Merge branch 'main' of https://github.com/JuliaEarth/geospatial-data-…

…science-with-julia

02-geoviz.qmd

@@ -110,13 +110,6 @@ but any vector with the same length can be passed:
 viz(img.geometry, color = 1:length(img.A))
 ```
 
-::: {.callout-note}
-
-Any vector of Julia objects implementing the `ascolors` function can be
-passed to the `color` option.
-
-:::
-
 The `alpha` option can be used to control the transparency of each geometry
 in the domain:
 
@@ -283,6 +276,29 @@ geotable |> viewer
 
 :::
 
+## cbar
+
+Any vector of Julia objects implementing the `getcolors`
+function from the [Colorfy.jl](https://github.com/JuliaGraphics/Colorfy.jl)
+module can be passed to the `color` option of `viz`/`viz!`.
+The final vector of colors will be a function of the `colormap`
+and `colorrange` options.
+
+To reproduce this behavior in the colorbar, the framework provides
+the `cbar` function. It is similar to Makie's colorbar, but addresses
+various practical challenges with `missing` values, units, etc.
+
+```{julia}
+grid = CartesianGrid(2, 2)
+vals = [1, missing, 3, 4]
+cmap = "cividis"
+
+fig = Mke.Figure()
+viz(fig[1,1], grid, color = vals, colormap = cmap)
+cbar(fig[1,2], vals, colormap = cmap)
+fig
+```
+
 We are now equipped with a set of visualization functions that can
 really improve the speed at which we explore and analyze geospatial data.
 These functions provide a consistent set of aesthetic options that we

05-transforms.qmd

@@ -295,6 +295,9 @@ values are `missing`. This is the purpose of the `DropMissing` transform:
 georef((a=[1,missing,3], b=[4,5,6])) |> DropMissing()
 ```
 
+The `DropNaN` is an alternative to drop all rows for which the selected column values
+are `NaN`.
+
 ### Statistical
 
 The framework provides various feature transforms for statistical analysis. We will
@@ -632,7 +635,11 @@ nt |> viewer
 ```
 
 The values of the variables are aggregated at geometric intersections using a default aggregation function,
-which can be overwritten with an option.
+which can be overwritten with an option. Once the geotable is defined over a `Grid`, it is possible to
+refine or coarsen the grid with the `Downscale` and `Upscale` transforms.
+
+The `Transfer` of values to a new geospatial domain is another very useful geospatial transform. The
+`Aggregate` transform is related, but aggregates the values with given aggregation functions.
 
 ## Remarks
 
@@ -647,7 +654,7 @@ Some additional remarks:
 - One of the major advantages of transforms compared to traditional row/column
   access in data science is that they **preserve geospatial information**.
   There is no need to keep track of indices in arrays to repeatedly
-  reattach attributes to geometries.
+  reattach values to geometries.
 - Transforms can be organized into three classes---feature, geometric and
   geospatial---depending on how they operate with the `values` and the
   `domain` of the geotable:

09-geojoins.qmd

@@ -109,6 +109,11 @@ In database-style join, the predicate function `isequal` is applied to arbitrary
 columns of tables. In `geojoin`, **geometric predicate** functions are applied to
 the `geometry` column of geotables.
 
+The `tablejoin` function can be used for database-style join between a geotable
+and a simple table (e.g., `DataFrame`). The result will be a new geotable over a
+subset of geometries from the first argument. Please check the documentation for
+more details.
+
 :::
 
 ::: {.callout-note}

11-interpolation.qmd

@@ -272,6 +272,16 @@ hundreds of thousands of measurements and very large grids.
 
 :::
 
+::: {.callout-note}
+
+## Tip for all users
+
+The `InterpolateMissing` transform can be used to interpolate `missing`
+values in a geotable using the same algorithm of `InterpolateNeighbors`.
+Likewise, the `InterpolateNaN` can be used to interpolate `NaN` values.
+
+:::
+
 ## Congratulations!
 
 Congratulations on finishing **Part IV** of the book. The interpolation