Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 8 — use the page links below, or browse by topic above.
- Python for GIS: What It Is and When to Use It
- QGIS Data Providers Explained: How QGIS Reads Any Data Source
- Radar Rainfall vs Rain Gauges Explained: Why They Disagree
- Radiometric Levels Explained: DN, TOA and Surface Reflectance
- Rainfall Return Periods Explained: How Rare Was That Storm?
- Raster Resampling Explained: Nearest, Bilinear and When Each Is Wrong
- Re-identification risk in spatial data explained
- Reanalysis vs Weather Station Data, Explained
- Recorded Crime Data Explained
- Regridding Explained: Bilinear, Conservative and Nearest for Gridded Data
- Renewable Siting Constraints Explained
- Repair, Reject or Flag? Choosing What Cleaning Should Do
- Reproducible GIS Environments Explained: conda, pip, Lockfiles and Docker
- Reruns and State Explained: Why Your Map App Redraws Everything
- Return periods and annual exceedance probability explained
- Rhumb Line vs Great Circle: Which Is Shorter, and by How Much
- Road Injury Severity Scales Explained
- Rooftop, Interpolated or Centroid: Geocoding Precision Levels
- Route Calibration Explained: Why Measures Are Not Lengths
- S2 Cells Explained: Squares on a Cube and When They Beat Hexagons
- Sample Design Explained: Where to Measure
- Sampling Bias in Occurrence Data Explained
- Sampling Rate and Gaps Explained: How Often Is Often Enough
- SAR geometry explained: layover, foreshortening, shadow and look direction
- SAR imagery explained: what radar measures and why it sees through cloud
- SAR polarisation explained: VV, VH and what each responds to
- Satellite NO2 Explained
- Sea level rise data explained
- Sea Surface Temperature Explained
- Sea-Ice Extent vs Area Explained