The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Different interpolation methods express different assumptions. potentiomap retains available fit information and upstream condition text so those assumptions can be reviewed.
library(potentiomap)
data("synthetic_wells")
points <- ps_make_points(
synthetic_wells, "x", "y", "gw_elevation", "well_id", "EPSG:26916"
)
result <- suppressWarnings(ps_interpolate(
points, methods = c("IDW", "TPS", "OK", "UK"), grid_res = 400,
return = "result", support = TRUE, support_max_distance = 1000
))
summary(result)
#> potentiomap interpolation summary
#> observations: 32
#> methods: IDW, TPS, OK, UK
#> minimum maximum
#> IDW 164.2422 170.7040
#> TPS 161.4859 174.6270
#> OK 163.8829 171.5570
#> UK 160.1756 172.2497
#> captured conditions: 9
result$conditions
#> method type class
#> 1 IDW message simpleMessage
#> 2 TPS warning potentiomap_tps_gcv_boundary_warning
#> 3 TPS message simpleMessage
#> 4 TPS message simpleMessage
#> 5 TPS message simpleMessage
#> 6 TPS message simpleMessage
#> 7 OK warning potentiomap_kriging_convergence_warning
#> 8 OK message simpleMessage
#> 9 UK message simpleMessage
#> text
#> 1 [inverse distance weighted interpolation]
#> 2 TPS GCV selected lambda 1.81248e-05 at a search boundary; inspect sensitivity and prediction support.
#> 3 Warning:
#> 4 Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
#> 5 (GCV) Generalized Cross-Validation
#> 6 minimum at right endpoint lambda = 1.812476e-05 (eff. df= 30.40003 )
#> 7 No convergence after 200 iterations: try different initial values?
#> 8 [using ordinary kriging]
#> 9 [using universal kriging]IDW records its power and neighbor limit. When TPS selects its smoothing parameter, supported generalized-cross-validation information is retained. Ordinary and universal kriging retain empirical and fitted variogram objects, their fitted parameters, and method-attributed conditions.
ps_diagnostics(result, "TPS")[c(
"selection_mode", "selected_lambda", "effective_degrees_of_freedom",
"selected_at_search_boundary", "return_status"
)]
#> $selection_mode
#> [1] "GCV"
#>
#> $selected_lambda
#> [1] 1.812476e-05
#>
#> $effective_degrees_of_freedom
#> [1] 30.40003
#>
#> $selected_at_search_boundary
#> [1] TRUE
#>
#> $return_status
#> [1] "success_with_warning"
ps_diagnostics(result, "UK")[c(
"coordinate_scaling", "model_matrix_rank", "condition_number",
"observed_range", "predicted_range",
"predicted_range_to_observed_range_ratio", "return_status"
)]
#> $coordinate_scaling
#> [1] "center_scale"
#>
#> $model_matrix_rank
#> [1] 6
#>
#> $condition_number
#> [1] 4.383233
#>
#> $observed_range
#> [1] 7.38
#>
#> $predicted_range
#> [1] 12.07408
#>
#> $predicted_range_to_observed_range_ratio
#> [1] 1.636054
#>
#> $return_status
#> [1] "success"Quadratic-drift UK centers and scales coordinates by default. The condition number, prediction-range ratio, and overshoot thresholds are heuristic review flags, not formal acceptance tests. A warning does not automatically discard a surface. Rank deficiency or nonfinite model components can make the requested fit impossible, in which case the function returns a classed error rather than another interpolation method.
| support_class | cells | percent |
|---|---|---|
| supported | 47 | 42.727273 |
| outside_training_hull | 55 | 50.000000 |
| beyond_maximum_distance | 0 | 0.000000 |
| outside_mask | 0 | 0.000000 |
| prediction_unavailable | 0 | 0.000000 |
| multiple_limitations | 8 | 7.272727 |
head(result$support$records)
#> cell inside_training_hull nearest_observation_distance
#> 1 1 FALSE 1205.6344
#> 2 2 FALSE 853.3371
#> 3 3 FALSE 565.6149
#> 4 4 FALSE 481.3068
#> 5 5 FALSE 668.8667
#> 6 6 FALSE 643.4787
#> within_maximum_distance inside_mask finite_prediction support_class
#> 1 FALSE TRUE TRUE multiple_limitations
#> 2 TRUE TRUE TRUE outside_training_hull
#> 3 TRUE TRUE TRUE outside_training_hull
#> 4 TRUE TRUE TRUE outside_training_hull
#> 5 TRUE TRUE TRUE outside_training_hull
#> 6 TRUE TRUE TRUE outside_training_hull
#> support_reason
#> 1 outside_training_hull;beyond_maximum_distance
#> 2 outside_training_hull
#> 3 outside_training_hull
#> 4 outside_training_hull
#> 5 outside_training_hull
#> 6 outside_training_hulldistance <- result$support$rasters[["nearest_observation_distance"]]
par(bg = "white")
terra::plot(
distance, col = hcl.colors(64, "YlGnBu"),
main = "Local observation support: nearest-well distance"
)
terra::plot(
terra::convHull(points), add = TRUE, border = "#9b3d2f",
lwd = 2, lty = 2
)
terra::plot(points, add = TRUE, pch = 21, bg = "white", cex = 0.75)
legend(
"topleft", legend = "Training convex hull", lty = 2, lwd = 2,
col = "#9b3d2f", bty = "n"
)
The convex hull is a description of the training network, not an aquifer boundary. Nearest-observation distances require projected coordinates by default because longitude and latitude degrees are not linear ground-distance units. Predictions are classified rather than automatically blanked.
The same support rasters can classify sections of modeled contours. Criteria are supplied explicitly rather than inferred from universal distances.
contours <- ps_contours(result$surfaces$IDW, interval = 1)
classified <- suppressWarnings(ps_contour_support(
contours, support = result$support,
supported_distance = 500, approximate_distance = 1200
))
knitr::kable(classified$summary)| contour_level | support_class | segment_count | total_line_length | retained_line_length | removed_line_length | |
|---|---|---|---|---|---|---|
| 7 | 165 | supported | 1 | 1316.53639 | 1316.53639 | 0 |
| 1 | 165 | approximate | 1 | 20.88435 | 20.88435 | 0 |
| 8 | 166 | supported | 1 | 1805.69040 | 1805.69040 | 0 |
| 2 | 166 | approximate | 2 | 1876.34622 | 1876.34622 | 0 |
| 9 | 167 | supported | 1 | 2979.87771 | 2979.87771 | 0 |
| 3 | 167 | approximate | 1 | 871.59269 | 871.59269 | 0 |
| 10 | 168 | supported | 1 | 3091.10331 | 3091.10331 | 0 |
| 4 | 168 | approximate | 2 | 874.56085 | 874.56085 | 0 |
| 11 | 169 | supported | 1 | 3342.05486 | 3342.05486 | 0 |
| 5 | 169 | approximate | 2 | 1511.22085 | 1511.22085 | 0 |
| 13 | 169 | unsupported | 1 | 200.32308 | 200.32308 | 0 |
| 12 | 170 | supported | 1 | 2252.61368 | 2252.61368 | 0 |
| 6 | 170 | approximate | 3 | 3475.69115 | 3475.69115 | 0 |
See the contour-support threshold comparison for maps showing how tighter, broader, and network-relative distance criteria change the same contour lines.
Distance-derived contour classes describe local observation support. They are not statistical confidence intervals. An identified uncertainty raster can be used as a separate criterion, but its meaning and units must be supplied and it is not silently resampled or treated as interchangeable with distance.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.