Version History of Major Areas 2023 (Scale 1:4 500 000)

The 'table__history' is a comprehensive collection of version history data derived from the 'tilastointialueet_suuralue4500k_2023' data collection. The table is classified as a history table, storing various versions of its base table rows. The data content of this table revolves around the 'Large Areas 2023' (Suuralueet 2023) at a scale of 1:4 500 000, sourced from the 'Statistics Finland' (Tilastokeskus) website in Finland.

The table holds various columns providing diverse information. It includes columns that store the number of the row in raw data and the dates when the row was extracted and updated. These columns, namely '_row_number' and '_start_date', together form a unique identifier for each row in this history table. The '_end_date' column indicates when a new version of the row was extracted. If '_end_date' is null, it refers to the most recent version of that row.

The table also contains geo-specific columns, which store geographic information valuable for geospatial data analytics. This geospatial data is presented using the WGS 84 coordinate reference system, with longitude first, followed by latitude. The geo-specific columns include the geographic coordinates and the type of geographic feature, encoded both in GeoJSON format and as text. The geographical center points of these features are also included, both as the coordinates and separately as longitude ('geom_center_x') and latitude ('geom_center_y').

Another category of columns in this table refers to the names of large areas in different languages and their identifiers, providing a multilingual reference for the areas covered by the data. The 'vuosi' column indicates the year the data refers to.

Given its rich and diverse data content, this table could support various data analytics applications. For instance, the geospatial data could be used to perform geographic trend analysis or to build interactive geographic visualizations. The version history data could be used to track changes over time, supporting trend forecasting or anomaly detection. Furthermore, the multilingual data enables international data analyses.