Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 3 — use the page links below, or browse by topic above.
- Cron, systemd, Airflow or CI? Choosing a Scheduler for GIS Jobs
- Crop phenology and growing seasons explained
- CRS in DuckDB: Why ST_Transform Moves Your Data to the Wrong Place
- Dataset versioning explained
- Deforestation Rates Explained
- Degree Heating Weeks Explained
- Degree of Urbanisation Explained
- DEM Hydrology Explained: From Elevation to Where Water Goes
- Differential privacy for spatial counts explained
- Digital Elevation Models Explained: DEM, DSM and DTM
- Discrete Global Grids Explained: H3, S2, Geohash and Why Cells Beat Coordinates
- Disease Clusters Explained
- Drought Indices Explained: Drought Monitor Categories, SPI and What Each Measures
- DSM, DTM and CHM from LiDAR: How Each Surface Is Derived
- Dual-Polarisation Radar Explained: ZDR, ρhv and ΦDP
- DuckDB or PostGIS: Which One a Workflow Needs
- DuckDB Spatial Explained: A Spatial Database That Is Just a File
- DuckDB's Memory Model: Streaming, Spilling and Limits
- Dynamic Segmentation Explained
- Dynamic Tile Server or Static Tiles: What to Serve
- Earthquake catalogues explained: magnitudes, completeness and declustering
- EIA-860 and EIA-923 Explained: Plants, Generators and What They Generate
- Election Map Colours Explained: Diverging Scales, Midpoints and Breaks
- Election Maps Explained: What a Results Map Can and Cannot Say
- Emissivity Explained
- EPSG Codes Explained: How to Choose the Right CRS in Python
- EV Share of the Fleet Explained: Registered Stock Against Yearly Additions
- Exposure Assessment Explained
- Expressions, Scripts, Models or Plugins? QGIS Extension Points Explained
- Extrapolation in Space Explained: The Area of Applicability