crati Valley faults where GPS data show extentional velocity gradients of ∼3 mm/yr and ∼2 mm/yr, respectively. We use dislocation model and a non-linear constrained optimization algorithm to invert for fault geometric parameters and slip-rates and evaluate the associated uncertainties adopting a bootstrap approach. Our analysis suggest the presence of two partially locked normal faults. To investigate the impact of elastic strain contributes from other nearby active faults onto the observed velocity gradient we use a block modeling approach. Our models show that the inferred slip-rates on the two analyzed structures are strongly impacted by the assumed locking width of the Calabrian subduction thrust. In order to frame the observed local deformation features within the present- day central Mediterranean kinematics we realyze a statistical analysis testing the indipendent motion (w.r.t. the African and Eurasias plates) of the Adriatic, Cal- abrian and Sicilian blocks. Our preferred model confirms a microplate like behaviour for all the investigated blocks. Within these kinematic boundary conditions we fur- ther investigate the Calabrian Slab interface geometry using a combined approach of block modeling and χ2ν statistic. Almost no information is obtained using only the horizontal GPS velocities that prove to be a not sufficient dataset for a multi-parametric inversion approach. Trying to stronger constrain the slab geometry we estimate the predicted vertical velocities performing suites of forward models of elastic dislocations varying the fault locking depth. Comparison with the observed field suggest a maximum resolved locking depth of 25 km.

Big data analytics and modeling for Human Mobility

The fast development of Information Communication Technologies (ICT) offers new opportunities to realize future smart cities. To understand, manage and forecast the city's behavior, it is necessary the analysis of different kinds of data from the most varied dataset acquisition systems. The aim of this research activity in the framework of Data Science and Complex Systems Physics is to provide stakeholders with new knowledge tools to improve the sustainability of mobility demand in future cities. Under this perspective, the governance of mobility demand generated by large tourist flows is becoming a vital issue for the quality of life in Italian cities' historical centers, which will worsen in the next future due to the continuous globalization process. Another critical theme is sustainable mobility, which aims to reduce private transportation means in the cities and improve multimodal mobility. We analyze the statistical properties of urban mobility of Venice, Rimini, and Bologna by using different datasets provided by companies and local authorities. We develop algorithms and tools for cartography extraction, trips reconstruction, multimodality classification, and mobility simulation. We show the existence of characteristic mobility paths and statistical properties depending on transport means and user's kinds. Finally, we use our results to model and simulate the overall behavior of the cars moving in the Emilia Romagna Region and the pedestrians moving in Venice with software able to replicate in silico the demand for mobility and its dynamic.

Data assimilation for advanced cross-scale unstructured-grid ocean modeling.

The coastal ocean is a complex environment with extremely dynamic processes that require a high-resolution and cross-scale modeling approach in which all hydrodynamic fields and scales are considered integral parts of the overall system. In the last decade, unstructured-grid models have been used to advance in seamless modeling between scales. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas have been developed only recently and need significant advancements. Here, we link the unstructured-grid ocean modeling to the variational data assimilation methods. In particular, we show results from the modeling system SANIFS based on SHYFEM fully-baroclinic unstructured-grid model interfaced with OceanVar, a state-of-art variational data assimilation scheme adopted for several systems based on a structured grid. OceanVar implements a 3DVar DA scheme. The combination of three linear operators models the background error covariance matrix. The vertical part is represented using multivariate EOFs for temperature, salinity, and sea level anomaly. The horizontal part is assumed to be Gaussian isotropic and is modeled using a first-order recursive filter algorithm designed for structured and regular grids. Here we introduced a novel recursive filter algorithm for unstructured grids. A local hydrostatic adjustment scheme models the rapidly evolving part of the background error covariance. We designed two data assimilation experiments using SANIFS implementation interfaced with OceanVar over the period 2017-2018, one with only temperature and salinity assimilation by Argo profiles and the second also including sea level anomaly. The results showed a successful implementation of the approach and the added value of the assimilation for the active tracer fields. While looking at the broad basin, no significant improvements are highlighted for the sea level, requiring future investigations. Furthermore, a Machine Learning methodology based on an LSTM network has been used to predict the model SST increments.

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.