Type

 

dataset

813 record(s)

 

Type of resources

Available actions

Topics

Keywords

Contact for the resource

Provided by

Years

Formats

Representation types

Update frequencies

Status

Scale

Resolution

From 1 - 10 / 813

  • 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).

  • 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.

  • Air traffic network-product is a link-knot routing dataset compliant with INSPIRE requirements. It includes f.ex. flight routes and aerodromes. Data shall not be used for operational flight activities or flight planning.

  • The Topographic database is a dataset depicting the terrain of all of Finland. The key objects in the Topographic database are the road network, buildings and constructions, administrative borders, geographic names, land use, waterways and elevation. Aerial photographs, scanning data and data provided by other data providers are utilised in updating the Topographic database. The updating is done in close cooperation with the municipalities. Field checks in the terrain are also needed to some extent, mostly as regards the classification of features. The topographic database is used in the production of other map products and in various optimisation tasks. 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.

  • 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.

  • Lack of spatial soil data in digital form has been a primary obstacle in establishing European policies on land use and environmental protection. Abundant data on soil characteristics exist in Finland but have been scattered among various sources, making it difficult for authorities to make country-wide presentations and predictions.The objective of the project was to create georeferenced soil map and database according to the instructions of the European Soil Bureau using data from existing databases and collecting some new data. The basis of the work was a geological map of quaternary deposits, which describes the soil at a depth of 1 metrem (parent material) according to the Finnish classification based on the concentration of organic matter and the texture of mineral material. Primary research topics included generalization methodology of soil polygons with GIS technology, calculation of soil characteristics needed in the database and computerizing the existing non-digital soil information. It was proved that aerial geophysics can be used for separation of shallow peats from deep peat soils and muddy soils and other wet areas can be identified. Soil names according to the FAO/Unesco system and the World Reference Base for Soil Resources (WRB-2014) were derived from the soil names of the Finnish soil classification system and geophysical data. Soilscape (Soil Mapping Units) of Finland with WRB-2014 soil classification, intented to be used in European scale e.g to delineate risk areas mentioned in soil framework directive proposal.

  • Elevation zones is a raster dataset that visualises elevation of the terrain. The product covers the whole of Finland. There are four product versions available in which the pixel sizes are 32, 64, 128 and 512 metres. The dataset does not contain elevation values; it is a colour image that visualises the height of the terrain above sea level as zones. The product Elevation zones is available as a version that covers the whole country and as versions that cover a certain area. 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.

  • The location of the real property unit is shown on the cadastral index map. On the map, there are property and other register unit boundaries, boundary markers and property identifiers. The Land Information System of Finland contains attribute data related to property. Further information (in Finnish): http://www.maanmittauslaitos.fi/kiinteistot/asiantuntevalle-kayttajalle/kiinteistotiedot-ja-niiden-hankinta 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

  • Elevation model 2 m is a model depicting the elevation of the ground surface in relation to sea level. Its grid size is 2 m x 2 m. The dataset is based on laser scanning data, the point density of which is at least 0.5 points per square metre. The product is available as versions with different coverage areas through-out the whole country but not as comprehensive. New data is added to the dataset continuously. Eleva-tion model 10 m is available as a version that covers the whole of Finland. Elevation model 2 m is produced in two quality classes: the elevation accuracy in class I is on average 0.3 metres and the elevation accuracy in class II varies between 0.3 metres and one metre. 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