In the context of Geographic Information Systems (GIS), spatial data refers to information that has a geographic or locational component.
Understanding Spatial Data
As defined, spatial data comprise the relative geographic information about the earth and its features. This is the fundamental data type that GIS operates on, allowing us to map, analyze, and understand patterns and relationships across geographic space.
A key aspect of spatial data is its connection to a specific place on Earth. A pair of latitude and longitude coordinates defines a specific location on earth. These coordinates provide the geographic reference that ties information to its real-world position.
Think of spatial data as answering the question "where?". It's not just what something is (like a population number or a temperature reading), but where it is located.
How Spatial Data is Stored
Spatial data needs to be stored and represented in a way that computers can process. According to how it's stored, spatial data are of two types according to the storing technique, namely, raster data and vector data.
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Raster Data: This type represents geographic features using a grid of cells or pixels. Each cell contains a value representing the feature or attribute at that location.
- Examples: Satellite imagery, aerial photos, digital elevation models (DEMs), gridded temperature maps.
- Best for: Representing continuously varying phenomena or large areas in detail.
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Vector Data: This type represents geographic features using points, lines, and polygons. Each feature is defined by its geometry (a series of coordinates) and can have associated attributes (non-spatial information).
- Examples: Property boundaries (polygons), roads (lines), building locations (points), rivers (lines).
- Best for: Representing discrete features with clear boundaries and specific attributes.
Here's a quick comparison:
| Feature | Raster Data | Vector Data |
|---|---|---|
| Representation | Grid of cells/pixels | Points, lines, polygons |
| Storage | Cell values in a grid | Coordinate lists for features |
| Data Type | Continuous or thematic | Discrete, distinct features |
| Examples | Satellite images, DEMs | Roads, parcels, cities |
| File Types | GeoTIFF, JPG2000, GRID | Shapefile (.shp), GeoJSON, KML |
Practical Applications of Spatial Data
Spatial data is crucial for countless applications:
- Navigation: GPS systems use spatial data (roads, points of interest) for routing.
- Urban Planning: Analyzing population density (raster) and zoning maps (vector) to make planning decisions.
- Environmental Monitoring: Tracking deforestation (raster), mapping river networks (vector), or analyzing pollution spread.
- Emergency Services: Dispatching units based on incident location and road networks.
- Business: Site selection based on customer demographics (spatial attributes) and competitor locations.
In essence, spatial data provides the geographic context necessary for understanding phenomena and making informed decisions about locations and distributions on Earth.
