Large Areas Data 2018 (Scale 1:4,500,000)

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The table under discussion forms part of a dataset collection titled 'tilastointialueet_suuralue4500k_2018', and contains data related to the 'Major Areas in 2018', which is an English translation of the Finnish source description 'Suuralueet 2018'. The table includes data extracted from the Finnish website 'Tilastokeskus' (Statistics Finland).

The table contains a wealth of information that can be used for various types of data analytics. For instance, the data includes geographic information, which is useful for geospatial data analytics. The geographic coordinates are presented using the WGS 84 coordinate reference system, with the order of axes being longitude first, followed by latitude.

Columns in the table, such as 'geom_geojson' and 'geom_geotext', store geographic information in different formats, while 'geom_type' specifies the format of the geographic data. The 'geom_centroid', 'geom_center_x', and 'geom_center_y' columns provide the centre point of the geographic area.

Also included in the table are columns 'name', 'namn', and 'nimi', presenting names in various languages, which could be useful for multilingual data analysis or international studies. The 'suuralue' and 'vuosi' columns provide the major area code and the year, respectively, allowing for temporal and regional analysis of the data.

Distinctly, the table includes two unique columns: '_extract_date' and '_row_number'. These contain the date of data extraction and the row number within the extracted data, respectively. These two columns uniquely identify each row in the table, which could enhance data management and tracking changes over time.

The table can be utilized in various ways in data analytics. For example, the geographic information can be used to conduct spatial analyses or create detailed maps of the major areas. The multilingual names can be used for international studies or cross-cultural comparisons. The unique identifiers can be used to track changes in the data over time, providing insights into trends or shifts in the major areas.

Field Descriptions

Name Data type Description
_extract_datedateThe date when the data on the row of the table was extracted from the data source. It helps in keeping the track of when the information was last updated.
_row_numberlongThe row number in the data extracted from the source on the extract date specified by the column '_extract_date'. It uniquely identifies each row.
geom_geojsonstringContains the geographic data in GeoJSON format. It represents the multi-point geographical features.
geom_geotextstringContains the geographical data in text format. It represents the multi polygon geographical features.
geom_typestringSpecifies the type of the geographical data. In this case, it is a multi polygon.
geom_centroidstringThe centroid or the geometric center of the multi polygon.
geom_center_xdoubleThe x-coordinate of the center point of the geographical feature.
geom_center_ydoubleThe y-coordinate of the center point of the geographical feature.
gml_idstringThe Global Markup Language (GML) ID for each row. It represents a unique identifier for the geographical feature.
namestringThe name of the geographical area in English.
namnstringThe name of the geographical area in Swedish.
nimistringThe name of the geographical area in Finnish.
suuraluestringA numerical identifier for the large geographical area.
vuosilongThe year of the data. It helps in understanding the time frame of the data.

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Additional Information

Last updated November 11, 2024
Created November 11, 2024
License No License Provided
Geographic coverage
Geographic granularity
Temporal coverage from
Temporal coverage to
Temporal coverage to
Countryfi
Databasefi_tilastokeskus
Extract date2024-11-11
Iddc4b5304-fe9c-4e41-95f8-1cae2e1aea7c
Package idca31ee6e-9b48-43ad-bc91-30c4382a05bd
Revision id5103ffe1-b67e-4170-a5c1-b40ce14171a0
Stateactive
Tabletilastointialueet_suuralue4500k_2018__table