describe variables

tutorial-content/content/working-with-shapefiles.md

@@ -57,7 +57,10 @@ to generate a collection of points at the center of each grid
 cell. This approach can be used without generating an $A$ matrix,
 but the matrix method improves efficiency.
 
-As an example, you generate these points like so:
+As an example, suppose that you have a grid with a longitudinal (zonal)
+dimension from `longitude0` to `longitude1` and a latitudinal (meridional) dimension
+from `latitude0` to `latitude1`, with equal spacing of `gridwidth` for
+both dimensions. You can generate a full list of grid cell points like so:
 
 `````{tab-set}
 ````{tab-item} R
@@ -84,6 +87,11 @@ pts = pd.DataFrame(np.array(np.meshgrid(longitudes, latitudes)).T.reshape(-1, 2)
 ````
 `````
 
+Often you can get the longitude and latitude values for the grid cells
+directly from your weather dataset. In this case, replace the steps to
+generate `longitudes` and `latitudes` variables by hand with directly
+loading those values.
+
 Now, you can iterate through each region, and get a list of all of the
 points within each region. Here's how you would do that with the
 `PBSmapping` library in R:
@@ -173,4 +181,4 @@ Matching observations by name can be very time-consuming. These problems even ex
 
 2. Use string matching. However, in this case, you will need to inspect all of the matches to make sure that they are correct.
 
-3. Construct “translation functions” for each dataset, which map the regional names in that dataset to a canonical list of region names. For example, choose the names in one dataset as a canonical list, and name the matching functions as `<dataset>2canonical` and `canonical2<dataset2>`.
+3. Construct “translation functions” for each dataset, which map the regional names in that dataset to a canonical list of region names. For example, choose the names in one dataset as a canonical list, and name the matching functions as `<dataset>2canonical` and `canonical2<dataset2>`.