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.
- 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
- Speckle explained: why radar images look grainy
- Spectral Bands Explained: What Satellite Imagery Actually Measures
- Spectral Indices Explained: NDVI, NDWI and What They Assume
- STAC Explained: How Satellite Imagery Catalogues Work
- Stops, Trips and Segmentation Explained
- Stream Order Explained: Strahler, Shreve and What They Measure
- Streamlit, Dash or Panel: Choosing a Framework for a Map App
- Street Network Orientation Explained
- Street Networks as Graphs Explained
- Surface Urban Heat Island Explained
- SVG, Canvas and WebGL: How Web Maps Draw Data, Explained
- Terrain Shading and the Horizon Explained
- Test Fixtures for GIS Code: Building Spatial Test Data You Can Trust
- The Address Data Model: Why an Address Is Not a String
- The Anatomy of a Batch Job: Discover, Apply, Report
- The coastline paradox explained for GIS work
- The Ecological Fallacy Explained