Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 10 — use the page links below, or browse by topic above.
- Speckle explained: why radar images look grainy
- Spectral Bands Explained: What Satellite Imagery Actually Measures
- Spectral Indices Explained: NDVI, NDWI and What They Assume
- Speedtest Data Explained: Who Runs Tests and What the Averages Mean
- STAC Explained: How Satellite Imagery Catalogues Work
- Stops, Trips and Segmentation Explained
- Storm Damage Estimates Explained: Why a Few Events Make Most of the Total
- Storm Events, Episodes and County Segments: What One Row Means
- 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
- Street Tree Censuses Explained: What a Tree Record Holds and Who Made It
- Surface Urban Heat Island Explained
- SVG, Canvas and WebGL: How Web Maps Draw Data, Explained
- Taxi Trip Records Explained: What Each Field in the TLC Data Means
- 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
- The Gutenberg-Richter Law Explained
- The Modifiable Areal Unit Problem Explained
- The OpenStreetMap Data Model Explained for Python Users
- The PyQGIS API Map: Which Class Does What
- The QGIS Processing Framework Explained
- The Raster Data Model Explained: Bands, dtype, NoData and the Transform
- The Variogram Explained: How Far Does a Measurement Reach
- Threads, Processes and the GIL: Parallelism in Python GIS Explained