Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 6 — use the page links below, or browse by topic above.
- Level of detail explained for 3D city models
- LiDAR Point Clouds Explained: Returns, Classes and Intensity
- Linear Referencing Explained: Routes, Measures and Events
- Location Analysis Explained: Catchments, Accessibility and Site Selection
- Location-Allocation Explained: p-Median, Coverage and What Each Optimises
- Logs, Metrics and Alerts: Observability for GIS Pipelines
- LRS Conflation Explained: Moving Events Between Networks
- M Values Explained: Geometry That Carries a Measure
- Management zones explained
- Map Scale Explained: How Much Detail a Scale Can Hold
- Margins of Error Explained: Why Survey Estimates Need Their Uncertainty
- Marine Heatwaves Explained
- Maritime boundaries explained: EEZ, territorial sea and baselines
- Match Quality Explained: Reading a Geocoder's Confidence Score
- Metadata standards compared: ISO 19115, STAC and Frictionless
- Morphological Tessellation Explained
- Nearest-Neighbour Joins Explained: Distance, Ties and Search Radius
- NetCDF and Gridded Data Explained: Dimensions, Variables and Attributes
- Network Distance vs Straight-Line Distance Explained
- Notebook, App or Report: Choosing How to Ship an Analysis
- Null, Empty, Missing and Invalid: Four Kinds of Broken Geometry
- OGC API Features Explained: The Standard Behind Modern Spatial APIs
- Old Map Projections Explained: What Grid Was a Historical Map Drawn On?
- Open data licences explained for spatial data
- OpenStreetMap Data Completeness Explained
- OpenStreetMap File Formats Explained: PBF, XML and the Rest
- OpenStreetMap's ODbL Licence Explained for Python Users
- Origin–Destination Data Explained
- Overlay Operations in GeoPandas: Union, Intersection, Difference Explained
- PMTiles and MBTiles Explained: Tile Archives Without a Server