Spatial vector data

• Vector data represent discrete objects in space
• Points
  • Single coordinates: $(x,y)$ or (longitude, latitude)
  • Examples: locations of sampling sites, GPS positions of animals, monitoring stations for air quality
• Lines
  • Connected sequence of points
  • Examples: rivers and streams, roads and railway lines, animal migration tracks
• Polygons
  • Closed shapes (boundaries with an inside and outside)
  • Examples: country or county boundaries, nature reserves, lakes, forest stands
• Vector data structure
  • Geometry: describes the shape and location (point/line/polygon)
  • Attributes: data attached to each geometry
  • Example: for a polygon representing a county, attributes are name, population, average income, disease rate
• Vector data are often handled with sf (“simple features”)
  • Stored like a data frame, but with a special geometry column
  • Each row = one feature (one point, line, or polygon)
  • Other columns = attributes (e.g. species, ID, measurement)

Raster data

• Raster data represent continuous surfaces as a grid of cells (pixels)
• Raster structure
  • The study area is divided into regular cells
  • Each cell covers the same area on the ground
• Each cell has one value
  • Examples: elevation, temperature, land cover class
• Resolution
  • Size of each cell on the ground
  • Examples: 10 m × 10 m, 1 km × 1 km
  • Smaller cells → higher resolution → more detail, but larger file size
• Extent
  • The spatial area covered by the raster