time

  • This page introduces the Google date and time dataset, focusing on the "time" canonical concept.

  • The dataset defines various time granularities like year, quarter, month, week, and day, all extending the core "time_point" concept.

  • Each time granularity concept can be used as a dimension in data slices, allowing for analysis across different time periods.

  • A comprehensive XML file and a CSV file for granularities are provided for detailed dataset structure and values.

  • The dataset is designed to standardize the representation of time within Google Public Data and can be leveraged for time-based data analysis.

This page documents the time canonical concept dataset. The complete XML file is also available for review.

Basic Info

Name Google date and time
Description Google date and time dataset
Namespace http://www.google.com/publicdata/dataset/google/time
URL http://code.google.com/apis/publicdata/docs/canonical/time.html

Provider

Name Google Inc.
Description Google Inc.
URL http://www.google.com

Imported Namespaces

No imported namespaces.

Topics

No topics defined.

Concepts

Concept: time_point

ID time_point [XML]
Name Point in time
Type date
Description Point in time, with a given granularity.
Attributes
granularity
ID
(implied)		 granularity [XML]
Concept granularity
Properties No local properties.

Concept: year

ID year [XML]
Name Year date
Extends time_point
Description A date with yearly granularity. The year concept is usually used directly in a slice definition to define a dimension that contains year. For example, a slice for yearly population by country would be defined as follows: 
  <slices>
    <slice id="country_slice">
      <dimension concept="country"/>
      <dimension concept="time:year"/>
      <metric concept="population"/>
      <table ref="country_slice_table"/>
    </slice>
  </slices>

The table definition for this slice would be defined as follows: 
  <tables>
    ...
    <table id="country_slice_table">
      <column id="country" type="text"/>
      <column id="year" type="date" format="yyyy"/>
      <column id="population" type="integer"/>
      <data>
        <file format="csv" encoding="utf-8">country_slice.csv</file>
      </data>
    </table>
    ...
  </tables>

And the data contained in the CSV file for this table would look like: 
  country, year, population
  AF, 1960, 9616353
  AF, 1961, 9799379
  AF, 1962, 9989846
  AF, 1963, 10188299
  ...
Attributes
granularity
ID granularity [XML]
Value YEARLY
Properties No local properties.

Concept: quarter

ID quarter [XML]
Name Quarter date
Extends time_point
Description A date with quarterly granularity. See the example for the year concept above.
Attributes
granularity
ID granularity [XML]
Value QUARTERLY
Properties No local properties.

Concept: month

ID month [XML]
Name Month date
Extends time_point
Description A date with monthly granularity. See the example for the year concept above.
Attributes
granularity
ID granularity [XML]
Value MONTHLY
Properties No local properties.

Concept: week

ID week [XML]
Name Week date
Extends time_point
Description A date with weekly granularity. See the example for the year concept above.
Attributes
granularity
ID granularity [XML]
Value WEEKLY
Properties No local properties.

Concept: day

ID day [XML]
Name Day date
Extends time_point
Description A date with daily granularity. See the example for the year concept above.
Attributes
granularity
ID granularity [XML]
Value DAILY
Properties No local properties.

Concept: granularity

ID granularity [XML]
Name Granularity
Type string
Data granularity_table
Description Granularity of time, i.e., the uncertainty that a point in time may be anywhere within some time interval.
Attributes No local attributes.
Properties No local properties.

Slices

No slices defined.

Tables

Table: granularity_table

ID granularity_table [XML]
Data granularity.csv [CSV]
Columns
Name Type
granularity string