All datasets in GIS can be categorized as being either discrete or continuous. Understanding the difference between these two types of data is important for effective data analysis and visualization.
What is Discrete GIS Data?
Discrete data is geographic data that only occurs in specific locations.
For polygon data, discrete data has well defined boundaries. Point and line GIS data such as tree locations, rivers, and streets all fall into the category of discrete datasets.
Discrete data is categorical or nominal in nature and is typically represented by a set of distinct, separate values.
Discrete GIS data can be represented using both vector and raster data models. Examples of discrete data include land use categories, soil types, or vegetation classes.
Maps made with discrete GIS data will have areas on the map that contain values from that dataset and areas on the map where that dataset is absent. In this map example below, tornado locations (red points) are an example of a discrete GIS dataset.
What is Continuous GIS Data?
Continuous data has no clearly defined boundaries.
Every point on a map made with continuous GIS data will contain a numeric value.
Continuous GIS data is typically represented by a continuous scale, such as a gradient or a heat map. Examples of continuous data include temperature, elevation, slope, and rainfall.
In GIS, continuous data is often represented by a raster data model, where data is stored in a grid of cells, with each cell representing a small area of the Earth’s surface. The values in each cell represent the value of the continuous variable being measured at that location.
Elevation, slope, temperature, and precipitation are examples of datasets that are continuous.
In the example map below, every point on the map within the contiguous United States contains a temperature value.
Differences between discrete and continuous GIS data
Different types of data measurement
Continuous data represents a measurement that can take on any value within a range, while discrete data represents a specific category or class.
For example, temperature is a continuous variable because it can take on any value within a range, while land use is a discrete variable because it is made up of distinct, separate categories such as forest, agriculture, or urban areas.
Data is visualized differently
Another important difference is the way that the data is represented and visualized.
Continuous data is often represented using a continuous color scale or a gradient, which allows for the visualization of patterns and trends across a range of values.
Discrete data, on the other hand, is typically represented using a set of distinct colors or symbols that correspond to each category or class.
The focus of the spatial analysis is different
In GIS analysis, the type of data being used has implications for the types of analysis and techniques that can be applied.
For example, continuous data is often used in terrain analysis or environmental modeling to identify areas of high or low elevation, slope, or other variables.
Discrete data is often used in land use or demographic analysis to identify patterns or clusters of different types of features or populations.