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Different types of data sets

The platform supports 3 types of data sets:

  • Movement data: Data from tracking moving assets where both the location and properties can vary over time.

  • Time series data: Data with time varying properties measured or computed for fixed locations.

  • Static data: Data defined for fixed locations with propeties that do not change over time.

Type 1: Movement data

Data of this type is obtained by tracking a moving asset over time. The asset could be anything: cars, boats, airplanes, people, …​ .

Tracking a moving vehicle over time
Figure 1. Tracking and recording the locations and speeds of moving cars

For each asset, at certain time intervals, the location and additional properties are recorded and stored.

Examples of movement data

Examples of this are:

  • AIS data, which contains the position and properties of vessels over time

  • Similar datasets exist for airplanes, cars

  • Tracking the location of people by tracking their mobile phones

Files the platform expects for movement data

Data sets of this type use the following files:

  • CSV data files where each line contains:

    • The unique id of the asset

    • The location of the asset

    • The timestamp at which the location was recorded

    • The values of any additional properties at the specified time (for example the speed or the heading)

  • Optionally, CSV metadata files for information that doesn’t change over time. Each line contains:

    • The unique id of the asset

    • The properties that don’t vary over time (for example color, brand)

See the data versus metadata article for more information on the differences between the two.

Type 2: Time series data

Data of this type is obtained by measuring (or computing) values in a fixed location or over a fixed area.

Device in a fixed location measuring temperatures over time
Figure 2. Measuring the temperature at regular time intervals in a fixed location

Those measurements can be taken at a certain point location, or represent a measurement over a certain area.

Examples of time series data

Examples of this are:

  • Average temperature measurement (for a computer, a room, a country, …​).

  • A traffic counter keeping track of how many vehicles are on a road segment.

  • A person counter measuring how many people are in a room at all times.

Files the platform expects for time series data

Data sets of this type use the following files:

  • GeoJSON files which define the shapes and constant properties for the areas where the time varying data is defined for. Each feature in the GeoJSON file represents a single measurement area, and defines:

    • A geometry, representing the coverage area of the measurement.

      This geometry is used during visualization of the data on the spatial map of the visual analytics page.

    • A unique identifier for the sensor or device taking the measurements, or for the covered area.

    • Additional properties about the measurement device or area (for example the brand or type of the device, or the name of the area).

  • CSV data files where each line contains:

    • A column containing the unique measurement device or area identifier to indicate to which device or area the recordings of that specific row belong.

    • A column with the timestamp of when the recording took place.

    • Columns for each measurement that was taken (for example the temperature).

Type 3: Static GeoJSON data

Data of this type are regular GeoJSON files, containing:

  • Locations (areas, lines or points)

  • Properties for each of those locations

The benefit of creating data sets for these GeoJSON files instead of creating a background or area of interest layer for it are that you can use the properties in the data to style and filter the GeoJSON.

Files the platform expects for static GeoJSON data

Data sets of this type use the following files:

  • GeoJSON files which define the shapes and properties for those shapes. Each feature in the GeoJSON file represents a single shape, and defines:

    • A geometry

      This geometry is used during visualization of the data on the spatial map of the visual analytics page.

    • A unique identifier for the location

    • Additional (static) properties about the location.