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.
- 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
- Seismic Hazard Explained
- Sentinel-1 products explained: SLC, GRD and radiometrically terrain-corrected
- Sentinel-2 or Landsat? Choosing a Satellite Imagery Source
- Serving Spatial Data Explained: Files, Features and Tiles
- Shapely Basics: Working with Geometry Objects in Python
- Sharing a Map App: Public, Private and In Between
- Site Suitability Explained: Constraints, Factors and Weights
- Slope and Aspect Explained: How They Are Computed from a Grid
- Small Multiples vs Animation: Choosing How to Show Change on a Map
- Small Numbers Explained: Why Rates in Small Areas Jump Around
- Snow Cover and NDSI Explained: How Satellites Map Snow
- Snow Water Equivalent Explained: Depth Is Not Water
- Solar Irradiance Explained
- Spatial Autocorrelation Explained
- Spatial Clustering Explained: DBSCAN, K-Means and What They Assume
- Spatial Dashboards Explained: When an App Beats a Map Image
- Spatial Data Quality: The Six Dimensions That Matter