Search results for "Computers in Earth Science"
showing 10 items of 323 documents
Spatio-temporal modelling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions
2021
Abstract We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of bot…
A spatial analysis of new business formation: Replicative vs innovative behaviour
2017
Abstract Using spatial econometric tools, the paper examines the spatial structure of new business formation of Italian regions during the period 2004–2007. In particular, the study empirically investigates whether new business formation in a given geographical area may be explained in terms of replicative and/or innovative entrepreneurial behaviour in each area as well as in the neighbouring areas. Additionally, the analysis focuses on the influence of urbanization on the birth of new firms. From the estimation of a Spatial Durbin Model, we find a significant degree of spatial dependence among Italian regions not only in new business formation but also in some of its determinants. We also …
Blind source separation for non-stationary random fields
2022
Regional data analysis is concerned with the analysis and modeling of measurements that are spatially separated by specifically accounting for typical features of such data. Namely, measurements in close proximity tend to be more similar than the ones further separated. This might hold also true for cross-dependencies when multivariate spatial data is considered. Often, scientists are interested in linear transformations of such data which are easy to interpret and might be used as dimension reduction. Recently, for that purpose spatial blind source separation (SBSS) was introduced which assumes that the observed data are formed by a linear mixture of uncorrelated, weakly stationary random …
Some links between conditional and coregionalized multivariate Gaussian Markov random fields
2020
Abstract Multivariate disease mapping models are attracting considerable attention. Many modeling proposals have been made in this area, which could be grouped into three large sets: coregionalization, multivariate conditional and univariate conditional models. In this work we establish some links between these three groups of proposals. Specifically, we explore the equivalence between the two conditional approaches and show that an important class of coregionalization models can be seen as a large subclass of the conditional approaches. Additionally, we propose an extension to the current set of coregionalization models with some new unexplored proposals. This extension is able to reproduc…
Windowed Etas Models With Application To The Chilean Seismic Catalogs
2015
Abstract The seismicity in Chile is estimated using an ETAS (Epidemic Type Aftershock sequences) space–time point process through a semi-parametric technique to account for the estimation of parametric and nonparametric components simultaneously. The two components account for triggered and background seismicity respectively, and are estimated by alternating a ML estimation for the parametric part and a forward predictive likelihood technique for the nonparametric one. Given the geographic and seismological characteristics of Chile, the sensitivity of the technique with respect to different geographical areas is examined in overlapping successive windows with varying latitude. A different b…
Deducing self-interaction in eye movement data using sequential spatial point processes
2016
Eye movement data are outputs of an analyser tracking the gaze when a person is inspecting a scene. These kind of data are of increasing importance in scientific research as well as in applications, e.g. in marketing and man-machine interface planning. Thus the new areas of application call for advanced analysis tools. Our research objective is to suggest statistical modelling of eye movement sequences using sequential spatial point processes, which decomposes the variation in data into structural components having interpretation. We consider three elements of an eye movement sequence: heterogeneity of the target space, contextuality between subsequent movements, and time-dependent behaviou…
Spatio-temporal small area surveillance of the COVID-19 pandemic
2022
Abstract The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number R t , even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating a…
Spatio-temporal statistical methods in environmental and biometrical problems
2017
This is the editorial letter for the Special Issue dedicated to the VIII International Workshop on Spatio-temporal Modelling (METMAVIII) which took place in Valencia (Spain) from 1 to 3 June 2016, and to the second Galician-Portuguese meeting of Biometry, with applications to Health Sciences, Ecology and Environmental Sciences (BIOAPP2016) held in Santiago de Compostela (Spain), 30–2 July 2016. This special issue summarises and discusses selected peer-reviewed contributions related to spatial and spatio-temporal statistical methodologies comprising both new methodological approaches and a wide range of applications related to environmental and biometrical problems. Point processes, lattice …
Assessing local differences between the spatio-temporal second-order structure of two point patterns occurring on the same linear network
2021
Abstract We introduce Local Indicators of Spatio-Temporal Association (LISTA) functions on linear networks and use them to build a statistical test for local second-order structure. This allows to identify differences in the spatio-temporal clustering behaviour of two point patterns, a point pattern of interest and a background one, both occurring on the same linear network. We assess the performance of the testing procedure for local second-order structure through simulation studies under a variety of scenarios that also account for different generating point processes. We show that the proposed local test is able to correctly identify the spatio-temporal difference in the local second-ord…
Influence of stress-level due to self-weight on the hydraulic conductivity of permeable concrete for geotechnical applications
2022
Permeable concrete is effectively used in many fields of civil and environmental engineering, including some geotechnical applications such as drainage piles, deep drainage trenches, and as a permeable backfill material for retaining walls. The principal requirements that a pervious concrete must have for geotechnical applications are those that simultaneously guarantee enough hydraulic conductivity and good filter properties against internal erosion of the soil in which the drains are realised. These requirements can be effectively achieved through special mix-designs in which a non-negligible amount of sand is added to the aggregates, unlike the permeable concretes used for example in the…