Deploying to gh-pages from @ 5a407e5 🚀

Author: Cidree

articles/aw_interpolation.html

@@ -111,8 +111,8 @@ memo_diff <- round(as.numeric(temp$mem_alloc[2] / temp$mem_alloc[1]),1)
 time_diff <- (1 - round(as.numeric(temp$median[1] / temp$median[2]),2))*100
 ```
 
-In this example working with 1 million points, {duckspatial} was 58%
-faster and used 5.4 times less memory than {sf}. Not bad.
+In this example working with 1 million points, {duckspatial} was 57%
+faster and used 5.5 times less memory than {sf}. Not bad.
 
 ``` r
 ggplot(data = df_bench_join) +
@@ -171,7 +171,7 @@ memo_diff <- round(as.numeric(temp$mem_alloc[2] / temp$mem_alloc[1]),1)
 time_diff <- (1 - round(as.numeric(temp$median[1] / temp$median[2]),2))*100
 ```
 
-In this example working with 1 million points, {duckspatial} was 73%
+In this example working with 1 million points, {duckspatial} was 76%
 faster and used 2.7 times less memory than {sf}.
 
 plot
@@ -238,6 +238,8 @@ df_bench_distance <- lapply(
 #> disabled.
 #> Warning: Some expressions had a GC in every iteration; so filtering is
 #> disabled.
+#> Warning: Some expressions had a GC in every iteration; so filtering is
+#> disabled.
 
 
 # calculate difference in performance
@@ -249,7 +251,7 @@ time_diff <- (1 - round(as.numeric(temp$median[1] / temp$median[2]),2))*100
 ```
 
 In this example calculating the distance between 10K points,
-{duckspatial} was 85% faster, but used 2 times more memory than {sf}.
+{duckspatial} was 86% faster, but used 2 times more memory than {sf}.
 Mind you that {sf} is still more efficient when calculating Euclidean
 distances.
 

articles/benchmark.html

@@ -6,7 +6,7 @@ articles:
   benchmark: benchmark.html
   duckspatial: duckspatial.html
   spatial_joins: spatial_joins.html
-last_built: 2026-01-08T18:28Z
+last_built: 2026-01-09T18:53Z
 urls:
   reference: https://cidree.github.io/duckspatial/reference
   article: https://cidree.github.io/duckspatial/articles