What is discrete data in GIS?
Discrete data in GIS represents objects with known and definable boundaries, standing distinctly separate from their surroundings.
In the context of Geographic Information Systems (GIS), discrete data is a fundamental data type used to represent geographic features. As stated in the reference, discrete data, sometimes known by other names such as thematic, categorical, or discontinuous data, most often represents objects within geographic space.
A defining characteristic of discrete data is that each object represented has known and definable boundaries. This means you can precisely determine where one object begins and another ends. Unlike continuous data, which smoothly transitions across space (like elevation or temperature), discrete data deals with distinct, separate entities.
Key Characteristics
Based on the definition, the key characteristics of discrete data include:
- Distinct Objects: It represents individual, separable features or phenomena.
- Definable Boundaries: Each object has a clear, non-ambiguous spatial extent.
- Categorical or Thematic: Often used to categorize areas based on specific attributes (e.g., land cover types, administrative units).
- Discontinuous: There are sharp transitions between different categories or objects, not gradual changes.
Representation in GIS
Discrete data can be stored and represented in both primary GIS data models:
- Feature (Vector) Data: This is a very common way to represent discrete data. Objects are stored as points, lines, or polygons, which inherently have defined boundaries or locations.
- Examples: Building footprints (polygons), road networks (lines), individual trees (points).
- Raster Data: Discrete data can also be stored in raster format. In this case, each pixel within the raster grid represents a specific category or object type. While pixels are the unit, the data itself represents discrete classifications.
- Examples: Land use maps (each pixel categorized as 'forest', 'urban', 'water'), soil type maps, political boundaries represented by cell values.
Practical Examples of Discrete Data
Discrete data is prevalent in various GIS applications. Examples include:
- Land Use/Land Cover: Maps showing distinct zones like residential areas, forests, agricultural fields, and bodies of water.
- Administrative Boundaries: Polygons representing countries, states, counties, or parcels of land.
- Infrastructure: Lines representing roads, railways, or utility lines; points representing fire hydrants or power poles.
- Ecological Units: Polygons defining specific habitat types or vegetation zones.
- Census Data: Polygons representing census tracts, often with associated categorical attributes.
Understanding discrete data is crucial for choosing appropriate analysis techniques and data storage methods in GIS. Because its boundaries are clearly defined, operations like calculating area, length, counting objects, or determining adjacency are typically straightforward.
