3,547 research outputs found
The impact of the Covid-19 pandemic on Italian mobility
No full textFrancesco Finazzi and Alessandro Fassò use location data collected by an earthquake-monitoring app to gauge compliance with lockdown measures in Italy
EM estimation of the dynamic coregionalization model with varying coefficients
No full textThe satellites from NASA's Earth Science Project Division, like AURA, produce data for the concentration of various airborne pollutants. Calibrating satellite data using ground level monitoring networks and other meteorological and land characterizing variables is mandatory. To do this, it is important to use an approach which is able to manage large datasets coming from different sources, structural missingness and spatial and temporal correlation.
In this paper, we extend the Dynamic Coregionalization Model introduced in Fassó and Finazzi (2011) to the case of space-time varying coefficients in order to increase the model flexibility and to make it suitable for large regions such as Europe
The dynamic coregionalization model in air quality risk assessment
No full textOne major role of environment agencies is to provide concise indicators about a country's air quality and its impact on population health. On the one hand, such environmental indicators must be easily understood by the public and on the other, they should be useful to compare air quality over different years. Indeed, it is important to assess whether any actions undertaken to improve air quality have been successful or not (Scott, 2007).
When the sets of airborne pollutants measured at different monitoring sites are different, it is not always clear how to define daily and yearly indicators for the whole country or how to evaluate their uncertainty. Moreover, it is not straightforward to compare across years when in each year, the structure of the monitoring network (sites included), the quantity of missing data and meteorological conditions differ.
In order to define a sound statistical framework within which to address the above issues, the dynamic coregionalization model introduced by Fassò and Finazzi (2010 and 2011) is considered. Based on temporal and spatial latent variables, the model is a multivariate hierarchical space-time model able to cope with missing data and with data coming from heterogeneous monitoring networks, where different pollutants may be measured at different sites.
When only the temporal latent variable is considered, the model can be used to derive a daily air quality indicator for the entire region of interest. If different pollutants have different impacts on population health, then a weighted indicator can be evaluated. When a set of covariates and the latent spatial variable are also included, the model can provide either daily or yearly average concentration maps for the pollutants considered.
By applying non-parametric bootstrap techniques, quantities such as the probability that a pollutant exceeds a threshold level L over a period of time at a particular point in space, the expected number of days that L has been exceeded during the year and the probability that L has been exceeded for at least N days can be evaluated. These results can then be related to population figures in order to assess population exposure and risk. This approach is illustrated using Scottish air quality data for 6 pollutants for the year 2009
Real-time detection of earthquakes through a smartphone-based sensor network
No full textThe Earthquake Network project implements a world-wide smartphone-based sensor network for the detection of earthquakes. The accelerometric sensor onboard each smartphone is used to detect vibrations which are immediately reported to a server. The server analyses the information coming from the entire network and when a quake is detected it is notified to all smartphone users in quasi real-time. In this work we propose and compare two solutions to the detection problem. One solution is based on a likelihood approach and the other is based on filtering
Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data
No full textThe information content of multivariable spatio-temporal data depends on the underlying spatial sampling scheme. The most informative case is represented by the isotopic configuration where all variables are measured at all sites. The opposite case is the completely heterotopic case where different variables are observed only at different locations. A well known approach to multivariate spatio-temporal modelling is based on the linear coregionalization model (LCM).In this paper, the maximum likelihood estimation of the heterotopic spatio-temporal model with spatial LCM components and temporal dynamics is developed. In particular, the computation of the estimates is based on the EM algorithm and two solutions are proposed: one is based on the more cumbersome exact maximization of the a posteriori expected log likelihood and the other is an approximate closed-form solution. Their properties are assessed in terms of bias and efficiency through an example of air quality dinamic mapping using satellite data and a Monte Carlo simulation campaign based on a large data set
The dynamic coregionalization model with application to air quality remote sensing
No full textIn this paper, we discuss the dynamic coregionalization model and its capability for model selection inference and interpretation in relation to spatio- temporal dynamic calibration and mapping of daily concentration of airborne particulate matter. To do this, we consider the problem of joint modelling ground level concentration data and satellite measurements of aerosol optical thickness (AOT), which are rarely available. The maximum likelihood estimation for the large data set related to the ”padano-veneto” region, North Italy, with missing data is covered by the stable EM algorithm and implemented on a small size computer cluster
A varying coefficients space-time model for ground and satellite air quality data over Europe
No full textThis paper introduces a flexible space-time data fusion model based on latent variables and varying coefficients that can be used to map air quality over large areas such as countries or continents.
The model is able to handle point data from ground level monitoring networks and pixel data from remote sensing. As a case study, the model is used to dynamically map the nitrogen dioxide concentration over Europe during 2009
Spatio-temporal modeling and remote sensing for a common European air quality assessment method
No full textEarthquake monitoring using volunteer smartphone-based sensor networks
No full textWe introduce here the Earthquake Network project which implements a world-wide smartphone-based sensor network for the detection of earthquakes. Thanks to the accelerometric sensor, smartphones possibly detect the waves of a quake and report the event to a cloud computing infrastructure. In this work, we propose a solution to the detection problem based on statistical modelling the arrival times of the smartphone reports.
Keywords. Dynamic networks; Real time monitoring; Android; F
Bayesian source detection and parameter estimation of a plume model based on sensor network measurements: Discussion
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