This article only applies to movement data

This article is only relevant when working with movement data, not when working with time series data.

You can read more about the differences between the two in this article.

What is the data properties file ?

One of the ways of defining the structure of your .csv files is by uploading a data properties file.

This file is a .csv file where each row specifies what kind of data a column in your data files contains.

  • Row 1 specifies the properties of column 1 in your data .csv files,

  • Row 2 the properties of column 2,

  • …​

Contents of the data properties file

The data properties file is a .csv file where each row specifies what kind of data a column in your data files contains.

Each of the rows specifies the following properties about a column:

  • Name (string): the display name used to provide a human-readable name for the column. This name will be shown in the UI.

  • Type (string with special syntax): the type of the data property. This is used to indicate which column contains the id, location, timestamp, …​ and to specify whether a property is a numeric property, a category (enum), or text. See the next section for the syntax.

  • Description (string): a human-understandable description of what the property represents. This is for example used in tooltips in the UI.

  • Include for analysis (boolean: true or false): only properties that are included for analysis can be used for filtering and styling. If you set this to false for the field that corresponds to the id of your records, your users will not be able to style by the id, filter by the id, or see an overview of the visible asset ids. This can be useful when privacy is a concern.

  • Accuracy value (number): the analytics engine rounds numeric properties down to the nearest multiple of the accuracy value. For example if the property represents the length of a ship in metres, using a value of 0.1 for the accuracy value will tell the analytics engine to work with values with 1 digit after the decimal point. For example, a length value 2.31 will then be rounded down to 2.3. The larger the accuracy value, the more compression the platform can perform and the better the performance. This is especially important for larger data sets. This setting is only used for numeric properties. It is also ignored for the mandatory properties (id, location and timestamp).

  • No data value (number, string or empty): some data sets use a special value to indicate that the actual value for a property is unknown or not set.

  • Minimum valid value (number of empty): when specified for a numeric property, all values below this value will be considered as unknown or not set. This setting is only used for numeric properties. It is also ignored for the mandatory properties (id, location and timestamp).

  • Maximum valid value (number of empty): when specified for a numeric property, all values above this value will be considered as unknown or not set. This setting is only used for numeric properties. It is also ignored for the mandatory properties (id, location and timestamp).

Example

The following is an example of a data properties file.

For readability, it is displayed as a table and not as a .csv file (but you can download the .csv version here):

MMSI

id:long

unique identifier

TRUE

0

time

time:yyyy-MM-dd’T’HH:mm:ss

time string

FALSE

0

latitude

y:float

y-coordinate

FALSE

0

longitude

x:float

x-coordinate

FALSE

0

Speed over Ground

float

Speed over Ground

TRUE

1

102.3

Course over Ground

float

Course over Ground

TRUE

1

360.0

Heading

heading:float

Heading

TRUE

1

511

0

360

Vessel name

string

Name of the vessel

FALSE

0

IMO

string

IMO

FALSE

0

Callsign

string

Callsign

FALSE

0

Vessel type

enum

VesselType

TRUE

0

Status

enum

Status

TRUE

0

Length

length:float

length

TRUE

0.1

0

Width

width:float

width

TRUE

0.1

0

Draft

float

Draft

TRUE

0.1

0

Cargo

float

Cargo

TRUE

1

In the above example, the line

Heading

heading:float

Heading

TRUE

1

511

0

360

indicates that the corresponding column

  • Is named Heading

  • Contains numbers that are stored as floats and that represent a heading

  • The description to use for that column is also Heading

  • The column should be included in the analytics

  • The accuracy value for this numeric property is 1, all heading values are rounded down to the nearest integer

  • When the column contains the value 511, it should be interpreted as "no data"

  • Only values in the interval [0,360] are valid values

Syntax of the type property

The type property definition consists out of 3 parts:

<prefix>:<type>:<additional_info>
  • The prefix:

    • id: use this prefix for the column representing the identifier of the record. There can be only one column defined as the id.

    • x: use this prefix for the column representing the WGS84 longitude coordinate in degrees form (e.g., 179.0523).

      In case your data uses a different coordinate reference system, you can specify this as additional info. There can be only one column defined as the x coordinate.

    • y: use this prefix for the column representing the WGS84 latitude coordinate in degrees form (e.g., 50.0234).

      In case your data uses a different coordinate reference system, you can specify this as additional info. There can be only one column defined as the y coordinate.

    • z: use this prefix for an elevation expressed as meters above sea level. It is used for 3D coordinates, for example when tracking air tracks. There can be only one column defined as the z coordinate.

    • width:, length:: use the prefixes for the columns representing the width and length of the asset. The visual analytics page uses these properties to scale the icons that represent the current position of the asset.

      The platform assumes these properties are expressed in meters.

    • toFront:, toBack:, toLeft:, toRight: use the prefixes if the relative position of the sensor recording the location of the asset is known.

      Sometimes the location-recording sensor is located off-center. For large assets like container ships, this additional offset can introduce an error of several meters on the location of the asset. When the data exposes this offset, the platform can compensate for it.

      The following image illustrates how the platform interprets the different options:

      Figure 1. The relation between the different properties and the location of the sensor

      The platform assumes these properties are expressed in meters.

    • heading: use this prefix for a heading expressed in degrees clockwise from the north.

    • None of the other properties take a prefix

  • The type is one of the following: time, string, float, double, short, int, long or enum.

    • time: indicates that this column contains the time stamp of the records. There can only one column defined as the timestamp.

    • string: indicates that this column contains text.

    • float: indicates that this column contains a single precision (32 bit) floating point number.

    • double: indicates that this column contains a double precision (64 bit) floating point number.

    • short:: indicates that this column contains a half precision (16 bit) integer number in the range [-32768, 32767].

    • int: indicates that this column contains a single precision (32 bit) integer number in the range [-2147483648, 2147483647].

    • long: indicates that this column contains a single precision (64 bit) integer number.

    • enum: indicates that this column contains a values that are categories or enumerations.For example different brands of a car, or types of a vessel.

  • Some properties allow specifying additional info:

    • When the x: and y: coordinates are expressed in a different coordinate reference system, you specify the EPSG code as additional info.

      For example x:float:EPSG:4269 and y:float:EPSG:4269

    • The platform tries to auto-detect the time format for the time property. However, if the format is known, it can be specified by defining the pattern as additional info.

      For example time:yyyy-MM-dd’T’HH:mm:ss.

      The syntax for the patterns is documented here.

You can use the following flowchart to assist you in selecting the correct type:

Figure 2. Flowchart to assist you in selecting the correct type