Provinces 2015 (1:4 500 000) - Dataset Collection of Finnish Statistical Regions

The table in question belongs to a dataset collection that captures geospatial information related to the administrative regions of Finland in 2015. The data is sourced from 'Tilastokeskus' (Statistics Finland), the national statistical institution of Finland. The table contains various columns that store a wealth of information about each region.

The data in the table can be broken down into three main categories - geographic, identification, and shape data. The geographic data, including geospatial coordinates and geographic type, is stored in columns that start with 'geom_'. This data is presented using the WGS 84 coordinate reference system, which uses longitude and latitude to pinpoint locations. This information could be used for geospatial data analytics, such as plotting the regions on a map or calculating the distance between them.

The identification data, found in columns such as 'name', 'namn', and 'nimi', provides different names for each region in various languages. This can be useful for creating multilingual representations of the data or for cross-referencing with other datasets.

The shape data, contained in 'shape_star' and 'shape_stle', gives information about the physical characteristics of each region. The 'shape_star' column provides the area size of the region while the 'shape_stle' column provides the perimeter length. This information could be used to analyze the physical characteristics of each region, for instance, comparing their sizes or evaluating the compactness of their shapes.

In addition to these, the table also contains two special columns - '_extract_date' and '_row_number'. The '_extract_date' column provides the date when the row data was extracted, while the '_row_number' represents the unique row number on that extract date. This combination uniquely identifies each row, which is important for tracking data changes over time. For instance, if the data is extracted periodically, these columns could be used to analyze trends or changes in the region data over time.