Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 9 — use the page links below, or browse by topic above.
- 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
- Spatial Features Explained: Turning Geometry into Columns
- Spatial Indexes Explained: R-trees and Why Spatial Joins Are Fast
- Spatial Interpolation Explained: Guessing Between the Samples
- Spatial Join Cardinality Explained: One-to-One, One-to-Many and What to Do
- Spatial k-anonymity explained
- Spatial Leakage Explained: Why Random Cross-Validation Lies
- Spatial Machine Learning Explained: Why Location Breaks the Usual Rules
- Spatial metadata explained: what a dataset must tell you
- Spatial Predicates Explained: Intersects, Within, Contains and the Rest
- Spatial SQL or GeoPandas? Choosing Where the Work Happens
- Spatial Web Services Explained: WFS, WMS and OGC API Features
- Species Distribution Models Explained
- Species Occurrence Data Explained