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If a field is specified, the channel definition must describe the encoded data’s type of measurement (level of measurement). The supported data types are: "quantitative", "temporal", "ordinal", "nominal", and "geojson".

Property Type Description
type Type

Required. The encoded field’s type of measurement ("quantitative", "temporal", "ordinal", or "nominal"). It can also be a geo type ("latitude", "longitude", and "geojson") when a geographic projection is applied.

Note: Data type here describes semantic of the data rather than primitive data types in programming language sense (number, string, etc.). The same primitive data type can have different types of measurement. For example, numeric data can represent quantitative, ordinal, or nominal data.


Quantitative data expresses some kind of quantity. Typically this is numerical data. For example 7.3, 42.0, 12.1.


Temporal data supports date-times and times. For example 2015-03-07 12:32:17, 17:01, 2015-03-16.

Note that when a "temporal" type is used for a field, Vega-Lite will treat it as a continuous field and thus will use a time scale to map its data to visual values. For example, the following bar chart shows the mean precipitation for different months.

Casting a Temporal Field as an Ordinal Field

To treat a date-time field as a discrete field, you can cast it be an "ordinal" field. This casting strategy can be useful for time units with low cardinality such as "month".


Ordinal data represents ranked order (1st, 2nd, …) by which the data can be sorted. However, as opposed to quantitative data, there is no notion of relative degree of difference between them. For illustration, a “size” variable might have the following values small, medium, large, extra-large. We know that medium is larger than small and same for extra-large larger than large. However, we cannot compare the magnitude of relative difference, for example, between (1) small and medium and (2) medium and large. Similarly, we cannot say that large is two times as large as small.


Nominal data, also known as categorical data, differentiates between values based only on their names or categories. For example, gender, nationality, music genre, and name are nominal data. Numbers maybe used to represent the variables but the number do not determine magnitude or ordering. For example, if a nominal variable contains three values 1, 2, and 3. We cannot claim that 1 is less than 2 nor 3.


GeoJSON data represents geographic shapes specified as GeoJSON.