1,721,054 research outputs found
La nuova geografia della crescita
No full textIl presente volume nasce nell’ambito di un vasto progetto di ricerca sulle regioni alpine, sollecitato dalla Trento School of Management che, superando le visioni stereotipate della montagna come luogo marginale rispetto allo sviluppo, ha messo in luce le caratteristiche di territori che partecipano appieno della crescita del paese, come pure delle sue difficoltà e contraddizioni. L’affermazione dell’economia della conoscenza, ha cambiato in modo sostanziale le logiche della localizzazione offrendo nuove opportunità, ma al tempo stesso presentando nuove problematiche per i territori montani per i quali, oltre al venir meno delle condizioni di marginalità, sembra che sia anche leggibile una differente capacità di inserirsi nelle nuove condizioni dello sviluppo. L’idea di fondo del presente volume è di leggere lo sviluppo regionale nel decennio 2003-2012 alla luce dei modelli di convergenza/divergenza dei livelli di benessere (misurati dal PIL pro capite) delle regioni europee. All’interno di questa cornice è possibile dare una nuova lettura del ruolo dei fattori localizzativi sulla crescita, anche attraverso una analisi delle dinamiche settoriali di gruppi di aree con differente densità abitativa, e – al loro interno – delle aree alpine, tenendo anche conto delle reciproche influenze che tendono a stabilirsi tra economie di regioni geograficamente vicine
Teaching statistics in the context of social foresight. An applied approach based on the use of an open-source software
No full textIncomplete geocoding and spatial sampling: The effects of locational errors on population total estimation
No full textModelling and predicting the spatio-temporal spread of COVID-19 in Italy
Full text linkBACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.METHODS: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.RESULTS: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.CONCLUSIONS: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished
A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models
No full textIn this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online
Measuring micro-level spatial clustering of firms while controlling for latent spatial heterogeneity
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