Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 4 — use the page links below, or browse by topic above.
- FAA Registration Status Codes Explained: Valid, Pending and Expired
- Failure Policy in Batch Processing: Fail Fast, Skip or Quarantine
- FEMA Disaster Declarations Explained: DR, EM, FM and the Programs Behind Them
- Field boundary data explained: where it comes from
- Fiona vs pyogrio: How GeoPandas Reads and Writes Files
- Fire Return Interval and Fire Rotation Explained
- Flight Delay Causes Explained: Carrier, Weather, NAS and Late Aircraft
- Flood hazard maps explained: depth, extent and which flood they describe
- Flood Insurance Payouts Explained: Damage, Coverage and What the NFIP Pays
- Flow Direction Explained: D8, D-Infinity and MFD Compared
- Flow Maps Explained: What a Line on a Map Says About Movement
- Forest Definitions Explained
- Forest Fragmentation Explained
- Generalisation for Zoom Levels Explained
- Geocoding Explained: From an Address String to a Coordinate
- Geohash Explained: Prefixes, Precision and the Edge Problem
- GeoJSON, Vector Tiles or Parquet: Choosing an API Response Format
- Geomasking methods explained: donut, random and adaptive
- GeoPandas Basics: Working with Spatial Data in Python
- GeoParquet and Columnar Storage Explained: Why It Reads So Much Faster
- Geoprivacy explained: why coordinates are personal data
- Georeferencing Explained: Turning a Scanned Map into Spatial Data
- Georeferencing Transformations Explained: Affine, Polynomial and Thin-Plate Spline
- GIS Data Sources Explained: Where Spatial Data Comes From
- GIS Vector File Formats Compared: Shapefile, GeoPackage, GeoJSON, Parquet
- Glacier Inventories Explained: Why the Same Glacier Has Different Areas
- Global Forest Change Explained
- GPS Error Explained: Why Your Track Wanders
- Gravity and Huff Models Explained: Predicting Where People Go
- GRIB, NetCDF or Zarr: Choosing a Format for Gridded Data