cleans

01-introduction.Rmd

@@ -99,7 +99,7 @@ Nowadays such lack of geographic data is hard to imagine.
 Every smartphone has a global positioning (GPS) receiver and a multitude of sensors on devices ranging from satellites and semi-autonomous vehicles to citizen scientists incessantly measure every part of the world.
 The rate of data produced is overwhelming.
 An autonomous vehicle, for example, can generate 100 GB of data per day [@theeconomist_autonomous_2016].
-Remote sensing data from satellites has become too large to analyze the corresponding data with a single computer, leading to initiatives such as  [OpenEO](http://r-spatial.org/2016/11/29/openeo.html).
+Remote sensing data from satellites has become too large to analyze the corresponding data with a single computer, leading to initiatives such as [OpenEO](http://r-spatial.org/2016/11/29/openeo.html).
 
 This 'geodata revolution' drives demand for high performance computer hardware and efficient, scalable software to handle and extract signal from the noise, to understand and perhaps change the world.
 Spatial databases enable storage and generation of manageable subsets from the vast geographic data stores, making interfaces for gaining knowledge from them vital tools for the future.

02-spatial-data.Rmd

@@ -56,15 +56,12 @@ library(spDataLarge)   # load larger geographic data
 
 This chapter will provide brief explanations of the fundamental geographic data models: vector and raster.
 We will introduce the theory behind each data model and the disciplines in which they predominate, before demonstrating their implementation in R.
-<!-- Vector and raster models are vital to geospatial analysis [@longley_geographic_2015]. -->
 
 The *vector data model* represents the world using points, lines and polygons.
 These have discrete, well-defined borders, meaning that vector datasets usually have a high level of precision (but not necessarily accuracy as we will see in Section \@ref(units)).
 The *raster data model* divides the surface up into cells of constant size.
 Raster datasets are the basis of background images used in web-mapping and have been a vital source of geographic data since the origins of aerial photography and satellite-based remote sensing devices.
 Rasters aggregate spatially specific features to a given resolution, meaning that they are consistent over space and scalable (many worldwide raster datasets are available).
-<!-- The downside of this is that small features can be blurred or lost. (commented - too specific) -->
-<!-- todo: add figure(s) showing raster data and blurring? -->
 
 Which to use?
 The answer likely depends on your domain of application: