Merge pull request #21 from r4space/master

Removed note about talking to Lovelace et al as that's now in Issue #10

episodes/01-spatial-data-structures-formats.Rmd

@@ -56,7 +56,6 @@ The downsides of raster data are
 
 Satellite imagery is probably the most complex raster representation you will work with. Satellites like Landsat capture information at multiple wavelengths, from both human-visible and infrared bands. Final products are split into specific wavelength ranges, so to make a colour image of the land surface, one may have to combine red, green, and blue datasets. 
 
-_nb shld talk to Lovelace et al about borrowing some images from - https://bookdown.org/robinlovelace/geocompr/spatial-class.html#raster-data, for both r and v_
 
 ## Vector
 
@@ -83,8 +82,6 @@ The downsides of vector data are
 
 Vector datasets are in use in many industries besides geospatial. For instance, computer graphics are largely vector-based, although the data structures in use tend to join points using arcs and complex curves rather than straight lines. Computer-aided design (CAD) is also vector-based. The difference is, again, that geospatial datasets are accompanied by information tying their features to real-world locations. Its time to talk about how that works.  
 
-_<worth mentioning arc-node topology yet? probs leave until specific vector lesson>_  
-
 ## Coordinate Reference Systems  
 
 A data structure cannot be considered geospatial unless it is accompanied by spatial reference system (SRS) information, in a format that geospatial applications can use to display and manipulate the data correctly. SRS information connects data to the Earth's surface using a mathematical model.