Provinces 2022 (Scale 1:1,000,000) - Data Table

The table in discussion belongs to a dataset collection that encapsulates data related to statistical areas in Finland. The table, sourced from the Finnish Statistics Bureau 'Tilastokeskus', provides comprehensive information about the regions in Finland for the year 2022, as indicated by the column 'vuosi'. The regions are represented in both Finnish and Swedish, as seen in the columns 'nimi' and 'namn' respectively, as well as in English in the 'name' column. The 'maakunta' column identifies each region with a unique code. The table also includes a unique identifier for each row, denoted by the 'gml_id' column.

The table is rich in geospatial data, with multiple columns dedicated to geographic information. These include 'geom_geojson', 'geom_geotext', 'geom_type', 'geom_centroid', 'geom_center_x', and 'geom_center_y'. These columns store data in various formats such as JSON and text, and provide information on the type of geographical feature represented, the coordinates of the region's centroid, and the x and y coordinates of the region's center. These geographic coordinates are presented using the WGS 84 coordinate reference system, with axis order longitude first, followed by latitude.

Two special columns, '_extract_date' and '_row_number', provide additional information about the data extraction process. The '_extract_date' column records the date on which the specific row's data was extracted from the source, while the '_row_number' column indicates the row number in the data extracted from the source on the specified extract date. Together, these two columns uniquely identify each row in the table.

The information in this table can be utilized in various ways in data analytics. For instance, the geospatial data can be used to conduct geospatial data analytics, which could help in studying the geographical distribution and characteristics of the regions. The '_extract_date' and '_row_number' can be used to track data changes over time, allowing for trend analysis and data quality assessments. The regional information can be used for demographic studies or in combination with other datasets for more complex analyses, such as socio-economic or environmental studies.