Introduction to GIS Course Section

• Course section focuses on Geographic Information Systems (GIS)
• Goal: familiarize with key concepts before coding implementation
• Concepts covered: vectorial vs raster maps, projections, resolution, map creation approaches
• Objective: apply all concepts in R code

Vector vs Raster Map Differences

• Vectorial maps: irregular polygons possible
• Raster maps: grid structure with equally distributed cells
  • All cells same size and disposition
  • Affects how forms are represented
• Use case selection:
  • Vector: when interested in object shapes
  • Raster: when interested in map content

Map Projections

• Problem: Earth not flat surface, requires conversion to flat representation
• Multiple projections exist: each has pros/cons depending on focus area
• Examples:
  • Accurate shapes at poles: specific projection needed
  • Accurate global view: different projection choice
• Shape deformation: same area appears different across projections
• South America example: shape changes significantly based on projection used
• Critical considerations:
  • Projection accuracy for specific Earth region
  • Scale of work (regional vs global)
  • Ensure all maps use same projection to avoid problems

Resolution Concepts

• Analogy: picture as proxy for raster map (built similarly)
• Resolution trade-offs:
  • Higher resolution: more detail but heavier files
  • Lower resolution: less detail but lighter files
• Practical considerations:
  • Computer capacity limitations
  • Internet connection speed
• Quality limitations: increasing resolution doesn't always improve accuracy
  • Original map quality matters
  • Transforming coarse to fine resolution doesn't create new information

Map Creation Approaches

Top-Bottom Approach (Satellite Maps)

• Satellites: orbit above Earth with camera-like sensors
• Process:
  • Different sensors and lenses take Earth surface representations
  • Send different wavelengths
  • Receive bounced-back signals
  • Apply corrections to create satellite images

Bottom-Top Approach (Upscaling)

• Example: Duarte et al. 2020 paper
• Process:
  • Real field data points collected
  • Model created using environmental drivers
  • Upscaling fills spaces between data points
  • Results in complete map coverage

Map Resources

• WorldClim: environmental variables (climate, precipitation, temperature)
• SoilGrids: physical and chemical soil properties