Understanding Geographic Data
Geographic data forms the foundation of all geospatial analysis — understanding data types, formats, sources, and quality is essential.
Theory & Foundations
Geographic data describes real-world features using geometry (where) and attributes (what). The two primary models — raster and vector — serve different analytical purposes.
Vector data represents discrete features as points (wells, cities), lines (roads, rivers), and polygons (lakes, districts). Each feature carries attribute data in a table, enabling SQL-like queries and spatial joins.
Raster data represents continuous phenomena as a grid of cells (pixels). Each cell stores a value — elevation, temperature, reflectance. Resolution determines the cell size on the ground: 10m Sentinel-2, 30m Landsat, 90m SRTM DEM.
Common formats: Shapefile (.shp — legacy, max 2GB), GeoPackage (.gpkg — modern, no size limit), GeoJSON (.json — web-friendly), GeoTIFF (.tif — georeferenced raster), Cloud-Optimized GeoTIFF (COG — cloud-native).
Data quality dimensions: Positional accuracy (how close to true location), Attribute accuracy (correctness of properties), Completeness (missing features), Temporal accuracy (currency of data), and Logical consistency (valid topology).
In-Depth Coverage
Vector Data Deep Dive
Vector features model discrete real-world objects with precise boundaries.
- Points: GPS waypoints, weather stations, city centers
- Lines: Roads, rivers, utility networks, flight paths
- Polygons: District boundaries, land parcels, lakes, forests
- Multi-geometries: Features with multiple parts (archipelagos)
Raster Data Deep Dive
Raster grids represent continuous fields across space.
- DEM/DTM: Elevation surfaces for terrain analysis
- Satellite imagery: Multi-band reflectance data
- Land cover: Classified categorical rasters
- Climate grids: Temperature, precipitation at regular intervals
Open Data Sources
Free geospatial data is abundant from government and international organizations.
- Natural Earth: Global vector and raster basemaps
- OpenStreetMap: Crowdsourced global features
- USGS Earth Explorer: Landsat and elevation data
- Copernicus Open Access Hub: Sentinel satellite data
- WorldPop: Gridded population estimates
Key Techniques
Data Visualization
- GeoPackage: 35%
- Shapefile: 30%
- GeoJSON: 20%
- GeoTIFF: 10%
- CSV/KML: 5%
- 📍PositionalHow close features are to true locations
- ✅AttributeCorrectness of feature properties
- 📊CompletenessCoverage of all real features
- ⏰TemporalHow current the data is
Practical Workflow
- Identify data needs for your analysis
- Search open data portals and archives
- Download in appropriate format
- Inspect metadata and CRS information
- Check data quality and completeness
- Reproject to project CRS if needed
- Clean: fix geometries, remove duplicates
- Document lineage and processing steps
Software & Tools
Video Tutorials
Real-World Application
Real-World Projects
Volunteers map crisis-affected areas rapidly after disasters.
Case Study
Problem-Based Learning
Census data in CSV with district names, boundary data in shapefile with different naming conventions.
Solution: Clean both datasets, standardize district names, perform table join in GIS, calculate population density per km², create choropleth map.