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.
- 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
- Tidal datums explained: which shoreline is the shoreline
- Time-Series Maps Explained: Events, Tracks, Snapshots and Fields
- Topological Coverage: What "Clean" Means for Adjacent Polygons
- Trajectories Explained: What Makes a Track Different from Points
- Transit accessibility explained: why travel time depends on the minute you leave
- Transit frequency and headways explained
- Urban Blocks Explained
- Urban Cooling and Green Space Explained
- Urban Extent Explained
- Urban Morphology Explained
- Vector Field Maps Explained
- Vector Tiles Explained: MVT, Layers and Why They Are Not GeoJSON
- Vector vs Raster Data in Python GIS: Key Differences
- Vegetation indices explained: NDVI, EVI, NDRE and when each fails
- Vertical Datums Explained: Why Your Elevations Are Off
- Visual Hierarchy Explained: What a Map Reader Sees First
- Weather Data Sources for Python, Explained: Stations, Reanalysis and APIs
- Weather Model Resolution Explained
- Weather Radar Reflectivity Explained: dBZ, Tilts and Beam Height
- Web Map Tiles Explained: XYZ, Zoom Levels and the Tile Pyramid
- What a Map App Can Afford to Send to the Browser
- What GDAL and OGR Actually Are (and Why Everything Depends on Them)