FORTRAN programs for space-time modeling

Cesare, L. De; Myers, Donald E.; Posa, D.

doi:10.1016/s0098-3004(01)00040-1

Cited by 72 publications

(44 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined spatio-temporal descriptions of soil properties are still incipient (Snepvangers et al, 2003). This approach has been used successfully in different areas (De Cesare et al, 2002;De Iaco et al, 2005;Stein et al, 1998), and constitutes a valuable statistical framework for data analysis and predictions in the space and time domain simultaneously (Kyriakidis and Journel, 1999).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of soil water tension in a tropical soil in Brazil

2006

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of soil water tension in a tropical soil in Brazil

2006

View full text Add to dashboard Cite

“…where k 1 , k 2 , and k 3 are non-negative (strictly positive for k 3 ) coefficients estimated from the sills of the spatial, temporal, and spatio-temporal semivariograms (De Cesare et al 2002). • The metric model (Dimitrakopoulos and Luo 1994):…”

Section: ð14:2þmentioning

confidence: 99%

“…The main difficulty in the practical implementation of the product-sum and sum-metric models is the inference of the sill of the ST semivariogram model, C st ð0Þ, which is most often estimated visually from the 3D plot of the experimental ST semivariogram γŝ t ðh, τÞ (e.g., De Cesare et al 2002;Heuvelink and Griffith 2010). In order to make the fitting procedure more user-friendly, the space-time sill C st ð0Þ was here computed as the following weighted average of experimental space-time semivariogram values:…”

Section: ð14:2þmentioning

confidence: 99%

Flint Drinking Water Crisis: A First Attempt to Model Geostatistically the Space-Time Distribution of Water Lead Levels

Goovaerts

2018

Handbook of Mathematical Geosciences

View full text Add to dashboard Cite

The drinking water contamination crisis in Flint, Michigan has attracted national attention since extreme levels of lead were recorded following a switch in water supply that resulted in water with high chloride and no corrosion inhibitor flowing through the aging Flint water distribution system. Since Flint returned to its original source of drinking water on October 16, 2015, the State has conducted eleven bi-weekly sampling rounds, resulting in the collection of 4,120 water samples at 819 "sentinel" sites. This chapter describes the first geostatistical analysis of these data and illustrates the multiple challenges associated with modeling the space-time distribution of water lead levels across the city. Issues include sampling bias and the large nugget effect and short range of spatial autocorrelation displayed by the semivariogram. Temporal trends were modeled using linear regression with service line material, house age, poverty level, and their interaction with census tracts as independent variables. Residuals were then interpolated using kriging with three types of non-separable space-time covariance models. Cross-validation demonstrated the limited benefit of accounting for secondary information in trend models and the poor quality of predictions at unsampled sites caused by substantial fluctuations over a few hundred meters. The main benefit is to fill gaps in sampled time series for which the generalized product-sum and sum-metric models outperformed the metric model that ignores the greater variation across space relative to time (zonal anisotropy). Future research should incorporate the large database assembled through voluntary sampling as close to 20,000 data, albeit collected under non-uniform conditions, are available at a much greater sampling density.

show abstract

“…Moreover, the R programming language (R Core Team 2017) has various packages devoted to geostatistics, such as the gstat package for geostatistical modeling, prediction and simulation (Pebesma and Wesseling 1998;Pebesma 2004;Pebesma and Bivand 2005), geoR (Ribeiro Jr. and Diggle 2001;Diggle and Ribeiro Jr. 2007), which contains functions for model-based geostatistics, as well as the geospt (Melo, Santacruz, and Melo 2015), RandomFields (Schlather, Malinowski, Menck, Oesting, and Strokorb 2015) and RGeostats (Renard, Desassis, Beucher, Ors, and Laporte 2014) packages, or ad-hoc routines for graphical interface in the ecological modeling software Bio7 (Austenfeld and Beyschlag 2012) or for exploratory spatial data analysis (Laurent, Ruiz-Gazen, and Thomas-Agnan 2012). Other contributions concern specialized routines and packages for spatio-temporal analysis (De Cesare, Myers, and Posa 2002;De Iaco, Myers, Palma, and Posa 2010;Pebesma 2012;Gabriel, Rowlingson, and Diggle 2013). However, none of the above mentioned packages provides tools for estimating and modeling the real and imaginary parts of a complex covariance function as well as for predicting complex-valued random fields.…”

Section: Introductionmentioning

confidence: 99%

The cgeostat Software for Analyzing Complex-Valued Random Fields

Iaco¹

2017

J. Stat. Soft.

View full text Add to dashboard Cite

Given a vectorial data set in two dimensions, a representation on a complex domain is often convenient. This representation is rarely considered in geostatistics, although interesting applications can be found in environmental sciences and meteorology (e.g., for wind fields). In such a case, some computational difficulties are related to the lack of software for estimating and modeling a complex covariance function, for predicting complex variables as well as for representing the output results. In this paper, the new Fortran software cgeostat for geostatistical analysis of complex-valued random fields is presented and an application is demonstrated.

show abstract

FORTRAN programs for space-time modeling

Cited by 72 publications

References 18 publications

Spatio-temporal variability of soil water tension in a tropical soil in Brazil

Spatio-temporal variability of soil water tension in a tropical soil in Brazil

Flint Drinking Water Crisis: A First Attempt to Model Geostatistically the Space-Time Distribution of Water Lead Levels

The cgeostat Software for Analyzing Complex-Valued Random Fields

Contact Info

Product

Resources

About