Concepts
Understand the core concepts behind spatial data in Python before diving into tasks and workflows.
Every concept, A–Z
Page 7 — use the page links below, or browse by topic above.
- Point Density Explained: What Resolution a Point Cloud Supports
- Polar Stereographic Projections Explained
- Population Grid Accuracy Explained
- Population Grids Explained
- PostGIS Explained: When a Spatial Database Beats a Folder of Files
- PostGIS Spatial Indexes Explained: GiST and the Two-Phase Filter
- Postmile and Milepost Systems Explained
- Pour Points Explained: Why Watershed Delineation Needs a Snapped Outlet
- Power Density and Land Use Explained
- Projected vs Geographic CRS: What Actually Changes When You Reproject
- Protected Area Data Explained
- Provenance and lineage explained for spatial workflows
- PyQGIS vs GeoPandas: Which to Use for GIS Automation
- Python for GIS: What It Is and When to Use It
- QGIS Data Providers Explained: How QGIS Reads Any Data Source
- Radar Rainfall vs Rain Gauges Explained: Why They Disagree
- Radiometric Levels Explained: DN, TOA and Surface Reflectance
- Rainfall Return Periods Explained: How Rare Was That Storm?
- Raster Resampling Explained: Nearest, Bilinear and When Each Is Wrong
- Re-identification risk in spatial data explained
- Reanalysis vs Weather Station Data, Explained
- Recorded Crime Data Explained
- Regridding Explained: Bilinear, Conservative and Nearest for Gridded Data
- Renewable Siting Constraints Explained
- Repair, Reject or Flag? Choosing What Cleaning Should Do
- Reproducible GIS Environments Explained: conda, pip, Lockfiles and Docker
- Reruns and State Explained: Why Your Map App Redraws Everything
- Return periods and annual exceedance probability explained
- Rhumb Line vs Great Circle Explained
- Road Injury Severity Scales Explained