Update docs

docs/src/data.md

@@ -69,7 +69,7 @@ table = DataFrame(T=rand(25), P=rand(25))
 We can georeference this table based on a given set of points:
 
 ```@example data
-georef(table, rand(Point{2}, 25)) |> plot
+georef(table, rand(Point, 25)) |> plot
 ```
 
 or alternatively, georeference it on a 5x5 regular grid (5x5 = 25 samples):
@@ -103,7 +103,7 @@ georef((T=T, P=P)) |> plot
 Alternatively, we can interpret the entries of the named tuple as columns in a table:
 
 ```@example data
-georef((T=vec(T), P=vec(P)), rand(Point{2}, 25)) |> plot
+georef((T=vec(T), P=vec(P)), rand(Point, 25)) |> plot
 ```
 
 ### Files

docs/src/domains.md

@@ -31,7 +31,7 @@ PointSet
 ```
 
 ```@example domains
-pset = PointSet(rand(Point{3}, 100))
+pset = PointSet(rand(Point, 100))
 
 viz(pset)
 ```

docs/src/transforms.md

@@ -147,7 +147,7 @@ UniqueCoords
 
 ```@example transforms
 # point set with repeated points
-p = rand(Point{2}, 50)
+p = rand(Point, 50)
 Ω = georef((Z=rand(100),), [p; p])
 ```
 

docs/src/variography/theoretical.md

@@ -210,7 +210,7 @@ To illustrate the concept, consider the following 2D data set:
 using Random # hide
 Random.seed!(2000) # hide
 
-points = rand(Point{2}, 50) |> Scale(100)
+points = rand(Point, 50, crs=Cartesian2D) |> Scale(100)
 geotable = georef((Z=rand(50),), points)
 
 viz(geotable.geometry, color = geotable.Z)