QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
#qgis #geostatistics #kriging #prediction #variogram
github.com/geosaber/geo...
This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
#qgis #geostatistics #kriging #prediction #variogram
github.com/geosaber/geo...
GitHub - geosaber/geostat: QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools - geosaber/geostat
github.com
February 14, 2025 at 12:44 AM
QGIS Processing Toolbox tool for Variogram Modeling and Ordinary Kriging using GSTools
This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
#qgis #geostatistics #kriging #prediction #variogram
github.com/geosaber/geo...
This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.
#qgis #geostatistics #kriging #prediction #variogram
github.com/geosaber/geo...
Added a variogram to the app!
I'm experimenting with showing the non-stationarity of correlations in a random slopes model.
I'm experimenting with showing the non-stationarity of correlations in a random slopes model.
December 20, 2024 at 4:14 PM
Added a variogram to the app!
I'm experimenting with showing the non-stationarity of correlations in a random slopes model.
I'm experimenting with showing the non-stationarity of correlations in a random slopes model.
New on CRAN: gcf (0.1.0). View at https://CRAN.R-project.org/package=gcf
gcf: Generalized Covariate Field
Generates generalized covariate field (GCF) variables from spatial covariates observed at projected coordinates, and selects a stable subset of them for geospatial prediction. For each input covariate the method builds spatial-pattern features (local indicator of spatial association, local Geary's c, log local variance, rank quantile entropy, geocomplexity, log scale variance, local variogram exponent, and signed z-score and median absolute deviation outlier strengths over a series of buffer radii) and neighbourhood-distribution features (buffer-wise quantiles of the covariate values surrounding each location), reduces the buffer and quantile sweeps to a compact set of interpretable functional summaries, and selects variables by random forest importance combined with spatial-block stability resampling and group voting. The GCF method is positioned as prediction-oriented feature construction: its output feeds any downstream regression learner. Methods are described in Song (2026) <<a href="https://doi.org/10.1080%2F13658816.2026.2729719" target="_top">doi:10.1080/13658816.2026.2729719</a>>.
CRAN.R-project.org
September 26, 2026 at 7:33 PM
New on CRAN: gcf (0.1.0). View at https://CRAN.R-project.org/package=gcf
interpolate this variogram on the Tree of Woe
September 26, 2023 at 7:56 PM
interpolate this variogram on the Tree of Woe
Sampling isn’t logistics. It reflects assumptions about spatial structure.
When the variogram is unknown, we rely on pilots, analog data, and space-filling designs.
When the variogram is unknown, we rely on pilots, analog data, and space-filling designs.
March 3, 2026 at 6:38 PM
Sampling isn’t logistics. It reflects assumptions about spatial structure.
When the variogram is unknown, we rely on pilots, analog data, and space-filling designs.
When the variogram is unknown, we rely on pilots, analog data, and space-filling designs.
Thank you for the suggestions! I've added effective sample size now. Will likely add a variogram when time allows
December 19, 2024 at 12:15 PM
Thank you for the suggestions! I've added effective sample size now. Will likely add a variogram when time allows
Correction: variogram. An example is in hbiostat.org/rmsc/long and a more flexible diagnostic for longitudinal data is in hbiostat.org/rmsc/markov
7 Modeling Longitudinal Responses using Generalized Least Squares – Regression Modeling Strategies
hbiostat.org
January 10, 2026 at 1:16 PM
Correction: variogram. An example is in hbiostat.org/rmsc/long and a more flexible diagnostic for longitudinal data is in hbiostat.org/rmsc/markov
Spatio-temporal kriging is an interpolation method that models autocorrelation using a variogram. It uses semi-variance which can be interpreted inversely of spatial autocorrelation (i.e. ↑semi-variance,↓spatial autocorrelation). The value is a function of distance and in this case, also time.
June 17, 2026 at 1:13 PM
Spatio-temporal kriging is an interpolation method that models autocorrelation using a variogram. It uses semi-variance which can be interpreted inversely of spatial autocorrelation (i.e. ↑semi-variance,↓spatial autocorrelation). The value is a function of distance and in this case, also time.
I found an example and adapted it: seems very straightforward (you do have to pick a spatial covariance/variogram model and range ...) gist.github.com/bbolker/2b7c...
February 25, 2025 at 4:32 PM
I found an example and adapted it: seems very straightforward (you do have to pick a spatial covariance/variogram model and range ...) gist.github.com/bbolker/2b7c...
The empirical variogram is calculated based on the data. Then a variogram model is chosen according to how well it approximates the empirical variogram. {gstat} provides 3 diagnostic plots. In the multi-line, the spread represents temporal autocorr and y-axis — spatial autocorr. Sum Metric is best.
June 17, 2026 at 1:13 PM
The empirical variogram is calculated based on the data. Then a variogram model is chosen according to how well it approximates the empirical variogram. {gstat} provides 3 diagnostic plots. In the multi-line, the spread represents temporal autocorr and y-axis — spatial autocorr. Sum Metric is best.
I just came across a geostatistics MOOC where in the second module they introduce data types (categorical vs ordinal etc), and in the third one they introduce the semi-variogram. WTF?
You must have clear the level of your audience! A resource cannot be directed to ANY level of expertise!
You must have clear the level of your audience! A resource cannot be directed to ANY level of expertise!
August 6, 2025 at 9:38 AM
I just came across a geostatistics MOOC where in the second module they introduce data types (categorical vs ordinal etc), and in the third one they introduce the semi-variogram. WTF?
You must have clear the level of your audience! A resource cannot be directed to ANY level of expertise!
You must have clear the level of your audience! A resource cannot be directed to ANY level of expertise!
link 📈🤖
Bayesian Kriging Approaches for Spatial Functional Data () Functional kriging approaches have been developed to predict the curves at
unobserved spatial locations. However, most existing approaches are based on
variogram fittings rather than constructing hierarchical statistical models.
T
Bayesian Kriging Approaches for Spatial Functional Data () Functional kriging approaches have been developed to predict the curves at
unobserved spatial locations. However, most existing approaches are based on
variogram fittings rather than constructing hierarchical statistical models.
T
December 17, 2024 at 1:33 AM
link 📈🤖
Bayesian Kriging Approaches for Spatial Functional Data () Functional kriging approaches have been developed to predict the curves at
unobserved spatial locations. However, most existing approaches are based on
variogram fittings rather than constructing hierarchical statistical models.
T
Bayesian Kriging Approaches for Spatial Functional Data () Functional kriging approaches have been developed to predict the curves at
unobserved spatial locations. However, most existing approaches are based on
variogram fittings rather than constructing hierarchical statistical models.
T
neuromapr generates surrogate brain maps preserving the spatial autocorrelation structure of the original. Eight null model methods --- variogram matching, Moran spectral randomization, spin tests, and more. Three lines of code to get a spatially-corrected p-value.
GitHub - LCBC-UiO/neuromapr: Spatial Null Models and Transforms for Brain Map Comparison
Spatial Null Models and Transforms for Brain Map Comparison - LCBC-UiO/neuromapr
github.com
February 15, 2026 at 11:00 AM
neuromapr generates surrogate brain maps preserving the spatial autocorrelation structure of the original. Eight null model methods --- variogram matching, Moran spectral randomization, spin tests, and more. Three lines of code to get a spatially-corrected p-value.
July 30, 2026 at 10:25 PM
With a variogram model, the kriging model can be fit. I used mlr3spatiotempcv to also choose a ML model along with tuning a local kriging model. First time around I gave the ML models calendar features and the raw projected coordinates. The result is this paint brush effect on the prediction surface
June 17, 2026 at 1:13 PM
With a variogram model, the kriging model can be fit. I used mlr3spatiotempcv to also choose a ML model along with tuning a local kriging model. First time around I gave the ML models calendar features and the raw projected coordinates. The result is this paint brush effect on the prediction surface
Hydrology Paper of the Day
@jsacerot
@AjamiHoori
on parameter importance for streamflow prediction: application of the Variogram Analysis of Response Surfaces (VARS) method; the SWAT model applied to three catchments in different regions; and spatial discretization and scale.
@jsacerot
@AjamiHoori
on parameter importance for streamflow prediction: application of the Variogram Analysis of Response Surfaces (VARS) method; the SWAT model applied to three catchments in different regions; and spatial discretization and scale.
The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales
Semi-distributed hydrologic models have been extensively utilized for simulating watershed-scale hydrologic processes, given their fast execution time…
www.sciencedirect.com
December 18, 2024 at 1:36 AM
Hydrology Paper of the Day
@jsacerot
@AjamiHoori
on parameter importance for streamflow prediction: application of the Variogram Analysis of Response Surfaces (VARS) method; the SWAT model applied to three catchments in different regions; and spatial discretization and scale.
@jsacerot
@AjamiHoori
on parameter importance for streamflow prediction: application of the Variogram Analysis of Response Surfaces (VARS) method; the SWAT model applied to three catchments in different regions; and spatial discretization and scale.
1/2 This looks fantastic. Suggestions: (1) Especially when there are only random intercepts, compute the effective sample size as in www.tandfonline.com/doi/abs/10.1...; (2) show the implied variogram (approximate by absolute difference in correlation vs. time gap and visually assess isotropism).
The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method
Correlated data frequently arise in contexts such as, for example, repeated measures and meta-analysis. The amount of information in such data depends not only on the sample size, but also on the ...
www.tandfonline.com
December 11, 2024 at 10:53 PM
1/2 This looks fantastic. Suggestions: (1) Especially when there are only random intercepts, compute the effective sample size as in www.tandfonline.com/doi/abs/10.1...; (2) show the implied variogram (approximate by absolute difference in correlation vs. time gap and visually assess isotropism).
The heatmap has time on the y-axis and distance on the x-axis with cells colored by semi-variance. The noise of the empirical variogram (sample, observed) makes this more difficult to judge the models, but I'd still have to go w/the Sum-Metric (or Simple Sum Metric). (btw lowest SSE is Sum-Metric)
![source - https://cran.r-project.org/web/packages/gstat/vignettes/st.pdf
full code - https://ercbk.github.io/Data-Science-Notebook/qmd/geospatial-spat-temp.html#sec-geo-sptemp-krig-var-ex
# observed noisy, metric abrupt temporally, prod/sep gradual in space and time
ggplot(tib_plot, aes(x = factor(floor(avgDist)), y = timelag, fill = gamma)) +
geom_tile() +
scale_fill_gradientn(
colors = rev(scico::scico(100, palette = "glasgow")),
na.value = "white"
) +
scale_x_discrete(
# skips every other value to prevent overlap of the labels
breaks = function(x) x[seq(1, length(x), by = 2)]
) +
facet_wrap(~ model, nrow = 2) +
labs(
title = "Spatio-Temporal Semi-Variance Surfaces",
subtitle = "Model comparison between the best fit of each type of model",
x = "distance (km)",
y = "time lag (days)",
fill = "Semi-\nVariance"
) +
theme_notebook(
strip.background = element_blank(),
panel.grid = element_blank(),
legend.key.height = unit(1.95, "cm"),
)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:dcv32lv6xgstilwv3klodild/bafkreig6sybo64p7uvdinizc4xvnkcspvrd3acrd5q3edukbjetoua5iwq)
June 17, 2026 at 1:13 PM
The heatmap has time on the y-axis and distance on the x-axis with cells colored by semi-variance. The noise of the empirical variogram (sample, observed) makes this more difficult to judge the models, but I'd still have to go w/the Sum-Metric (or Simple Sum Metric). (btw lowest SSE is Sum-Metric)
AI agents can now quantify spatial correlation and detect directional patterns using the new variogram-modeling-analysis capability.
September 7, 2026 at 9:28 PM
AI agents can now quantify spatial correlation and detect directional patterns using the new variogram-modeling-analysis capability.
August 14, 2026 at 6:22 PM
Shuang Hu, Johan Segers: Estimating the H\"usler--Reiss variogram matrix by clipped moments https://arxiv.org/abs/2607.19672 https://arxiv.org/pdf/2607.19672 https://arxiv.org/html/2607.19672
July 23, 2026 at 6:50 AM
Shuang Hu, Johan Segers: Estimating the H\"usler--Reiss variogram matrix by clipped moments https://arxiv.org/abs/2607.19672 https://arxiv.org/pdf/2607.19672 https://arxiv.org/html/2607.19672
New on CRAN: MBGapp (0.1.0). View at https://CRAN.R-project.org/package=MBGapp
MBGapp: Interactive 'shiny' Application for Model-Based Geostatistics
Provides an interactive 'shiny' application for teaching and applied analysis of geostatistical data. Users can explore spatial data, assess spatial correlation through the empirical variogram, fit model-based geostatistical models for continuous, prevalence and count outcomes, produce spatial predictions, and download reports. The methodology follows the model-based geostatistics framework of Diggle and Giorgi (2019, ISBN:9781138732353).
CRAN.R-project.org
July 10, 2026 at 9:56 PM
New on CRAN: MBGapp (0.1.0). View at https://CRAN.R-project.org/package=MBGapp