Francesco Finazzi

A model-based framework for air quality indices and population risk evaluation, with an application to the analysis of Scottish air quality data

The paper is devoted to the development of a statistical framework for air quality assessment at the country level and for the evaluation of the ambient population exposure and risk with respect to airborne pollutants. The framework is based on a multivariate space–time model and on aggregated indices defined at different levels of aggregation in space and time. The indices are evaluated, uncertainty included, by considering both the model outputs and the information on the population spatial distribution. The framework is applied to the analysis of air quality data for Scotland for 2009 referring to European and Scottish air quality legislation.

A comparison of clustering approaches for the study of the temporal coherence of multiple time series

Two approaches for clustering of time series have been considered. The first is a novel approach based on a modification of classic state-space modelling while the second is based on functional clustering. For the latter, both k-means and complete-linkage hierarchical clustering algorithms are adopted. The two approaches are compared using a simulation study, and are applied to lake surface water temperature for 256 lakes globally for 5 years of data, to investigate information obtained from each approach.

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalisation or mortality data. However, this approach limits the analysis to individuals characterised by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population. We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO2 concentration integrating several data sources characterised by different spatio-temporal resolution; in the second stage we link the concentration to the β2-agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.

Dynamic model-based clustering for spatio-temporal data

In many research fields, scientific questions are investigated by analyzing data collected over space and time, usually at fixed spatial locations and time steps and resulting in geo-referenced time series. In this context, it is of interest to identify potential partitions of the space and study their evolution over time. A finite space-time mixture model is proposed to identify level-based clusters in spatio-temporal data and study their temporal evolution along the time frame. We anticipate space-time dependence by introducing spatio-temporally varying mixing weights to allocate observations at nearby locations and consecutive time points with similar cluster’s membership probabilities. As a result, a clustering varying over time and space is accomplished. Conditionally on the cluster’s membership, a state-space model is deployed to describe the temporal evolution of the sites belonging to each group. Fully posterior inference is provided under a Bayesian framework through Monte Carlo Markov chain algorithms. Also, a strategy to select the suitable number of clusters based upon the posterior temporal patterns of the clusters is offered. We evaluate our approach through simulation experiments, and we illustrate using air quality data collected across Europe from 2001 to 2012, showing the benefit of borrowing strength of information across space and time.

An analysis of temporal and spatial patterns in Italian hospitalization rates for multiple diagnosis

In this paper, the Italian hospitalization database provided by the Ministry of Health is analyzed in terms of the temporal and spatial patterns of the hospitalization rates. The database covers the period 2010-2012 and the rates are evaluated for 110 Italian provinces and 18 diagnosis groups as defined by the ICD-9 classification. The analysis is based on a novel model-based clustering approach which enables clustering of spatially registered time series with respect to latent temporal patterns. The clustering result is analyzed to study the spatial distribution of the latent temporal patterns and their trend in order to identify possible critical areas in terms of increasing rates. Additionally, emerging spatial patterns may help common causes driving the hospitalization rates to be identified.

A GPU Software Library for Likelihood-Based Inference of Environmental Models with Large Datasets

Statistical environmental models are computationally intensive due to the high dimension of the data, both in space and time, and due to the inferential techniques required for parameter estimation and spatial prediction. In particular, the computational complexity of these techniques is related to matrix operations (inversion, solution of linear systems, factorization) involving large dense matrices. Recently, much attention has been paid around the possibility of taking advantage of graphics processing units (GPUs) for mathematical computation. GPUs provide a high degree of parallelism at a reasonable cost and may represent a viable alternative compared to the classic computer cluster configurations. In this work, we develop the shared library GPU4GL implementing ad-hoc linear-algebra functions running on GPUs and compare them with the standard algorithms for CPU. As an example, we apply the GPU functions of GPU4GL to make inference on a non-separable space–time model for air quality data.