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  • NLS-FI INSPIRE Geographical Names Theme Dataset is a dataset depicting the Named Places and Geographical Names covering the whole of Finland. It contains the following INSPIRE feature types: NamedPlace The elements are updated weekly. The dataset is based on the Geographic Names Register of the National Land Survey of Finland: http://www.paikkatietohakemisto.fi/geonetwork/srv/fin/catalog.search#/metadata/eec8a276-a406-4b0a-8896-741cd716ade6 The dataset is available via the NLS-FI INSPIRE Download Service (WFS) for Geographical Names Theme and it can be viewed via the NLS-FI INSPIRE View Service (WMS) for Geographical Names.

  • The themes of the Topographic database and Topographic map raster series (scale 1:10,000) have been compiled into seven theme entities, theme rasters, required by the Inspire directive: hydrography, elevation, traffic network, land use, land cover, place names and buildings. In addition to the above, the theme rasters also include the municipal division, road names and map sheet division as separate themes. The theme rasters have not been implemented according to the imaging technology defined in the Inspire directive. They will be produced later. The product belongs to the open data of the National Land Survey of Finland. More information: Topographic data and how to acquire it http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/topographic-data-and-how-acquire-it.

  • NLS orthophotos are an aerial photo data set covering the whole of Finland. The geometry of the orthophotos corresponds to a map. NLS orthophotos are updated every 3 to 10 years. The product is a part of the open data of the National Land Survey of Finland. More information: Acquisition of open data http://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/topographic-data-and-how-acquire-it

  • The GTK’s Mineral Deposit database contains all mineral deposits, occurrences and prospects in Finland. Structure of the new database was created in 2012 and it is based on global geostan-dards (GeoSciML and EarthResourceML) and classifications related to them. The database is in Oracle, data products are extracted from the primary database. During 2013 GTK’s separate mineral deposit databases (Au, Zn, Ni, PGE, U, Cu, Industrial minerals, FODD, old ore deposit database) were combined into a single entity. New database contains extensive amount of information about mineral occurrence feature along with its associated commodities, exploration activities, holding history, mineral resource and re-serve estimates, mining activity, production and geology (genetic type, host and wall rocks, min-erals, metamorphism, alteration, age, texture, structure etc.) Database will be updated whenever new data (e.g. resource estimate) is available or new deposit is found. Entries contain references to all published literature and other primary sources of data. Also figures (maps, cross sections, photographs etc.) can be linked to mineral deposit data. Data is based on all public information on the deposits available including published literature, archive reports, press releases, companies’ web pages, and interviews of exploration geologists. Database contains 33 linked tables with 216 data fields. Detailed description of the tables and fields can be found in separate document. (http://tupa/metaviite/MDD_FieldDescription.pdf) The data products extracted from the database are available on Mineral Deposits and Exploration map service (http://gtkdata.gtk.fi/MDaE/index.html) and from Hakku -service (http://hakku.gtk.fi).

  • The data on acid sulfate soils in 1:250 000 scale contains material generated since 2009 on the existence and properties of sulfate soils on the Finnish coastal areas and their drainage basins roughly up to the highest shoreline of the ancient Litorina Sea. The data contains the following levels: - Acid sulfate soils, 1:250 000 maps o Probability of the existence of acid sulfate soils o Probability of the existence of coarse-grained acid sulfate soils - Acid sulfate soils, profile points on 1:250 000 maps - Acid sulfate soils, survey points on 1:250 000 maps - Acid sulfate soils, profile point fact sheets on 1:250 000 maps The data gives a general outlook on the properties and occurrence of acid sulfate soils. The regional existence of sulfate soils is presented as a regional map plane using a four-tiered probability classification: high, moderate, low and very low. These classifications are complemented with regional planar data on whether the acid sulfate soil is coarse-grained, since its properties are significantly different from typical fine-grained sulfate soils. The drilling point (profile points and survey points) observations and analysis data are presented as point-like data on the map and as profile point fact sheets linked to points The survey data can be utilised, for example, in the planning and execution of land use and water management as required by environmental protection and land use. The survey scale is 1:20 000 – 1:50 000. The observation point density is 1–2 / 2 km² on average, and the minimum area of the region-like pattern is usually 6 hectares. The surveys collected data on the lithostratigraphy, existence of sulfide and the depth where found, and the soil pH values. The survey depth is three metres. The laboratory analyses included the determination of elements with the ICP-OES method and pH incubation. The data is published in GTK’s Acid Sulfate Soils map service.

  • Bedrock of Finland 1:200 000 is a unified bedrock map dataset covering the whole Finland. It has been compiled by generalising the scale-free bedrock map feature dataset. The dataset consists of a lithological/stratigraphic geological unit polygon layer and linear layers, in which faults, diverse overprinting lines and dykes are represented. The dataset also includes an origin of the data and a quality estimation of the data polygon layers. The stratigraphic geological unit polygon layer includes lithological coding, geological time period and hierarchical lithostratigraphical or lithodemic classification as attributes in accordance with the Finnish database for stratigrafic geological units (Finstrati). The line layers have their own hierarchical classification. The data are at 1:200 000 scale, which indicates that the main part of the scale-free data have been generalised to correspond to a product at a scale of 1:200 000. Those areas where the source data is coarser than 1:200 000 have not been generalised. Coordinate reference system of the dataset was transformed in March 2013. The transformation from Finnish National Grid Coordinate System (Kartastokoordinaattijärjestelmä, KKJ) Uniform Coordinate Frame to ETRS-TM35FIN projection was done by using the three-dimensional transformation in accordance with the recommendations for the public administration JHS154.

  • VRK INSPIRE Addresses Theme Dataset is a dataset depicting the Addresses of Buildings in Finland. It contains the following INSPIRE feature types: Address, AdminUnitName, PostalDescriptor, ThoroughfareName. The elements are updated weekly. The dataset is based on "Väestötietojärjestelmän rakennus- ja huoneistotiedot" by the Population Register Centre (Väestörekisterikeskus). The dataset is available via the VRK INSPIRE Download Service (WFS) for Addresses Theme and it can be viewed via the VRK INSPIRE View Service (WMS) for Addresses.

  • Road traffic accidents involving personal injury known to the police in Finland and reported to Statistics Finland, which have co-ordinate data. The data cover the following information: vvonn = year of the accident kkonn = month of the accident kello = time of the accident vakav = seriousness of the accident: 1 = accident resulting in death, 2 = accident resulting in injury, 3 = accident resulting in serious injury onntyyppi = type of accident: 0 = same direction of travel (going straight), 1 = same direction of travel (turning), 2 = opposite direction of travel (going straight), 3 = opposite direction of travel (turning), 4 = intersecting direction of travel (going straight), 5 = intersecting direction of travel (turning), 6 = pedestrian accident (on pedestrian crossing), 7 = pedestrian accident (elsewhere), 8 = running off the road, 9 = other accident lkmhapa = number of passenger cars and vans in the accident lkmlaka = number of buses and lorries in the accident lkmjk = number of pedestrians in the accident lkmpp = number of cyclists in the accident lkmmo = number of mopeds in the accident lkmmp = number of motor cycles in the accident lkmmuukulk = number of other vehicles in the accident x = x co-ordinate of the accident y = y co-ordinate of the accident The general Terms of Use must be observed when using the data: http://tilastokeskus.fi/org/lainsaadanto/copyright_en.html.

  • The database consists of three components: "Published age determination”, ”Published Sm-Nd isotope data" and "Pb isotope data on galena". The "Published age determination" database is based on age determinations, which comprise predominantly U-Pb zircon data produced at the Geological Survey of Finland since 1960’s. For igneous rocks the age register contains radiometric ages mostly interpreted as primary ages. The information given consists of location data, rock type, method, mineral analyzed, age results, comments and references. "Published Sm-Nd isotope data" comprise Sm-Nd data procuded at GTK since 1981, which mostly are used to constrain the origin of crust. "Pb isotope data on galena" gives results produced at GTK since 1970's, and include also previously unpublished data.

  • The technical harvesting potential of logging residues and stumps from final fellings can be defined as the maximum potential procurement volume of these available from the Finnish forests based on the prevailing guidelines for harvesting of energy wood. The potentials of logging residues and stumps have been calculated for fifteen NUTS3-based Finnish regions covering the whole country (Koljonen et al. 2017). The technical harvesting potentials were estimated using the sample plots of the eleventh national forest inventory (NFI11) measured in the years 2009–2013. First, a large number of sound and sustainable management schedules for five consecutive ten-year periods were simulated for each sample plot using a large-scale Finnish forest planning system known as MELA (Siitonen et al. 1996; Redsven et al. 2013). MELA simulations consisted of natural processes and human actions. The ingrowth, growth, and mortality of trees were predicted based on a set of distance-independent tree-level statistical models (e.g. Hynynen et al. 2002) included in MELA and the simulation of the stand (sample plot)-level management actions was based on the current Finnish silvicultural guidelines (Äijälä et al. 2014) and the guidelines for harvesting of energy wood (Koistinen et al. 2016). Final fellings consisted of clear cutting, seed tree cutting, and shelter-wood cutting, but only the clear-cutting areas were utilized for energy wood harvesting. As both logging residues and stumps are byproducts of roundwood removals, the technical potentials of chips have to be linked with removals of industrial roundwood. Future potentials were assumed to materialize when the industrial roundwood fellings followed the level of maximum sustainable removals. The maximum sustainable removals were defined such that the net present value calculated with a 4% discount rate was maximized subject to non-declining periodic industrial roundwood and energy wood removals and net incomes, and subject to the saw log removal remaining at least at the level of the first period. There were no constraints concerning tree species selection, cutting methods, age classes, or the growth/drain ratio in order to efficiently utilize the dynamics of forest structure. The felling behaviour of the forest owners was not taken into account either. For the present situation in 2015, the removal of industrial roundwood was assumed to be the same as the average level in 2008–2012. Fourth, the technical harvesting potentials were derived by retention of 30% of the logging residues onsite (Koistinen et al. 2016) and respectively by retention of 16–18% of stump biomass (Muinonen et al. 2013). Next, the regional potentials were allocated to municipalities proportionally to their share of mature forests (MetINFO 2014). Subsequently, the municipality-level potentials were spread evenly on a raster grid at 1 km × 1 km resolution. Only grid cells on Forests Available for Wood Supply (FAWS) were considered in this operation. Here, FAWS was defined as follows: First, forest land was extracted from the Finnish Multi-Source National Forest Inventory (MS-NFI) 2013 data (Mäkisara et al. 2016). Second, restricted areas were excluded from forest land. The restricted areas consisted of nationally protected areas (e.g. nature parks, national parks, protection programme areas). References Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P (2014) Metsänhoidon suositukset [Guidelines for sustainable forest management]. Metsätalouden kehittämiskeskus Tapion julkaisuja. Hynynen J, Ojansuu R, Hökkä H, Salminen H, Siipilehto J, Haapala P (2002) Models for predicting the stand development – description of biological processes in MELA system. The Finnish Forest Research Institute Research Papers 835. Koistinen A, Luiro J, Vanhatalo K (2016) Metsänhoidon suositukset energiapuun korjuuseen, työopas [Guidelines for sustainable harvesting of energy wood]. Metsäkustannus Oy, Helsinki. Koljonen T, Soimakallio S, Asikainen A, Lanki T, Anttila P, Hildén M, Honkatukia J, Karvosenoja N, Lehtilä A, Lehtonen H, Lindroos TJ, Regina K, Salminen O, Savolahti M, Siljander R (2017) Energia ja ilmastostrategian vaikutusarviot: Yhteenvetoraportti. [Impact assessments of the Energy and Climate strategy: The summary report.] Publications of the Government´s analysis, assessment and research activities 21/2017. Mäkisara K, Katila M, Peräsaari J, Tomppo E (2016) The Multi-Source National Forest Inventory of Finland – methods and results 2013. Natural resources and bioeconomy studies 10/2016. Muinonen E, Anttila P, Heinonen J, Mustonen J (2013) Estimating the bioenergy potential of forest chips from final fellings in Central Finland based on biomass maps and spatially explicit constraints. Silva Fenn 47. Redsven V, Hirvelä H, Härkönen K, Salminen O, Siitonen M (2013) MELA2012 Reference Manual. Finnish Forest Research Institute. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O, Teuri M (1996) MELA Handbook. Metsäntutkimuslaitoksen tiedonantoja 622. ISBN 951-40-1543-6.