KUVAUS: Tampereen rakennelmien 2D-seinälinjat aluemuotoisena geometriana korkeudeltaan nollattuna. Mukana vain valmiit ja julkisesti näytettävät rakennelmat. Virkistys aamuisin klo 6.35. KATTAVUUS: Tampereen kaupunkiseutu PÄIVITYS: Aineistoa päivitetään jatkuvasti uusien rakennelmien valmistuessa. YLLÄPITOSOVELLUS: StellaMap (DGN-tiedostot) ja FME KOORDINAATTIJÄRJESTELMÄ: Aineisto tallennetaan ETRS-GK24FIN (EPSG:3878) tasokoordinaattijärjestelmässä. GEOMETRIA: vektori (alue) SAATAVUUS: Aineisto on saatavilla WFS-rajapinnalta Tampereen kaupungin sisäiseen käyttöön sekä konsulteille sopimuksella/käyttöehdolla. Aineiston primäärilähde on Oracle-tietokanta. JULKISUUS: Aineisto on julkisesti katsottavissa Oskari-karttapalvelussa. TIETOSUOJA: Aineistoon ei liity tietosuojakysymyksiä. KENTÄT: -TYYPPI: Rakennelma, Muu rakennelma, Portaat tai esteettömyysluiska, Laituri, Piippu tai Allas -ALALUOKKA: Varastotila, Katos, Muu rakennelma, Maastoportaat, Autokatos, Joukkoliikenteen pysäkkikatos, Muu laituri, Venelaituri, Mainosrakennelma, Jätekatos, Muistomerkki, Savupiippu, Huvipuistolaite, Raunio, Katsomo, Muu piippu, Maatalousallas, Maauima-allas, Esteettömyysluiska, Hyppyrimäki, Keittokatos, Suihkulähde tai suihkukaivo -RAKENNELMALUOKKA_FACTA: Tulee rakennelmalle i_pyraknro perusteella Factasta (huom. ei löydy kaikilta rakennelmilta). -PYSYVA_RAK_NRO_FACTA: Rakennelman pysyvä rakennusnumero Factasta. AINEISTOSTA VASTAAVA TAHO: Tampereen kaupunki, Paikkatietoyksikkö, paikkatieto_tuki@tampere.fi

FIN Aineiston tarkoituksena on: -Identifioida tie- ja rata-alueet, joiden varrella esiintyy uhanalaisia ja silmälläpidettäviä lajeja -Identifioida tie- ja rata-alueet, joiden varrella esiintyy hyviä elinvoimaisia niittyindikaattorilajeja (hyönteisten mesi- ja ravintokasveja) -Identifioida tie- ja rata-alueet, joiden varrella esiintyy suojelualueita -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua -Identifioida tie- ja rata-alueet, joiden varrella esiintyy komealupiinia tai kurtturuusua uhanalaisten lajien lisäksi -> Löytää herkät alueet ja paikallistaa vieraslajien uhka Tieto esitetään 1 kilometrin ruuduissa. Aineistosta on julkaistu kaksi erillistä versiota. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Avoin versio, jonka lajitietoa on karkeistettu mahdollisista herkistä lajeista johtuen. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0) ja sitä saa käyttää lisenssiehtojen mukaisesti -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Alkuperäinen karkeistamaton versio. Tämä versio on vain viranomaiskäyttöön eikä kyseistä aineistoa saa jakaa Aineistosta on tehty tarkempi menetelmäkuvaus https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf sekä muuttujaseloste https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx ENG The purpose of the material is to: -Identify road and rail areas that have nearby observations of endangered and near threatened species -Identify road and rail areas with good meadow indicator plant species -Identify road and rail areas along which there are protected areas -Identify the road and rail areas along which there are observations of Lupinus polyphyllus or Rosa rugosa observations -Identify the road and rail areas along which there are Lupinus polyphyllus or Rosa rugosa observations in addition to sensitive species -> Finds sensitive areas and identify the overall threat of alien species The data is presented in 1-kilometer square grid cells. There are two separate versions of the data. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_avoin: Open access version, in which its species-related parts have been simplified due to data restriction issues. The material belongs to Syke's open materials (CC BY 4.0) and may be used in accordance with the license terms. -VaylanvarsienVieraslajitJaArvokkaatElinymparistot_kayttorajoitettu: Original version. This version is only for official use and the material in question may not be shared. A more precise description about the data procedures can be found from (In Finnish) https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_Menetelmakuvaus.pdf Furthermore, all the variables in the data are explained in this bilingual variable description https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/VierasVayla_VariableDescription.xlsx This dataset was updated with the newest species observations on 10/2023 and 11/2024 Process code for this can be found from https://github.com/PossibleSolutions/VierasVayla_SpeciesUpdate

The Arctic SDI Gazetteer Service is a service that contains authoritative place names data from the arctic area. The service can be used for searching place names and performing reverse geocoding. The service contains about 2.87 million place name locations with about 3.15 million place names. It contains data from following sources: * Canada (Natural Resources Canada, updated: 02/2018) * Denmark (including Greenland) (SDFE, updated: 05/2017) * Finland (National Land Survey of Finland, updated: 04/2017) * GEBCO Undersea feature names gazetteer (updated: 04/2019) * Iceland (National Land Survey of Iceland, updated: 08/2017) * Norway (Norwegian Mapping Authority, updated: 08/2017) * Russia (Russian Mapping Agency, updated: 04/2019) * Sweden (Swedish National Mapping Agency, updated: 05/2017) * USA (US Geological Survey, updated: 05/2017)

This dataset contains points of information describing the location and size of spills of mineral oil observed during aerial surveillance flights by HELCOM Contracting Parties during 1998-2023. The data covers detections from fixed-wing aircraft only. Since 2014 Contracting Parties have also reported spills of other substances and unknown substances. The purpose of the regional aerial surveillance is to detect spills of oil and other harmful substances and thus prevent violations of the existing regulations on prevention of pollution from ships. Such illegal spills are a form of pollution which threatens the marine environment of the Baltic Sea area. Further information on detected spills in the Baltic Sea area and HELCOM aerial surveillance activities can be found at http://www.helcom.fi/baltic-sea-trends/maritime/illegal-spills/ and https://helcom.fi/action-areas/response-to-spills/aerial-surveillance/ The dataset contains the following information: Country Year Spill_ID = A unique code which will enable each individual spill to be individually identified FlightType = The type of flight the detection was made during: National = "N", CEPCO = "C", Super CEPCO = "SC", Tour d’Horizon = “TDH” Date = The date of the detection (dd.mm.yyyy) Time_UTC = The time of the detection in UTC (hh:mm) Wind_speed = The wind speed at the time of the detection (m/s) Wind_direc = The wind direction in degrees at the time of the detection (degrees) Latitude = The latitude of the detection (decimal degrees, WGS84) Longitude = The longitude of the detection (decimal degrees, WGS84) Length__km = The length of the detection (km) Width__km = The width of the detection (km) Area__km2_ = The area of the detection (km2) Spill_cat = Spill/pollution category: Mineral Oil = “Oil", Other Substance = "Other substance" , "Unknown substance" = “Unknown” EstimVol_m = If Spill_cat="Oil", then estimated min. volume of oil spill. Volume of the detection confirmed/observed as mineral oil as calculated using the Bonn Agreement Oil Appearance Code using the lower figure (BAOAC minimum) in m3. Vol_Category = Category of the detection: <0,1m3 = “1”, <0,1-1m3 = “2”, 1-10 m3 = “3”, 10-100 m3 = “4”, >100 m3 = “5” Type_substance = If Spill_cat="Other substance" or "Unknown. Product name or type of OS or GAR substances that could be identified (in case of known polluter, or via visual identification - cf. BAOAC Atlas). - Examples for OS: vegetable oils (palm oil sun flower oil, soya oil etc.), fish oil, molasses, various chemicals (methanol, biodiesels/FAME, toluene, paraffines etc.); Examples of GAR: solid cargo residues (e.g. coal residues), plastics, fish nets, … OR "Unknown" (in case the type of substance could not be identified) Polluter = Type of polluter source: Offshore Installation = “Rig”, Vessel = “Ship”, Other Polluter or source (e.g. land based source) = “Other”, Unknown = “Unknwon” (in case of an “orphan” spill that cannot be linked to a polluter) Remarks = Any additional information to inform on particular situations Description of marine litter sightings

NLS-FI INSPIRE Download Service (WFS) for Cadastral Parcels Theme is an INSPIRE compliant direct access Web Feature Service. It contains the following INSPIRE feature types: BasicPropertyUnit, CadastralParcel, CadastralBoundary. The service is based on the NLS-FI INSPIRE Cadastral Parcels dataset. The dataset is administrated by the National Land Survey of Finland.

The Finnish Uniform Coordinate System (in Finnish Yhtenäiskoordinaatisto, YKJ) has been used in biological observation mapping since the 1970s. Based on YKJ, Finland is divided in square-shaped areas, the size of which are determined according to the needs of the study. The area division used in national biomonitoring is 10 km x 10 km squares, but in some cases 1 km x 1 km and 100 m x 100 m YKJ squares are also used. This data set includes XY-lines that form square grid in four scales according to Unified Coordinate System (100 m - 100 km), with identifiers describing each square.

The raw materials of forest chips are small-diameter trees from thinning fellings and logging residues and stumps from final fellings. The harvesting potential consists of biomass that would be available after technical and economic constraints. Such constraints include, e.g., minimum removal of energywood per hectare, site fertility and recovery rate. Note that the techno-economic potential is usually higher than the actual availability, which depends on forest owners’ willingness to sell and competitive situation. The harvesting potentials were estimated using the sample plots of the 12th national forest inventory (NFI12) measured in the years 2014–2018. 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; Hirvelä et al. 2017; http://mela2.metla.fi/mela/tupa/index-en.php). 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). Future potentials were assumed to materialize when the industrial roundwood fellings followed the level of maximum sustained yield (79 mill. m3 in this calculation). The maximum sustained yield was 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 potential for energywood from thinnings was calculated separately for all the energywood from thinnings (Stemwood for energy from thinnings) and for material that does not fulfill the size-requirements for pulpwood (Stemwood for energy from thinnings (smaller than pulpwood-sized trees)). Note that the decision whether pulpwood-sized thinning wood is directed to energy or industrial use, is based on the optimisation by MELA. The minimum top diameter of pulpwood in the calculation was 6.3 cm for pine (Pinus sylvestris) and 6.5 cm for spruce (Picea abies) and broadleaved species (mainly Betula pendula, B. pubescens, Populus tremula, Alnus incana, A. glutinosa and Salix spp.). The minimum length of a pulpwood log was assumed at 2.0 m. Energywood could be harvested as whole trees or as delimbed. The dry-matter loss in the supply chain was assumed at 5%. The potentials for logging residues and stumps were calculated as follows: The crown biomass removals of clear fellings were obtained from MELA. According to harvesting guidelines for energywood (Koistinen et al. 2016) mineral soils classified as sub-xeric (or weaker) and peatlands with corresponding low nutrient levels were left out from the potentials. Next, technical recovery rates were applied (70% for logging residues and 82-84% for stumps) (Koistinen et al. 2016; Muinonen et al. 2013). Finally, a dry-matter loss of 20% and 5% was assumed for residues and stumps, respectively. The techno-economical harvesting potentials were first calculated for nineteen Finnish regions and then distributed on a raster grid at 1 km × 1 km resolution by weighting with Multi-Source NFI biomasses as described by Anttila et al. (2018). The potentials represent time period 2026-2035 and are presented as average annual potentials in solid cubic metres over bark. References Äijälä O, Koistinen A, Sved J, Vanhatalo K, Väisänen P. 2014. Metsänhoidon suositukset. [Guidelines for sustainable forest management]. Metsätalouden kehittämiskeskus Tapion julkaisuja. Anttila P., Nivala V., Salminen O., Hurskainen M., Kärki J., Lindroos T.J. & Asikainen A. 2018. Regional balance of forest chip supply and demand in Finland in 2030. Silva Fennica vol. 52 no. 2 article id 9902. 20 s. https://doi.org/10.14214/sf.9902 Hirvelä, H., Härkönen, K., Lempinen, R., Salminen, O. 2017. MELA2016 Reference Manual. Natural Resources Institute Finland (Luke). 547 p. 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]. Tapion julkaisuja. 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 Fennica 47(4) article 1022. https://doi.org/10.14214/sf.1022. Siitonen M, Härkönen K, Hirvelä H, Jämsä J, Kilpeläinen H, Salminen O et al. 1996. MELA Handbook. 622. 951-40-1543-6.