Spatio-temporal geostatistical modeling for French fertility predictions

Iaco, Sandra De; Palma, Monica; Posa, D.

doi:10.1016/j.spasta.2015.10.002

Cited by 24 publications

(24 citation statements)

References 47 publications

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“…where γ st (h s , 0) and γ st (0, h t ) are the marginal spatial and temporal variograms, respectively (Equation 12). The great advantage of this type of model is that sample-based marginal adjustments are used, and only one global threshold parameter is incorporated for space-time interaction [24]. In Equation (13) the parameter k is positive and has an identity involving the global threshold, C st (0, 0) together with the spatial and temporal threshold, C s (0) and C t (0), respectively, given by:…”

Section: Spatiotemporal Variogrammentioning

confidence: 99%

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Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Medeiros

Lima

Olinda

et al. 2019

Water

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Knowing the dynamics of spatial–temporal precipitation distribution is of vital significance for the management of water resources, in highlight, in the northeast region of Brazil (NEB). Several models of large-scale precipitation variability are based on the normal distribution, not taking into consideration the excess of null observations that are prevalent in the daily or even monthly precipitation information of the region under study. This research proposes a novel way of modeling the trend component by using an inflated gamma distribution of zeros. The residuals of this regression are generally space–time dependent and have been modeled by a space–time covariance function. The findings show that the new techniques have provided reliable and precise precipitation estimates, exceeding the techniques used previously. The modeling provided estimates of precipitation in nonsampled locations and unobserved periods, thus serving as a tool to assist the government in improving water management, anticipating society’s needs and preventing water crises.

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Section: Spatiotemporal Variogrammentioning

confidence: 99%

“…The main advantage of the methodology proposed in this study is that with the space-time kriging technique, predictions can be made for unobserved locations and times [21,24]. As an example of this methodology applicability, the spatial-temporal kriging of precipitation was carried out in the period 2015 ( Figure 8).…”

Section: Componentmentioning

confidence: 99%

Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Medeiros

Lima

Olinda

et al. 2019

Water

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“…This component was determined here by an ordinary least squares (OLS) regression model. The residual ε included the following three components: spatial, temporal, and interaction-based components [1]. For modeling purposes, it was assumed that these three components were second-order stationary, mutually independent, and spatially isotropic.…”

Section: Spatiotemporal Modelmentioning

confidence: 99%

“…The deterministic component m(s, t) was estimated using multiple linear regression. Several studies have used this type of regression method to model the trend component in spatiotemporal geostatistics [1,13,17,18]. In this study, the geographic coordinates (latitude and longitude) and a temporal index employed to contour the effect of the annual seasonality on the precipitation were considered covariates.…”

Section: Components Of the Trendmentioning

confidence: 99%

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Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

Medeiros

Lima

Olinda

et al. 2019

Water

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The purpose of this study was to provide a detailed framework to use the spatiotemporal kriging to model the space-time variability of precipitation data in Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, highly variable distribution of rainfall in space and time. In this region, it is common to find high rates of rainfall at locations adjacent to those with no record of rain. Paraíba experiences localized periods of drought within rainy seasons and distinct precipitation patterns among the state’s mesoregions. The mean precipitation values observed at several irregularly spaced rain gauge stations from 1994 to 2014 showed remarkable variations among the mesoregions in Paraíba throughout the year. As a consequence of this behavior, there is a need to model the rainfall distribution jointly with space and time. A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba. The rainfall data indicate intense spatial and temporal variabilities that directly affect the water resources of the entire region. The results provide a detailed spatial analysis of sectors experiencing precipitation conditions ranging from a scarcity to an excess of rainfall. The present study should help drive future research into spatiotemporal rainfall patterns across all of NEB.

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30 Years of space–time covariance functions

Porcu

Furrer

Nychka

2020

WIREs Computational Stats

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In this article, we provide a comprehensive review of space–time covariance functions. As for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit d‐dimensional sphere. We start by providing background information about (spatial) covariance functions and their properties along with different types of covariance functions. While we focus primarily on Gaussian processes, many of the results are independent of the underlying distribution, as the covariance only depends on second‐moment relationships. We discuss properties of space–time covariance functions along with the relevant results associated with spectral representations. Special attention is given to the Gneiting class of covariance functions, which has been especially popular in space–time geostatistical modeling. We then discuss some techniques that are useful for constructing new classes of space–time covariance functions. Separate treatment is reserved for spectral models, as well as to what are termed models with special features. We also discuss the problem of estimation of parametric classes of space–time covariance functions. An outlook concludes the paper. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Multivariate Analysis

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Spatio-temporal geostatistical modeling for French fertility predictions

Cited by 24 publications

References 47 publications

Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Space–Time Kriging of Precipitation: Modeling the Large-Scale Variation with Model GAMLSS

Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil

30 Years of space–time covariance functions

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