Statistical Region Data Collection 4500k
Source
Tilastokeskus
Description
The dataset collection in question includes a series of related data tables which originate from the Statistics Finland (Tilastokeskus) website, based in Finland. The collection contains numerous tables that encompass a variety of years, providing a comprehensive and chronological perspective of the data. The data in this collection is sourced from the Statistics Finland's service interface (WFS). Therefore, the dataset collection provides a rich source of statistical data, collated and organized into tables, offering a detailed exploration of specific areas over a range of years. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).
Keywords
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Municipalities at 1:4,500,000 Scale
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Description
The table belongs to a dataset collection, and it contains data on Municipalities at a detailed scale of 1:4,500,000. Originating from the website of Statistics Finland ('Tilastokeskus'), the table features several columns that carry valuable information. Two special columns in this table are '_extract_date' and '_row_number', which contain the date when the data was extracted and the corresponding row number in the data extracted on that particular date, providing a unique identifier for each row.
The table also contains multiple 'geom_' columns, which hold geographic information useful for geospatial data analytics. For instance, 'geom_geojson' contains Geographical JSON data, 'geom_geotext' and 'geom_centroid' provide geographical text and centroid points, and 'geom_center_x' and 'geom_center_y' display the geographical center coordinates. All these geographic coordinates are presented using the WGS 84 coordinate reference system, with longitude listed first, followed by latitude.
Other columns such as 'gml_id', 'kunta', 'name', 'namn', 'nimi', and 'vuosi' offer specific details about each municipality, including its unique ID, municipality code, and names in different languages, alongside the year of data extraction.
This table can serve as a rich resource for data analytics. For instance, the geospatial data can be used to map the municipalities of Finland, track changes over time, or analyze spatial patterns and correlations. The '_extract_date' and '_row_number' can be useful in tracking data extraction workflows and ensuring data integrity.