Introduction to Spatial Data¶
What is spatial data?¶
Spatial data, also known as geospatial data, refers to information about the physical location, trajectory, and shape of objects on Earth or in space. Spatial data includes information like coordinates, dimensions, direction, elevation, and any other attributes that describe a specific location’s features.
Types of spatial data¶
Vector data¶
Vector data represents discrete features in space through geometric shapes such as:
- Points: Represent specific locations, like a city or a landmark.
- Lines: Represent linear features, such as roads or rivers.
- Polygons: Represent areas, like lakes, parks, or country boundaries.
Vector data formats¶
Popular vector data formats include:
- Shapefile
- GeoJSON
- GeoPackage
- GeoParquet
For a more comprehensive list of vector data formats see Vector drivers in the Geospatial Data Abstraction Library (GDAL).
Raster data¶
A raster data structure uses a grid of cells (pixels) to represent spatial data. Each cell contains one or more values, often representing attributes like elevation, temperature, or land cover, making it suitable for continuous phenomena and satellite imagery analysis.
Typical use cases for raster data include the following:
- Remote Sensing - Satellite imagery and aerial photography.
- Elevation Models - Digital Elevation Models (DEMs) used for topographic mapping.
- Environmental Monitoring - Mapping temperature, rainfall, or vegetation coverage over large areas.
- Scientific Research - In fields like physics and meteorology, measurements over time and space are often stored in multi-dimensional arrays (e.g., temperature data over multiple time points and locations).
Raster data formats¶
Popular raster data formats include:
- Cloud Optimized GeoTIFFs (COGs)
- netCDF
- ArcGRID
- jpeg2k
For a more comprehensive list of raster data formats see Raster drivers in the Geospatial Data Abstraction Library (GDAL).