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Data Visualization

Basic Data Types

The different data types in a dataset determine the type of charts you can use. These are the basic data types that a dataset will typically contain.

Numerical/Quantiative Data

  • Continuous: Data that can take on any value within a specified interval. It can be measured, and can take on any value, including fractions or decimals.

    • Example:  A measure of greenhouse gas emissions in the US.

  • Discrete: Data that has distinct values that can be counted. It can only take on integer values.

    • Example: The number of households in a county.

Categorical/Qualitative Data

  • Nominal: Data that has distinct values representing different groups or categories with no inherent ranking or order.

    • Example: The names of different counties.

  • Ordinal: Data that has distinct values representing categories with a meaningful order or ranking.

    • Example: Education levels, such as elementary, middle, high school, college, and post-graduate.

  • Binary: Data that has values that can only be one of two distinct categories.

    • Example: success/failure, yes/no

Special Data Types

In addition to basic data types, there are temporal and spatial data types. These data types are tied to specific use cases, and are wrangled and visualized in specific ways.

Temporal Data

  • Time Series: data points collected over an interval of time and indexed by time.
    • Example: Daily weather data.

Spatial Data

  • Raster: Uses pixels to represent geographic information. Each pixel value represents an area on Earth.
    • Example: Satellite images
  • Vector: Represents geographic features as points, lines, and polygons to define the shape of a spatial object. 
    • Example: A polygon representing the boundaries for a state.