This is the latest in my series of screencasts demonstrating how to use the tidymodels packages, from just getting started to tuning more complex models. Today’s screencast walks through how to use spatial resampling for evaluating a model, with this week’s #TidyTuesday dataset on geographic data. 🗾
Here is the code I used in the video, for those who prefer reading instead of or in addition to video.
Explore data
Geographic data is special when it comes to, well, basically everything! This includes modeling and especially evaluating models. This week’s #TidyTuesday is all about exploring spatial data for the #30DayMapChallenge this month, and especially the spData and spDataLarge packages along with the book Geocomputation with R.
Let’s use the dataset of landslides (plus not-landslide locations) in Southern Ecuador.
data("lsl", package = "spDataLarge")
landslides <- as_tibble(lsl)
landslides
## # A tibble: 350 × 8
## x y lslpts slope cplan cprof elev log10_carea
## <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 715078. 9558647. FALSE 37.4 0.0205 0.00866 2477. 2.61
## 2 713748. 9558047. FALSE 41.7 -0.0241 0.00679 2486. 3.07
## 3 712508. 9558887. FALSE 20.0 0.0390 0.0147 2142. 2.29
## 4 713998. 9558187. FALSE 45.8 -0.00632 0.00435 2391. 3.83
## 5 714308. 9557307. FALSE 41.7 0.0423 -0.0202 2570. 2.70
## 6 713488. 9558117. FALSE 52.9 0.0323 0.00703 2418. 2.48
## 7 714948. 9558347. FALSE 51.9 0.0399 0.000791 2546. 3.15
## 8 714678. 9560357. FALSE 38.5 0.0164 0.0299 1932. 3.26
## 9 714368. 9560287. FALSE 24.1 -0.0188 -0.00956 2059. 3.20
## 10 712528. 9559217. FALSE 50.5 0.0142 0.0151 1973. 2.60
## # … with 340 more rows
How are these landslides (plus not landslides) distributes in this area?
ggplot(landslides, aes(x, y)) +
stat_summary_hex(aes(z = elev), alpha = 0.6, bins = 12) +
geom_point(aes(color = lslpts), alpha = 0.7) +
coord_fixed() +
scale_fill_viridis_c() +
scale_color_manual(values = c("gray90", "midnightblue")) +
labs(fill = "Elevation", color = "Landslide?")
Create spatial resamples
In tidymodels, one of the first steps we recommend thinking about is “spending your data budget.” When it comes to geographic data, points close to each other are often similar so we don’t want to randomly resample our observations. Instead, we want to use a resampling strategy that accounts for that autocorrelation. Let’s create both resamples that are appropriate to spatial data and resamples that might work for “regular,” non-spatial data but are not a good fit for geographic data.
library(tidymodels)
library(spatialsample)
set.seed(123)
good_folds <- spatial_clustering_cv(landslides, coords = c("x", "y"), v = 5)
good_folds
## # 5-fold spatial cross-validation
## # A tibble: 5 × 2
## splits id
## <list> <chr>
## 1 <split [306/44]> Fold1
## 2 <split [256/94]> Fold2
## 3 <split [251/99]> Fold3
## 4 <split [303/47]> Fold4
## 5 <split [284/66]> Fold5
set.seed(234)
bad_folds <- vfold_cv(landslides, v = 5, strata = lslpts)
bad_folds
## # 5-fold cross-validation using stratification
## # A tibble: 5 × 2
## splits id
## <list> <chr>
## 1 <split [280/70]> Fold1
## 2 <split [280/70]> Fold2
## 3 <split [280/70]> Fold3
## 4 <split [280/70]> Fold4
## 5 <split [280/70]> Fold5
The spatialsample package currently provides one method for spatial resampling and we are interested in hearing about what other methods we should support next.
How do these resamples look? Let’s create a little helper function:
plot_splits <- function(split) {
p <- bind_rows(
analysis(split) %>%
mutate(analysis = "Analysis"),
assessment(split) %>%
mutate(analysis = "Assessment")
) %>%
ggplot(aes(x, y, color = analysis)) +
geom_point(size = 1.5, alpha = 0.8) +
coord_fixed() +
labs(color = NULL)
print(p)
}
The spatial resampling creates resamples where observations close to each other are together.
walk(good_folds$splits, plot_splits)
The regular resampling doesn’t do this; it just randomly resamples all observations.
walk(bad_folds$splits, plot_splits)
This second option is not a good idea for geographic data.
Fit and evaluate model
Let’s create a straightforward logistic regression model to predict whether a location saw a landslide based on the other characteristics like slope, elevation, amount of water flow, etc. We can estimate how well this same model fits the data both with our regular folds and our special spatial resampling.
glm_spec <- logistic_reg()
lsl_form <- lslpts ~ slope + cplan + cprof + elev + log10_carea
lsl_wf <- workflow(lsl_form, glm_spec)
doParallel::registerDoParallel()
set.seed(2021)
regular_rs <- fit_resamples(lsl_wf, bad_folds)
set.seed(2021)
spatial_rs <- fit_resamples(lsl_wf, good_folds)
How did our results turn out?
collect_metrics(regular_rs)
## # A tibble: 2 × 6
## .metric .estimator mean n std_err .config
## <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 accuracy binary 0.737 5 0.0173 Preprocessor1_Model1
## 2 roc_auc binary 0.808 5 0.0201 Preprocessor1_Model1
collect_metrics(spatial_rs)
## # A tibble: 2 × 6
## .metric .estimator mean n std_err .config
## <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 accuracy binary 0.677 5 0.0708 Preprocessor1_Model1
## 2 roc_auc binary 0.782 5 0.00790 Preprocessor1_Model1
If we use the “regular” resampling, we get a more optimistc estimate of performance which would fool us into thinking our model would perform better than it really could. The lower performance estimate using spatial resampling is more accurate because of the autocorrelation of this geographic data; observations near each other are more alike than observations far apart. With geographic data, it’s important to use an appropriate model evaluation strategy!
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