ELY Regions 2022 (Scale 1:4 500 000) Table - Dataset Collection

The table in question is an integral part of a dataset collection that encapsulates regional data. This table holds vital data about ELY-areas for the year 2022 at a scale of 1:4,500,000, sourced from the Statistical Center 'Tilastokeskus' in Finland.

The dataset is rich with information, organized into various columns, each with unique significance. Notably, two columns, namely, the extraction date, and the row number, are of particular importance as they serve to uniquely identify each row. The extraction date refers to the specific date when the data was pulled from the source, while the row number indicates the sequence of data as it was extracted.

One of the main features of the table is the geographic information. Columns beginning with 'geom_' contain various forms of geospatial data. This includes 'geom_geojson' and 'geom_geotext', which hold the geojson and geotext data respectively, 'geom_type' detailing the type of geometric shape (like multipolygon or polygon), and 'geom_centroid' indicating the centroid of the geometric shape. Additionally, 'geom_center_x' and 'geom_center_y' provide the X and Y coordinates of the center of the geometric shape respectively. All geographic coordinates are presented using the WGS 84 coordinate reference system, with the axis order being longitude first, followed by latitude.

Other columns in the table provide additional details such as the unique identifier for each ELY area ('id'), the name of the area in different languages ('name', 'namn', 'nimi'), and the year of data ('vuosi'). The 'gml_id' column contains a unique identifier for each row, which is used for geospatial data analytics.

The data in this table can be used for a variety of analytical purposes. For example, the geographic information can be used for geospatial data analytics to understand geographic distribution and patterns of the ELY areas. The unique identifiers and names can be used to cross-reference or merge this data with other datasets to provide a more holistic view of the region under study. The extraction date and row number can be used to track changes and trends over time. The comprehensive nature of this dataset makes it a valuable resource for anyone interested in regional studies, geographic analysis, urban planning, and related fields.