• “space” is generally 2D (\(\mathbb{R}^2\); could be surface of a sphere)
• also: networks, trees, lattices, …
• features: points, polygons, lines (and collections thereof); rasters
• also: continuous or categorical values associated with features
  • counts (disease incidence) or continuous values (GDP) associated with polygons (countries/provinces/counties)
  • values associated with grid cells (digital elevation models)
  • points (locations of murders)

e.g. 

• points to density fields (2D kernel density estimation)
• points to polygon values (square or hex binning)
• fields to polygons (contouring)
• point values to fields (interpolation; akima does bicubic/bilinear)

• points on maps
• contour plots on maps
• choropleth maps (filled polygons)

spatial graph gallery

• big (especially rasters)
• sphere to 2D:
  • projections xkcd maps
  • CRS or datums (Bivand, Pebesma, and Gómez-Rubio 2013)
• shapefile formats
  • geocoding (name to coordinates); reverse geocoding (coordinates to name)
• see R Spatial task view
• intro to visualizing spatial data in R

• colour issues are much more salient for spatial data
• big blocks of colour
• often use colour gradients for continuous data
• continuous vs segmented, appropriate endpoints (background)
• ColorBrewer project, RColorBrewer package

• sf (“simple features”): tidy spatial data (web page)
• maptools

• top of Cleveland hierarchy (x,y coordinates) are used up
• insets (Alaska/Hawaii etc.)
• map decoration
• representing uncertainties: Correll, Moritz, and Heer (2018), MacEachren et al. (2005), Koo, Chun, and Griffith (2018), Bolin and Lindgren (2017)
• not misrepresenting areas (e.g. cartograms: Perrier (2019), Höhle (2016))
  • red/blue maps
  • red/blue/purple maps
• linking?

• maps (base-R maps, some basic spatial data sets)
• ggmap (maps in ggplot, including downloading data from google maps etc.)
• leaflet (map widget)
• tmap (an alternative ggplot-like approach: see here)
• mapcan (political maps for Canada)