Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 5 — use the page links below, or browse by topic above.
- House Price Index Methods Explained: Median, Mix-Adjusted, Repeat-Sales and Hedonic
- How Accurate Is a Historical Map? Measuring Survey Error
- How DEM Resolution and Source Change a Drainage Network
- How DuckDB Uses (and Does Not Use) a Spatial Index
- How Many Decimal Places Do Coordinates Need?
- How PostGIS Stores Geometry: SRID, EWKB and the Typed Column
- How QGIS Styling Works: Renderers, Symbols and Rules
- How to Automate QGIS with Python (PyQGIS): The Complete Workflow
- How to Create Interactive Maps in Python with Folium
- How to Make a GIS Workflow Reproducible in Python
- HTTP Caching for Spatial Data: ETags, Max-Age and Invalidation
- Idempotency Explained: Why a GIS Job Must Be Safe to Re-run
- IDW, Kriging, Splines or TIN? Choosing an Interpolator
- Incremental Processing: How a Batch Job Knows What Changed
- InSAR explained: measuring millimetres of ground motion from phase
- Inside Airbnb Data Explained: What the Snapshots Contain
- Interactive vs Static Maps, Explained
- Introduction to Rasterio: Reading Raster Data in Python
- Isochrones Explained: Travel Time Areas and What They Assume
- Kernel Density Explained: What a Heatmap Actually Shows
- Labelling Explained: Why Automatic Map Labels Collide
- Land Cover Change Explained
- Land Cover Map Accuracy Explained
- Land Cover Products Explained
- Land Surface Temperature Explained
- Landsat Thermal Data Explained
- Landslide susceptibility explained: what the map is and is not
- LAS, LAZ and COPC Explained: How Point Clouds Are Stored
- Latitude and Longitude Explained
- Lazy Loading Explained: Why xarray Reads Nothing Until You Ask