WMS This is a description of your Web Map Server. WFS WMS GEOSERVER

NONE NONE text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html text/javascript application/json XML INIMAGE BLANK JSON JSONP This is a description of your Web Map Server. EPSG:4326 EPSG:3785 EPSG:3857 EPSG:900913 EPSG:32647 EPSG:32736 CRS:84 -180.0 180.0 -90.0 90.0 geonode:10min This dataset contains 10 minutes walking distances from the planned Turku tramway stops. This dataset was created by calculating a 10-minute journey along the Digiroad network at a speed of 4 km/h. This dataset was produced to be used in web-map created for MAAN7771 geo-visualization challenge group work. features 10min EPSG:3067 CRS:84 22.216609352945575 22.370171833263353 60.43301321460108 60.46213093470677 other other other other text/xml other other geonode:aanaar_saami_language_1900 features aanaar_saami_language_1900 EPSG:3857 CRS:84 26.17936269161342 28.82472047698809 68.6496100100299 69.43469275198237 geonode:adna_data_points_3ed3d738471797002beabe40f991c267 Genetic data collection locations from Uralic and neighbouring areas. This dataset is compiled by Anne-Mai Ilumäe from several resources and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). Additional references are found in the point information. adna_data_points_3ed3d738471797002beabe40f991c267 genetics ancient DNA features EPSG:4326 CRS:84 7.75 54.070923 45.99 69.222171 other other other other text/xml other other geonode:akkala_saami_language_1900 features akkala_saami_language_1900 EPSG:3857 CRS:84 29.657800221897272 34.158048428495256 67.28290055291826 68.34474851060504 geonode:archipelago_development_preferences4006c3889fc7 This dataset contains information about development preferences in the Archipelago Sea area. The data was collected during summer 2022 with Maptionnaire online platform. Development preferences were ready given options and respondents mapped as points places where they identify a specific development preference. Participants had the opportunity to locate as many points as they wanted. Document contains attribute metadata of respondents, development preferences and coordinates. The data was produced by the University of Turku, Department of Geography and Geology in collaboration with Metsähallitus and the Archipelago Sea Biosphere Reserve. This data covers geographically Archipelago Sea area and its municipalities. archipelago_development_preferences4006c3889fc7 participatory mapping features Development preference Archipelago Sea National Park Archipelago Sea PPGIS EPSG:4326 CRS:84 19.507332 23.570627 59.5486 60.723339 other other other other text/xml other other geonode:archipelago_landscape_values_3b633c2a37562d2a1f4f986fe7241a4c This dataset contains information about landscape values in the Archipelago Sea area. The data was collected during summer 2022 with Maptionnaire online platform. Values were ready given options and respondents mapped as points places where they identify a specific landscape value. Participants had the opportunity to locate as many points as they wanted. Document contains attribute metadata of respondents, values and coordinates. The data was produced by the University of Turku, Department of Geography and Geology in collaboration with Metsähallitus and the Archipelago Sea Biosphere Reserve. This data covers geographically Archipelago Sea area and its municipalities. PPGIS Landscape values Archipelago Sea archipelago_landscape_values_3b633c2a37562d2a1f4f986fe7241a4c participatory mapping features Archipelago Sea National Park EPSG:4326 CRS:84 19.890143 23.580319 59.721911 60.996031 other other other other text/xml other other geonode:bankfull_model_of_tana_catchment_river_network This dataset contains the simulated area that corresponds to bankfull stage in the main Tana River stem and principal tributaries. This area is obtained thanks to the FluvialCorridor toolbox for ArcGIS that can be found in the following link: https://github.com/EVS-GIS/Fluvial-Corridor-Toolbox-ArcGIS Tenojoki bankfull Tana River features bankfull_model_of_tana_catchment_river_network EPSG:5048 CRS:84 23.68385390769338 28.61454114829112 68.67340670506056 70.52394398889217 other other other other text/xml other other geonode:birch_event_date_a9ccb31aae8023dc111b1c01bbba3b2e features birch_event_date_a9ccb31aae8023dc111b1c01bbba3b2e EPSG:2393 CRS:84 19.702120998359888 31.380913496247466 60.04634708355933 70.07254370724849 geonode:bronze_age_bone_artefacts_4f111719bfa01539b7fa6ce289d1e96a features bronze_age_bone_artefacts_4f111719bfa01539b7fa6ce289d1e96a EPSG:2393 CRS:84 21.63906179262352 25.95018800936949 60.47666513274223 64.89675775590503 geonode:bronze_age_bone_classified_e4c46b053358d6bda792170fe578ead3 features bronze_age_bone_classified_e4c46b053358d6bda792170fe578ead3 EPSG:2393 CRS:84 21.63906179262352 25.95018800936949 60.47666513274223 64.89675775590503 geonode:bronze_age_bronze_artefacts features bronze_age_bronze_artefacts EPSG:2393 CRS:84 17.58136949745227 30.793686744785816 59.86257048133554 68.87662329526532 geonode:bronze_age_clay_artefacts_ca91bd4c705a028b8a9e03086a39aa27 features bronze_age_clay_artefacts_ca91bd4c705a028b8a9e03086a39aa27 EPSG:2393 CRS:84 21.455733326486385 29.98257092969263 61.302944715237324 64.52346176418857 geonode:bronze_age_pottery_artefacts_0dcd2d0aa5f9d08a7642a3a4cb04b9ae features bronze_age_pottery_artefacts_0dcd2d0aa5f9d08a7642a3a4cb04b9ae EPSG:2393 CRS:84 16.742322921976136 37.004431406871326 59.882759289135485 70.07314300827473 geonode:bronze_age_pottery_artefacts_e07aef1f3649c003646dd062b197c225 features bronze_age_pottery_artefacts_e07aef1f3649c003646dd062b197c225 EPSG:2393 CRS:84 16.742322921976136 37.004431406871326 59.882759289135485 70.07314300827473 geonode:bronze_age_pottery_classified_dce188b31ffd43b5059b58875b9b23df features bronze_age_pottery_classified_dce188b31ffd43b5059b58875b9b23df EPSG:2393 CRS:84 16.742322921976136 37.004431406871326 59.882759289135485 70.07314300827473 geonode:bronze_age_pottery_main_f61b37fb934b4de897042aad434b8086 features bronze_age_pottery_main_f61b37fb934b4de897042aad434b8086 EPSG:3857 CRS:84 21.589074368188786 32.428595594982625 60.03015187656967 70.07180010740268 geonode:bronze_age_stone_tools_artefactsc6b9730e7e4e features bronze_age_stone_tools_artefactsc6b9730e7e4e EPSG:2393 CRS:84 16.315637653016154 32.82594226216952 59.751622752085005 70.07294594806827 geonode:bronze_age_stone_tools_classified41c0fc32c406 features bronze_age_stone_tools_classified41c0fc32c406 EPSG:2393 CRS:84 16.315637653016154 32.82594226216952 59.751622752085005 70.07294594806827 geonode:buildingsaoi features buildingsaoi EPSG:32737 CRS:84 39.1856785458722 39.2266730212205 -6.197498267948469 -6.134073269929412 geonode:compressed_cultural_landscape_service_density_kernel_surface_f Kernel density surface of cultural landscape services calculated from service point data from Lulanzi village located at the Southern highlands of Tanzania. The point data layer was produced in a participatory mapping campaign and it was organized in Tanzania from February to March 2016. SUSLAND GeoTIFF Landscape services compressed_cultural_landscape_service_density_kernel_surface_f participatory mapping lul_cul_k_sum Tanzania WCS ecosystem services EPSG:32736 CRS:84 35.85182151448117 35.90298861479584 -8.007777505626969 -7.947799625756473 other other other other text/xml other other geonode:compressed_cumulated_heat_sum_turku_2002_only_terrestrial_area This dataset is a raster file (tiff) which represents a cumulated heat sum in 2002 from May to August in the city region of Turku, Finland. Dataset was produced as part of HERCULES project. In addition of Turku city, the data covers municipalities of Kaarina, Lieto, Raisio and Rusko. The data represents how many degree-hours it has cumulated during May, June, July, and August of 2002 in a particular 250 m grid square if the temperature has been over 25 Celsius degrees. For example, if the temperature has been 26 Celsius degrees for five hours, it means five degree-hour in the data. If the temperature has been 27 Celsius degrees for five hours, it means ten degree-hour in the data. The cumulated temperature map has been produced using linear regression model based on about 70 TURCLIM temperature observations, CORINE land cover data, SLICES land use data, and digital elevation model. This dataset contains only terrestrial areas. Temperature compressed_cumulated_heat_sum_turku_2002_only_terrestrial_area GeoTIFF heatsum WCS Climate TURCLIM EPSG:3067 CRS:84 22.01070575647043 22.893390427628937 60.326975808928616 60.75344274351976 other other other other text/xml other other geonode:compressed_cumulated_heat_sum_turku_2002_reduced This dataset is a raster file (tiff) which represents a cumulated heat sum in 2002 from May to August in the city region of Turku, Finland. Dataset was produced as part of HERCULES project. In addition of Turku city, the data covers municipalities of Kaarina, Lieto, Raisio and Rusko. The data represents how many degree-hours it has cumulated during May, June, July, and August of 2002 in a particular 250 m grid square if the temperature has been over 25 Celsius degrees. For example, if the temperature has been 26 Celsius degrees for five hours, it means five degree-hour in the data. If the temperature has been 27 Celsius degrees for five hours, it means ten degree-hour in the data. The cumulated temperature map has been produced using linear regression model based on about 70 TURCLIM temperature observations, CORINE land cover data, SLICES land use data, and digital elevation model. This data contains both terrestrial areas and water bodies. Climate GeoTIFF heatsum Temperature WCS TURCLIM compressed_cumulated_heat_sum_turku_2002_reduced EPSG:3067 CRS:84 22.01070575647043 22.893390427628937 60.326975808928616 60.75344274351976 other other other other text/xml other other geonode:compressed_estimated_amount_of_elderly_pedestrians_in_turku This dataset estimates the amount of elderly (60 and above) pedestrians in Turku and Kaarina, SW Finland. The estimation is based on the amount of elderly inhabitants within 700 m. Dataset is created from Statistics Finland’s 250 m x 250 m Grid database combined with buildings from National land survey of Finland’s topographic database. This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF compressed_estimated_amount_of_elderly_pedestrians_in_turku Elderly person Pedestrians Transport networks Estimated_elderly_pedestrians_Turku WCS Population distribution — demography EPSG:3067 CRS:84 22.078077493152115 22.4244080252094 60.38708492063262 60.536787526455505 other other other other text/xml other other geonode:compressed_estimated_amount_of_pedestrians_in_turku This dataset estimates the amount of pedestrians in Turku and Kaarina, SW Finland. The estimation is based on the amount of inhabitants and employees within 700 m. Dataset is created from Statistics Finland’s 250 m x 250 m Grid database combined with buildings from National land survey of Finland’s topographic database. This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF WCS Elderly person Pedestrians Transport networks compressed_estimated_amount_of_pedestrians_in_turku Estimated_pedestrians_Turku Population distribution — demography EPSG:3067 CRS:84 22.07808876411908 22.424419192110012 60.387079242741194 60.53678182284941 other other other other text/xml other other geonode:compressed_land_cover_classification_of_unguja_1996_1996_lcc This Raster Dataset contains seven different land cover classes from Unguja (Island in Tanzania).  The land cover classification was done for the already geo-referenced SPOT-3 HRV image from 30.06.1996.  Training sites from 2009_lcc_landsat.img classification were modified based on the SPOT satellite images and DoSUP 1989 aerial photographs visual interpretation. Classified with maximum likelihood supervised method for radiometrically non-corrected images. The classificiation based on 203 training sites for the the 12 spectral feature, all bands have been used for the classification. The 12 spectral classes then post-classified to 7 land cover classes. WCS GeoTIFF compressed_land_cover_classification_of_unguja_1996_1996_lcc features EPSG:21037 CRS:84 39.00790307190086 39.76602177554153 -6.690720299341144 -5.624210755225356 other other other other text/xml other other geonode:compressed_land_cover_classification_of_unguja_2009_2009_lcc This raster dataset is a land cover classification of the island of Unguja, in Tanzania. The dataset consists of seven classes. The data was done using the geo-referenced Landsat TM satellite image. The Landsat TM satellite image was collected on the 1st of July 2009. The data set was produced as part of the Zanzibar Research activities of UTU. The data quality is described in more detail in the section of Data quality. "Landsat-8" GeoTIFF compressed_land_cover_classification_of_unguja_2009_2009_lcc WCS ''classification'' EPSG:21037 CRS:84 39.04041208990804 39.713144443208684 -6.535216604406475 -5.643544578882607 other other other other text/xml other other geonode:compressed_landsat_tm_image_unguja_2009 This raster dataset is a satellite imagery of the island of Unguja, in Tanzania. The satellite imagery was collected using Landsat TM satellite, which has 6 bands. The data was collected on the 1st of July 2009. The data set was produced as part of the Zanzibar Research activities of UTU. The data quality is described in more detail in the section of Data quality. "Landsat-8" GeoTIFF ''satellite imagery'' WCS compressed_landsat_tm_image_unguja_2009 EPSG:21037 CRS:84 39.04041208990804 39.713144443208684 -6.535216604406475 -5.643544578882607 other other other other text/xml other other geonode:compressed_pemba_topo_1913 No abstract provided compressed_pemba_topo_1913 WCS GeoTIFF EPSG:21037 CRS:84 39.551314878428535 39.93592494730289 -5.565825294978293 -4.7901116294176065 other other text/xml other other other other geonode:compressed_slipperiness_in_april_in_turku This dataset estimates the regional variability of slippery conditions in April in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Human health and safety April_slipperiness_Turku compressed_slipperiness_in_april_in_turku Meteorological geographical features WCS slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other text/xml other other other other geonode:compressed_slipperiness_in_december_in_turku This dataset estimates the regional variability of slippery conditions in December in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä December_slipperiness_Turku GeoTIFF Human health and safety Meteorological geographical features compressed_slipperiness_in_december_in_turku WCS slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other text/xml other other geonode:compressed_slipperiness_in_february_in_turku This dataset estimates the regional variability of slippery conditions in February in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Human health and safety April_slipperiness_Turku compressed_slipperiness_in_february_in_turku Meteorological geographical features WCS slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other text/xml other other geonode:compressed_slipperiness_in_january_in_turku This dataset estimates the regional variability of slippery conditions in January in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF compressed_slipperiness_in_january_in_turku Human health and safety Meteorological geographical features January_slipperiness_Turku WCS slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other text/xml other other geonode:compressed_slipperiness_in_march_in_turku This dataset estimates the regional variability of slippery conditions in March in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä compressed_slipperiness_in_march_in_turku Human health and safety WCS GeoTIFF slipperiness Meteorological geographical features April_slipperiness_Turku EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other text/xml other other other other other text/xml other other other other other text/xml other other geonode:compressed_slipperiness_in_may_in_turku This dataset estimates the regional variability of slippery conditions in May in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Human health and safety Meteorological geographical features May_slipperiness_Turku compressed_slipperiness_in_may_in_turku WCS slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other text/xml other other geonode:compressed_slipperiness_in_november_in_turku This dataset estimates the regional variability of slippery conditions in November in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Human health and safety compressed_slipperiness_in_november_in_turku Meteorological geographical features WCS November_slipperiness_Turku slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other other text/xml other geonode:compressed_slipperiness_in_october_in_turku This dataset estimates the regional variability of slippery conditions in April in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rThis dataset estimates the regional variability of slippery conditions in October in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppäainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Human health and safety Meteorological geographical features compressed_slipperiness_in_october_in_turku WCS October_slipperiness_Turku slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other text/xml other other geonode:compressed_slipperiness_in_september_in_turku This dataset estimates the regional variability of slippery conditions in September in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Human health and safety September_slipperiness_Turku compressed_slipperiness_in_september_in_turku Meteorological geographical features WCS slipperiness EPSG:3067 CRS:84 22.078069185104 22.424440979065036 60.38705071913932 60.53679037983412 other other other other text/xml other other geonode:compressed_slipping_risk_in_turku_for_elderly This dataset estimates the risk of a person slipping at particular location in Turku and Kaarina, SW Finland. The estimation combines average yealy slipperiness, estimated amount of elderly pedestrians and additional risk caused by slopes (slipperiness and pedestrian data are also availabe seperately). GeoTIFF Human health and safety Slipping slipperiness WCS compressed_slipping_risk_in_turku_for_elderly Slipping_risk_elderly_Turku EPSG:3067 CRS:84 22.07816674171665 22.42433920418375 60.38713456666433 60.53674250012912 other other other other text/xml other other geonode:compressed_spot_xs_1996_image_north_unguja SPOT 2 High-Resolution Visible (HRV) multispectral image (XS) scene of southern Zanzibar (Unguja) taken in June 1996. Three bands (Green, Red, NIR) Spot1996_UngujaS SPOT compressed_spot_xs_1996_image_north_unguja GeoTIFF zanzibar satellite image WCS Spot1996_UngujaN EPSG:4326 CRS:84 39.111791991 39.770355175 -6.242264162 -5.628025662 other other other other text/xml other other geonode:compressed_the_depth_model_of_pulmanki_river The data showcases the changes in depth of Pulmanki river. The dataset is part of a study made by J. Salmela, E. Kasvi, M. T. Vaaja, H. Kaartinen, A. Kukko, A. Jaakkola and P. Alho (2019). The subject of the study is morphological changes and riffle-pool dynamics in meangering rivers. The study was created due to the lack of studies of the simultaneous exploration of morphological changes and maintenance of RPS along river reaches over several consecutive years. Temporal Change compressed_the_depth_model_of_pulmanki_river GeoTIFF depth Depth Elevation Erosion meandering river WCS EPSG:3067 CRS:84 28.033703670145762 28.0444896372273 69.92547274586147 69.93404193583973 other other other other text/xml other other geonode:compressed_world_settlement_footprint_wsf_2015_v2 The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely:• The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5• The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf• The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential). GeoTIFF "inspireidentified" "urbanization" compressed_world_settlement_footprint_wsf_2015_v2 WCS EPSG:4326 CRS:84 23.98995882 26.010000399 59.989943831 62.010075242 other other other other text/xml other other geonode:compressed_yearly_slipperiness_in_turku This dataset estimates the yearly regional variability of slippery conditions in Turku and Kaarina, SW Finland. The portion of time when temperature was 0 ℃ (± 0.5 ℃) and either rainfall or snow depth were measured was used as an indication of slipperiness. Estimation is based on 19 years of measurements from dense network of 57 temperature loggers (TURCLIM). This dataset was produced as part of the Bachelor’s Thesis of Iiro Seppä GeoTIFF Yearly_slipperiness_Turku compressed_yearly_slipperiness_in_turku Human health and safety Meteorological geographical features WCS slipperiness EPSG:3067 CRS:84 22.07816674171665 22.42433920418375 60.38713456666433 60.53674250012912 other other other other text/xml other other geonode:courland_livonian_villages_4f781e558aa57ec4a8df34cfa0e307b9 features courland_livonian_villages_4f781e558aa57ec4a8df34cfa0e307b9 EPSG:3857 CRS:84 21.832023597357413 22.610362175000148 57.56189567490561 57.759711005000135 geonode:culture_heatmap This raster dataset visualizes the spatial density of culture service locations in Turku, Finland. The heatmap was generated from point-based service data processed in QGIS and it illustrates areas with higher or lower concentrations of cultural services. The dataset supports the broader analysis of whether the services are distributed evenly across socioeconomically diverse neighborhoods in Turku. As a smoothed density surface, the raster highlights general spatial patterns rather than exact service counts and is intended for exploratory visualization, accessibility assessment and comparisons between neighborhoods in the context of segregation and service availability. WCS wellbeing "Accessibility" culture_heatmap GeoTIFF EPSG:3067 CRS:84 22.158770046785662 22.422451267123957 60.39911068380731 60.495526723525856 other other other other text/xml other other geonode:cumulative_yearly_hdd_and_cdd_in_iran_over_a_time__period_from features cumulative_yearly_hdd_and_cdd_in_iran_over_a_time__period_from EPSG:4326 CRS:84 44.99527778 60.90027778 27.2142 38.55805556 geonode:east_khanty_language features east_khanty_language EPSG:3857 CRS:84 68.76321899565464 76.94278947676798 58.539348718141454 63.36197423500558 geonode:east_khanty_language_1900 features east_khanty_language_1900 EPSG:3857 CRS:84 69.91412419850363 85.72049314665148 57.137894204024235 63.630748159646615 geonode:east_mansi_language_1900 features east_mansi_language_1900 EPSG:3857 CRS:84 62.93960395096946 68.71301787927324 58.74084639959649 61.89065500439633 geonode:eastern_arc_forests_model features eastern_arc_forests_model EPSG:32737 CRS:84 35.10819876392655 38.81864097291464 -9.074811058836886 -3.2986340023156497 geonode:eastern_mari_language_1900 features eastern_mari_language_1900 EPSG:3857 CRS:84 49.118649991949866 58.27084251900715 54.63851204651446 57.7375420562045 geonode:erzya_and_moksha_language_1900 features erzya_and_moksha_language_1900 EPSG:3857 CRS:84 45.568989763516655 52.6990805310108 52.68117344592177 55.250714130865745 geonode:erzya_language_1900 features erzya_language_1900 EPSG:3857 CRS:84 42.31740771396041 53.58324911774268 52.16489261964733 55.517192575652146 geonode:finnic_branch_9c4ec3f9281ac95263d4946c31b229dd This file include the distribution of the Uralic languages grouped as Finnic branch. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). 1900 traditional finnic_branch_9c4ec3f9281ac95263d4946c31b229dd features uralic languages Finnic branch EPSG:3857 CRS:84 12.328052251566389 38.03927705039896 55.96945428985337 68.70770783483253 other other other other text/xml other other geonode:finnic_dialect_labels_0ccc8314235f83d7747145a847622e22 features finnic_dialect_labels_0ccc8314235f83d7747145a847622e22 EPSG:3857 CRS:84 12.688302000641626 37.97970545101387 56.152800513482966 68.70061648219722 geonode:finnish_dialect_areas Traditional Finnish language area in the beginning of the 1900s. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). finnish language features 1900 traditional dialects finnish_dialect_areas Finnic branch EPSG:3857 CRS:84 12.328052251566389 31.572385597321283 59.270023391384754 70.85336674821714 other other other other other text/xml other geonode:forest_enets_language features forest_enets_language EPSG:3857 CRS:84 86.08911540361943 88.3942487823056 68.60635536503821 69.47901140453918 geonode:forest_enets_language_1900 features forest_enets_language_1900 EPSG:3857 CRS:84 84.90948604060313 87.44790553154293 68.17248536487622 69.17512309952455 geonode:forest_nenets_language features forest_nenets_language EPSG:3857 CRS:84 66.77289287184948 80.19245212343083 61.81523076644432 65.91392805506699 geonode:forest_nenets_language_1900_03229d6811820604055fac29a5399a0f features forest_nenets_language_1900_03229d6811820604055fac29a5399a0f EPSG:3857 CRS:84 66.6532977906399 80.36125610240802 61.56165650281724 66.47546171351028 geonode:geonode_wsf2015_v2_dar_es_salaam_2d3b31f710f4474b1f51cf8350ff1 The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely: • The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5 • The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf • The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential). GeoTIFF Sentinel-1 geonode_wsf2015_v2_dar_es_salaam_2d3b31f710f4474b1f51cf8350ff1 land WCS EPSG:4326 CRS:84 37.989933028471384 40.010064439399365 -8.010007893908536 -5.989966314508969 other other other other text/xml other other geonode:geonode_wsf2015_v2_helsinki_dfdf79932ca0dc5680a3a96e47ca7547 The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely:• The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5• The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf• The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential). GeoTIFF geonode_wsf2015_v2_helsinki_dfdf79932ca0dc5680a3a96e47ca7547 "inspireidentified" "urbanization" WCS EPSG:4326 CRS:84 23.989958820053882 26.01000039945345 59.989943831143705 62.010075242071686 other other text/xml other other other other geonode:geonode_wsf2019_v1_dar_es_salaam_5fa5cc74a64f3804543c4b3ad87bd The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery. Data has been created by German Aerospace Center. Exact creation date is unknown, but the data is from the year 2021 and is meant to represent that year. "opendata" geonode_wsf2019_v1_dar_es_salaam_5fa5cc74a64f3804543c4b3ad87bd “global” WCS land Sentinel-2 “global settlement extent” GeoTIFF “settlement” "urbanization" Sentinel-1 EPSG:4326 CRS:84 37.989933028471384 40.010064439399365 -8.010007893908536 -5.989966314508969 other other other text/xml other other other geonode:geonode_wsf2019_v1_helsinki_1_5443cd03644b30cfad7884de20c26f7c The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery. GeoTIFF Sentinel-1 geonode_wsf2019_v1_helsinki_1_5443cd03644b30cfad7884de20c26f7c "opendata" "global" "inspireidentified" Sentinel-2 "urbanization" land "global settlement extent" WCS EPSG:4326 CRS:84 23.989958820053882 26.01000039945345 59.989943831143705 62.010075242071686 other other other other other other text/xml geonode:healthcare_service_areas_ws_satakunta This dataset contains the servce areas around public health care services offering primary and emergency care in the wellbeing services counties of Southwest Finland and Satakunta. The service areas have been calculated with four intervals (2 km, 5 km, 10 km, 20 km) with the distances being along roads. Service area polygons were each calculated around one health centre, and same distance polygons have then been merged. Polygons were clipped by healtcare region areas and islands without health care center and road connection to mainland were clipped out. The purpose of the dataset is to provide an overview of the availability of healthcare in the counties of southwest Finland. The dataset was produced as a practical for the course Geospatial Data Management and Visualization in the University of Turku (2025) by the group Mapmaniacs. features healthcare_service_areas_ws_satakunta EPSG:4326 CRS:84 21.2129527 23.84185114943625 59.920571885946885 62.30878787016974 other other other other other text/xml other geonode:healthcare_service_points_ws_satakunta This dataset contains Information about public health service point locations in Southwest Finland and Satakunta. Each point contins information about the municipality, region, type, website link and the opening hours. The purpose of the dataset is to identify large gaps between care needs and the accessibility or availability of care services. The dataset was produced as a practical for the course Geospatial Data Management and Visualization in the University of Turku (2025) by the group Mapmaniacs. All public health care centres in the areas were mapped, that were active at the data collecting time. features healthcare_service_points_ws_satakunta EPSG:4326 CRS:84 21.35482132761559 23.587464264192512 60.020409626227355 62.143595992093594 other text/xml other other other other other geonode:hill_mari_language_1900 features hill_mari_language_1900 EPSG:3857 CRS:84 45.487199090605316 47.58985639398226 55.91872757994185 56.94078243037673 geonode:hospital_points_fixed The dataset contains service point information of hospitals in Southwest Finland. The data was modified from Lounaistieto service point dataset. The service points were reduced to include only those hospitals that are operational in 2025. The attribute table contains information about the locations, addresses, opening hours, and contact details of the service points. features hospital_points_fixed EPSG:3067 CRS:84 21.42331067978817 23.127348869767356 60.318063525695045 60.854050274470865 other other other other text/xml other other geonode:hospitals_by_car_fixed The dataset contains hospital accessibility layer by car in Southwest Finland. The layer represents travel time lengths from hospital service points within a one-hour distance, at five-minute intervals. The calculation of travel times utilized the road and speed limit network provided by Digiroad, as well as the service point data from Lounaistieto. features hospitals_by_car_fixed EPSG:3067 CRS:84 21.14191170792343 23.971260992155592 59.92445267223001 61.17145356798142 other other other other text/xml other other geonode:hospitals_by_walking_fixed The dataset contains hospital accessibility layer by walking in Southwest Finland. The layer represents travel time lengths from hospital service points within a one-hour distance, at five-minute intervals. The calculation of travel times utilized the road network provided by Digiroad, as well as the service point data from Lounaistieto. features hospitals_by_walking_fixed EPSG:3067 CRS:84 21.340103242177165 23.20656854503392 60.275022133342986 60.894061546642156 other other other other text/xml other other geonode:hungarian_branch features hungarian_branch EPSG:3857 CRS:84 15.933032033054559 26.962805395463896 45.48314228465055 48.86494804983776 geonode:ingrian_and_votic_villages features ingrian_and_votic_villages EPSG:3857 CRS:84 28.086181846021162 29.003177212247166 59.43581796215138 59.83018255359459 geonode:ingrian_language_1900 features ingrian_language_1900 EPSG:3857 CRS:84 28.061755290282154 30.584904480652977 59.00926581010561 60.00263092700017 geonode:iron_age_clay_artefacts_c0ca9eea94fc0d5539201fe1179ca47f features iron_age_clay_artefacts_c0ca9eea94fc0d5539201fe1179ca47f EPSG:2393 CRS:84 22.286925174545605 27.337389952678752 60.459986207536986 61.70434230879498 geonode:iron_age_pottery_artefacts_50e6ebfb71dceb719e8c5c7a98692f02 features iron_age_pottery_artefacts_50e6ebfb71dceb719e8c5c7a98692f02 EPSG:2393 CRS:84 -180.0 180.0 59.64166393198133 68.42645077606133 geonode:iron_age_pottery_classified_308ef726a4a76a69d9c0bc337aceed13 features iron_age_pottery_classified_308ef726a4a76a69d9c0bc337aceed13 EPSG:2393 CRS:84 -180.0 180.0 59.64166393198133 68.42645077606133 geonode:iron_age_stone_tools_classifiedb5229aea5258 features iron_age_stone_tools_classifiedb5229aea5258 EPSG:2393 CRS:84 17.912458734366705 30.774703881989012 59.73281384080405 67.98791098409033 geonode:kamas_mator_language_1900 features kamas_mator_language_1900 EPSG:3857 CRS:84 90.91678375979309 102.11506412642474 52.79163754959073 55.51556004773298 geonode:karelian_language Current distribution of Karelian language. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). features karelian_language current 2000 karelian language Finnic branch EPSG:3857 CRS:84 23.520535777570583 37.50260814830945 56.76741347264887 66.18124726600385 other other other other text/xml other other geonode:karelian_language_1900 karelian_language_1900 features EPSG:3857 CRS:84 29.58008548999946 37.51174165492562 55.96945428985337 66.98900330727233 geonode:khanty_branch features khanty_branch EPSG:3857 CRS:84 62.61293979036869 85.07288002321249 57.137894204024235 66.82569573060997 geonode:khanty_core_language_zsirai_1900 features khanty_core_language_zsirai_1900 EPSG:3857 CRS:84 62.6238223209453 81.74967340585368 58.573439069433604 66.799093792282 geonode:khanty_language_abondolo_1900_dba307bc898df0f91cc85b95199d6ada features khanty_language_abondolo_1900_dba307bc898df0f91cc85b95199d6ada EPSG:3857 CRS:84 61.965052836692315 82.27716235876423 57.847371772021226 68.53946857268168 geonode:khanty_language_haarmann_1900_a15b334fd3cc6c1d79d0f14f4a01805b features khanty_language_haarmann_1900_a15b334fd3cc6c1d79d0f14f4a01805b EPSG:3857 CRS:84 63.54253318888011 80.39580847481739 57.47369162498492 66.57470564257918 geonode:khanty_language_lytkin_1900 features khanty_language_lytkin_1900 EPSG:3857 CRS:84 63.97643821157681 85.14249138168601 57.870123812368846 67.08141759015705 geonode:kigogo_air_pollution_experiences_kernel_r250_p45_2023 This raster layer is a Kernel density surface of local community members' air pollution experiences in Kigogo, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University. The experiences were mapped as points and polygons. This Kernel density estimation surface has been produced using the point data. Parameters used: Radius 250m; pixel size 45m.  kigogo_air_pollution_experiences_kernel_r250_p45_2023 WCS participatory mapping GeoTIFF EPSG:32736 CRS:84 39.22810221329785 39.26468963113568 -6.824876060505544 -6.8126682351222385 other other other other text/xml other other geonode:kigogo_climate_stressor_experiences_2023 This vector layer represents local community members' climate stressor experiences in Kigogo, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University.  features participatory mapping kigogo_climate_stressor_experiences_2023 EPSG:4326 CRS:84 39.23042498991555 39.261978494104504 -6.823622233720209 -6.813363000000004 other other other other text/xml other other geonode:kigogo_extreme_heat_experiences_kernel_r250_p45_2023 This raster layer is a Kernel density surface of local community members' extreme heat experiences in Kigogo, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University. The experiences were mapped as points and polygons. This Kernel density estimation surface has been produced using the point data. Parameters used: Radius 250m; pixel size 45m.  kigogo_extreme_heat_experiences_kernel_r250_p45_2023 WCS participatory mapping GeoTIFF EPSG:32736 CRS:84 39.23121562549793 39.26295028735635 -6.824881985905215 -6.812332608743125 other other other other text/xml other other geonode:kigogo_flood_experiences_kernel_r250_p45_2023 This raster layer is a Kernel density surface of local community members' flood experiences in Kigogo, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University. The experiences were mapped as points and polygons. This Kernel density estimation surface has been produced using the point data. Parameters used: Radius 250m; pixel size 45m.  kigogo_flood_experiences_kernel_r250_p45_2023 WCS participatory mapping GeoTIFF EPSG:32736 CRS:84 39.229359642052 39.26477990037573 -6.826755040787312 -6.810921208410384 other other other other text/xml other other geonode:kildin_saami_language_1900 features kildin_saami_language_1900 EPSG:3857 CRS:84 32.25261453795425 38.76013149165157 67.41526463749697 69.47606028900002 geonode:komi_language_1900 features komi_language_1900 EPSG:3857 CRS:84 34.21669341997724 66.35081200206922 58.22229113496179 68.45082808110791 geonode:komi_permyak_language_1900 features komi_permyak_language_1900 EPSG:3857 CRS:84 51.461248214578795 56.13820927250469 58.22229113496179 61.000238566081784 geonode:komi_zyrian_language_1900 features komi_zyrian_language_1900 EPSG:3857 CRS:84 34.22464642671838 66.35081200206922 59.25240433430845 68.43617795233219 geonode:lahti_temperature_differences_2014_2016ac5f07809c5e Dataset includes temperature information measured from 8 locations during 1.6.2014-31.5.2016. Data includes temperature measurement for situations when the temperature difference between the measured points has been the largest. Dataset also includes the coordinates for each temperature logger in EPSG:3067. Some environmental details are also included regarding the loggers´ position. Environmental details are form SLICES2010 land usage dataset and 25m resolution DEM elevation model. Temperature measurements are done using Hobo U23-001 Pro V2 temperature and moisture loggers, measuring every 30mins, located 3m above ground level. Climate features Urban heat island Temperature lahti_temperature_differences_2014_2016ac5f07809c5e EPSG:5048 CRS:84 25.62941477614213 25.67467673882612 60.94711591043583 60.99421217818416 other other other other text/xml other other geonode:landscape_service_and_lulc_grid_information_from_ie8368b27bc98 The dataset consists of variables calculated in to 500 times 500 meters grid cells from landscape service point data and LULC polygon data. The data set covers approximately the same spatial extent as was used in the participatory mapping data collection campaign during February and March 2016. This data set was created for the purpose to study the spatial distribution and density of provisioning landscape services in the village landscape. For more information on the variables included and their calculation see the associated article: https://doi.org/10.1016/j.ecoser.2019.101056 features Tanzania Landscape services landscape_service_and_lulc_grid_information_from_ie8368b27bc98 participatory mapping ecosystem services SUSLAND EPSG:32736 CRS:84 34.99803817380199 35.084996598510386 -9.468788756218927 -9.386935481432216 other other other other text/xml other other other other other other text/xml other other geonode:landscape_service_and_lulc_grid_information_from_l9d2e914448da The dataset consists of variables calculated in to 500 times 500 meters grid cells from landscape service point data and LULC polygon data. The data set covers approximately the same spatial extent as was used in the participatory mapping data collection campaign during February and March 2016. This data set was created for the purpose to study the spatial distribution and density of provisioning landscape services in the village landscape. For more information on the variables included and their calculation see the associated article: https://doi.org/10.1016/j.ecoser.2019.101056 SUSLAND Landscape services features participatory mapping landscape_service_and_lulc_grid_information_from_l9d2e914448da Tanzania Lulanzi_grid_SUSLAND ecosystem services EPSG:32736 CRS:84 35.84710953166093 35.90648989550813 -8.012886520955172 -7.9447212492248305 other other other other text/xml other other geonode:landscape_service_and_lulc_grid_information_from_t4f2dfabeab62 The dataset consists of variables calculated in to 500 times 500 meters grid cells from landscape service point data and LULC polygon data. The data set covers approximately the same spatial extent as was used in the participatory mapping data collection campaign during February and March 2016. This data set was created for the purpose to study the spatial distribution and density of provisioning landscape services in the village landscape. For more information on the variables included and their calculation see the associated article: https://doi.org/10.1016/j.ecoser.2019.101056 Tanzania participatory mapping features SUSLAND landscape_service_and_lulc_grid_information_from_t4f2dfabeab62 ecosystem services Landscape services EPSG:32736 CRS:84 35.066814591623896 35.12609854752432 -7.877423434632053 -7.804813773423868 other other other other text/xml other other other other other other text/xml other other geonode:ludic_language features ludic_language EPSG:3857 CRS:84 33.48596219672441 34.25608906876792 60.961851366046794 62.412407738537695 geonode:ludic_language_1900 Distribution of Ludic language at the beginning of the 20th century. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). 1900 traditional features ludic_language_1900 Finnic branch ludic language EPSG:3857 CRS:84 33.207379053354394 34.794874111074854 60.95852958205102 62.798623701499466 other other other other other text/xml other geonode:lulanzi_servicepoints Landscape service (provisioning and cultural) point locations from Lulanzi village located at the Southern highlands of Tanzania. This data was produced in a participatory mapping campaign and it was organized in Tanzania from February to March 2016. participatory mapping ecosystem services features Landscape services Lulanzi_Servicepoints Tanzania SUSLAND lulanzi_servicepoints EPSG:32736 CRS:84 35.85182511748558 35.90332097245905 -8.007777505422936 -7.948318784426317 other other other other text/xml other other geonode:lule_saami_language_1900 features lule_saami_language_1900 EPSG:3857 CRS:84 12.787119988000086 22.567472027596075 65.89856442060825 68.60521067900008 geonode:mansi_branch features mansi_branch EPSG:3857 CRS:84 59.562640566552645 68.71301787927324 57.89199435796803 64.7487214998178 geonode:mansi_language Current distribution of Mansi language. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). mansi branch 2000 features mansi_language current mansi language EPSG:3857 CRS:84 57.075828823951525 69.00151403694747 59.65153786330203 64.33025769209343 other other other other text/xml other other geonode:mari_branch features mari_branch EPSG:3857 CRS:84 45.487199090605316 58.27084251900715 54.63851204651446 57.7375420562045 geonode:meadow_mari_language_1900 features meadow_mari_language_1900 EPSG:3857 CRS:84 46.813070716376515 50.34974506330391 55.859226792561884 57.35923324873539 geonode:measurements_of_water_properties_with_ysi_multipar8bb181df8d5d The data are based on the seasonal development of five surface-layer water variables (temperature, salinity, Secchi depth, chlorophyll and acidity) and the persistence of their geographical patterns at the Archipelago Sea gradient features monitoring measurements_of_water_properties_with_ysi_multipar8bb181df8d5d persistence water quality EPSG:5048 CRS:84 21.183822133814946 22.541155851764575 60.01258452755598 60.60025344131371 other other other other text/xml other other geonode:medieval_livonian_villages features medieval_livonian_villages EPSG:3857 CRS:84 21.39632437741173 25.51178169189275 56.53881579036981 58.09497749664124 geonode:medservice_exp3067 This Dataset contains information about the location of health care services, divided into Hospitals, Private and Public Health Care Service Points. The point data aims to display the current distribution of health care service points in SW Finland for the year 2024. This dataset was produced under the visualization challange of the course "MAAN 7771 Geospatial Data Management and Visualization" at the University of Turku. Data derived from Lounaistieto service points. features medservice_exp3067 EPSG:3067 CRS:84 21.2463899880135 23.60348899484453 60.14601788518395 61.021959139490484 other other other other text/xml other other geonode:moksha_language_1900 features moksha_language_1900 EPSG:3857 CRS:84 42.5259520267527 53.4117869622405 52.44675593736427 55.172502536319556 geonode:monthly_cooling_demand_cdd_in_iran41e80c7e7235 features monthly_cooling_demand_cdd_in_iran41e80c7e7235 EPSG:4326 CRS:84 44.99527778 60.90027778 27.2142 38.55805556 geonode:monthly_heating_demand_hdd_in_iran6dc3ecc896d6 features monthly_heating_demand_hdd_in_iran6dc3ecc896d6 EPSG:4326 CRS:84 44.99527778 60.90027778 27.2142 38.55805556 geonode:mordvin_branch features mordvin_branch EPSG:3857 CRS:84 42.31740771396041 53.58324911774268 52.16489261964733 55.517192575652146 geonode:msili_layer features msili_layer EPSG:2393 CRS:84 -28.039581371140518 84.08208583093764 49.88915665233486 72.9316398653882 geonode:new_dataset_ba_bronze_classified features new_dataset_ba_bronze_classified EPSG:5048 CRS:84 17.582597645769933 30.795011659050875 59.86277706392577 68.87678962653517 geonode:nganasan_language features nganasan_language EPSG:3857 CRS:84 80.54509883517058 111.51003047591954 66.81582883862177 74.01650148436198 geonode:nganasan_language_1900 features nganasan_language_1900 EPSG:3857 CRS:84 84.16914817948151 113.42577367106715 70.63919816416353 75.69559561577407 geonode:north_estonian_language_1900 features north_estonian_language_1900 EPSG:3857 CRS:84 21.832367384000143 28.195649900435367 57.78436920800013 59.670884507000075 geonode:north_khanty_language features north_khanty_language EPSG:3857 CRS:84 62.61293979036869 71.5971035758413 60.90984486754052 66.82569573060992 geonode:north_khanty_language_1900 features north_khanty_language_1900 EPSG:3857 CRS:84 62.61293979036869 71.20200181707385 60.49522815367243 66.82569573060997 geonode:north_mansi_language_1900 features north_mansi_language_1900 EPSG:3857 CRS:84 59.64552578255533 64.91823881318227 61.869479630942465 64.7487214998178 geonode:north_saami_language_1900 features north_saami_language_1900 EPSG:3857 CRS:84 14.369883660000069 31.463110554433158 67.11400076823145 71.16526927300016 geonode:northern_selkup_language features northern_selkup_language EPSG:3857 CRS:84 77.62715930548683 88.09575687523707 62.40352479101319 67.02163798032028 geonode:northern_selkup_language_1900 features northern_selkup_language_1900 EPSG:3857 CRS:84 78.8899521154942 88.25679468306006 61.69363812050884 67.20878329133923 geonode:nw_mari_language_1900 features nw_mari_language_1900 EPSG:3857 CRS:84 45.73222312180469 48.986177432732674 56.774049555540465 57.667427216559275 geonode:outdoor_activities_in_turku_places7faee136f6cf features outdoor_activities_in_turku_places7faee136f6cf EPSG:5048 CRS:84 21.66221448374929 23.087525817051137 60.16929594240696 60.98492354104273 geonode:outdoor_activities_in_turku_places_fall7214d2a9e7bf features outdoor_activities_in_turku_places_fall7214d2a9e7bf EPSG:4326 CRS:84 21.9319725 22.88649559 60.27451492 60.85555062 geonode:outdoor_activities_in_turku_routes_fallc23c91b37176 features outdoor_activities_in_turku_routes_fallc23c91b37176 EPSG:4326 CRS:84 22.09153175 22.46918678 60.37891469 60.63119623 geonode:outdoor_activities_in_turku_routesd6b8f1d7f50e features outdoor_activities_in_turku_routesd6b8f1d7f50e EPSG:5048 CRS:84 21.955926819634765 22.505079774023198 60.33957231370219 60.75515453065459 geonode:paavo_sw_f_2024 This Dataset shows the current share (2024) of elderly people in each municipality of SW Finland. The distribution of the age groups that are 65 years old or even older are mapped in form of Polygones. The Polygones displays the percentage of the elderly people relative to the total elderly population across the study area. This dataset was produced under the visualization challange of the course MAAN 7771 Geospatial Data Management and Visualization at the University of Turku and aims to help urban planners to understand today’s spatial distribution of aging populations and existing health care access. Data derived from Statistics Finland datasets. features paavo_sw_f_2024 2024 EPSG:3067 CRS:84 20.87993995463531 23.987958444226898 59.668194204788115 61.17518800159555 other other other other text/xml other other geonode:paavo_sw_f_2045 This Dataset contains information about the change of the elderly population in the future (2045) in each municipality of SW Finland. The projected increase or decrease in the elderly population is mapped in form of Polygones. The Polygones displays the percentage of increase or decrease of elderly residents. This dataset was produced under the visualization challange of the course "MAAN 7771 Geospatial Data Management and Visualization" at the University of Turku and aims to highlight future hotspots of demographic pressure and areas where demand for care services is expected to grow most. Data derived from Statistics Finland datasets. 2045 features paavo_sw_f_2045 EPSG:3067 CRS:84 20.87993995463531 23.987958444226898 59.668194204788115 61.17518800159555 other other other other text/xml other other geonode:participatory_mapping_data_for_developing_the_anin6864a0f0ed8c features participatory_mapping_data_for_developing_the_anin6864a0f0ed8c EPSG:4326 CRS:84 22.23229339 22.27623039 60.44087038 60.45814738 geonode:pemba_buildings_2004_cd759cbb7e13bf70fb93f41f65a3a8f0 No abstract provided features pemba_buildings_2004_cd759cbb7e13bf70fb93f41f65a3a8f0 EPSG:21037 CRS:84 39.60687803020106 39.86815508358668 -5.469608043458699 -4.871923175098679 other other other other text/xml other other geonode:pemba_roads_2004 No abstract provided features pemba_roads_2004 EPSG:21037 CRS:84 39.60344401911917 39.87509540796138 -5.471367190974018 -4.867612716619439 other other other other text/xml other other geonode:permic_branch features permic_branch EPSG:3857 CRS:84 45.39241168732159 63.57303184131866 54.19309269847818 67.0473945525008 geonode:permic_languages_1900 features permic_languages_1900 EPSG:3857 CRS:84 45.39241168732159 63.57303184131866 54.19309269847818 67.0473945525008 geonode:pite_saami_language_1900 features pite_saami_language_1900 EPSG:3857 CRS:84 12.800791863000093 20.300260778699037 65.88725652638148 67.27153485825642 geonode:pulmanki_river_ecological_and_soil_survey_autumn_2016 The data are based on a field survey of sub-arctic Pulmanki river valley vegetation and soil in September 2016, field measurements of topography, bathymetry and flow and water level during 2009-2016 and hydraulic modelling. The data are discussed in detail in Nylén et al. 2019. The owner of this dataset is Tua Nylén (tua.nylen@utu.fi) and Fluvial and Coastal Research Group, University of Turku. Lichen Unit stream power Species richness Bryophyte Sub-arctic Species distribution Disturbance Riverine features Lapland Elevation model pulmanki_river_ecological_and_soil_survey_autumn_2016 Vascular plant River valley Litter EPSG:3067 CRS:84 28.027726171101026 28.04385109576548 69.9184261569446 69.9380978713006 other other other other text/xml other other geonode:pysakit This dataset contains information about the planned Turku tramway. This dataset includes the planned tramstops. This dataset was originally created by city of Turku and it is edited to be used in accessibility analysis relating to the planned tramway. features pysakit EPSG:3857 CRS:84 22.223366830712305 22.362778691204642 60.43627218108835 60.45377639438957 other other other other text/xml other other geonode:raitiotielinjaus This dataset contains information about the planned Turku tramway. This dataset includes the planned tramline. features raitiotielinjaus EPSG:3877 CRS:84 22.221820092099122 22.363989372759566 60.43628858311865 60.45454789258337 other other other other text/xml other other geonode:saami_branch The distribution of the Uralic languages grouped as Saami branch. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO(https://sites.utu.fi/urko/). 1900 traditional features uralic languages saami_branch saami branch EPSG:3857 CRS:84 9.544118686000115 41.404140472412145 61.76170501630038 71.16526927300016 other other other other other text/xml other geonode:saami_branch_all Distribution of Saami language branch at the beginning of the 20th century (updated). This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). 1900 saami_branch_all traditional features uralic languages saami branch EPSG:3857 CRS:84 9.544118686000115 41.404140472412145 61.76170501630047 71.16526927300016 other other other other text/xml other other geonode:saami_branch_points features saami_branch_points EPSG:3857 CRS:84 9.895818817532207 41.395361842255326 61.958459175742476 71.06592473392864 geonode:samoyedic_branch samoyedic_branch features EPSG:3857 CRS:84 43.27367282200007 113.42577367106715 52.79163754959073 75.69559561577407 geonode:samoyedic_dialect_labels features samoyedic_dialect_labels EPSG:3857 CRS:84 68.25755978648556 97.96773766972193 54.229930878599646 73.12203447689262 geonode:schools_total_serv_7f5e5b063ede73f97378c8471cdd03fc This dataset contains basic education level school points within the Turku metropolitan area, including the municipalities of Turku, Kaarina, Lieto, Naantali, Raisio, and Rusko. The basic education school data were originally uploaded from the Lounaistieto open data portal and clipped using the Digiroad network for accessibility analyses and therefore the schoolpoints now lay on their closest road and not on the original places.  features schools_total_serv_7f5e5b063ede73f97378c8471cdd03fc "Schools" After-school services Accessibility Digiroad EPSG:3067 CRS:84 21.64978063623207 22.777124253475073 60.35825704808867 60.603899368090865 other other other other text/xml other other geonode:seasonal_something_40ff2dfba647957332784c81fd542ddc features seasonal_something_40ff2dfba647957332784c81fd542ddc EPSG:5048 CRS:84 22.658874043043568 23.09222433176096 60.196757176579645 60.361780025505496 geonode:services_onroad This dataset includes after-school service points such as “Uimahallit” (swimming pools), “Uimapaikat” (swimming areas), “Jäähallit” (ice rinks), “Liikuntasalit ja palloiluhallit” (sports halls), “Kuntosalit” (gyms), “Urheilukentät” (sports fields), “Stadionit” (stadiums), “Nuorisotilat ja -keskukset” (youth facilities and centres), and “Kirjastot” (libraries). The service point data were downloaded from the Lounaistieto open data portal and clipped to the Turku metropolitan area, which includes the municipalities of Turku, Kaarina, Lieto, Naantali, Raisio, and Rusko. A 15-minute walking distance (1,250 m) buffer was calculated around each school, and the number of after-school services within these buffers is this dataset. This dataset represents the resulting spatial layer showing after-school services located within the 15-minute walking areas from schools, providing a basis for accessibility and service distribution analysis in the Turku region. "Public services" "Walkability" "After-school services" features "Turku metropolitan area" "opendata" services_onroad "Accessibility" "Service points" EPSG:3067 CRS:84 21.920382965485796 22.777190549639467 60.368256532469914 60.60575158772363 other text/xml other other other other other geonode:skolt_saami_language_1900 features skolt_saami_language_1900 EPSG:3857 CRS:84 27.66287469717857 33.133311394000096 67.41051368643092 70.0167503930001 geonode:skolt_saami_resettlement features skolt_saami_resettlement EPSG:3857 CRS:84 27.429188997469442 29.344526997515104 68.24188945762909 69.80341110184546 geonode:south_estonian_language_1900 features south_estonian_language_1900 EPSG:3857 CRS:84 25.10854675829393 28.807033980928693 56.33479857830822 58.492659230359386 geonode:south_khanty_language_1900 south_khanty_language_1900 features EPSG:3857 CRS:84 66.73956493052823 71.64626877163809 58.573439069433704 60.74599469182328 geonode:south_saami_language_1900 features south_saami_language_1900 EPSG:3857 CRS:84 9.544118686000115 18.674672002427574 61.76170501630038 66.13507721600011 geonode:stone_age_amber_artefacts_190ad0161c981bc9ce4a55730efe29a6 features stone_age_amber_artefacts_190ad0161c981bc9ce4a55730efe29a6 EPSG:2393 CRS:84 17.825707995529513 31.44349934896302 59.770008793487214 67.68376750776135 geonode:stone_age_amber_classified_cb23d89f2895d6cbbb5a0d146ee42711 features stone_age_amber_classified_cb23d89f2895d6cbbb5a0d146ee42711 EPSG:2393 CRS:84 17.825707995529513 31.44349934896302 59.770008793487214 67.68376750776135 geonode:stone_age_birchbark_artefacts_6194780d4f69a06df079d1e3a38e2e68 features stone_age_birchbark_artefacts_6194780d4f69a06df079d1e3a38e2e68 EPSG:2393 CRS:84 16.938513429022723 31.380913496247466 59.959421240788956 70.07259659393902 geonode:stone_age_bone_artefacts_19711993cd6dc79588a1e87efde785c3 features stone_age_bone_artefacts_19711993cd6dc79588a1e87efde785c3 EPSG:2393 CRS:84 16.95729800100627 36.83547610205213 59.76943390590233 69.70833510967536 geonode:stone_age_bone_classified_e4ef2d5d0796c03fb4a22bfa0dc680a4 features stone_age_bone_classified_e4ef2d5d0796c03fb4a22bfa0dc680a4 EPSG:2393 CRS:84 16.95729800100627 36.83547610205213 59.76943390590233 69.70833510967536 geonode:stone_age_clay_artefacts_14e7bfc6c9b1c43dc54cb1ed71365310 features stone_age_clay_artefacts_14e7bfc6c9b1c43dc54cb1ed71365310 EPSG:2393 CRS:84 18.61708847564506 31.59826829019493 59.91153694886981 65.36417093754864 geonode:stone_age_pottery_classified_48d2b18e903aa4c413691baf58bfb737 features stone_age_pottery_classified_48d2b18e903aa4c413691baf58bfb737 EPSG:2393 CRS:84 16.54101336486399 38.49488525722283 59.76999500688622 70.07238605216583 geonode:stone_age_pottery_event_date_4d535c3aaee191ce7bac8f3918f0d5d7 features stone_age_pottery_event_date_4d535c3aaee191ce7bac8f3918f0d5d7 EPSG:2393 CRS:84 19.226856122756892 32.65973134925858 59.899949186270256 70.06998307150405 geonode:stone_age_pottery_photo_link_examplec4bf5492658b stone_age_pottery_photo_link_examplec4bf5492658b features EPSG:2393 CRS:84 19.226856122756892 32.65973134925858 59.899949186270256 70.06998307150405 geonode:stone_age_stone_tools_artefacts2890dba69109 features stone_age_stone_tools_artefacts2890dba69109 EPSG:2393 CRS:84 -28.039581371140518 84.08208583093764 49.88915665233486 72.9316398653882 geonode:stone_age_stone_tools_classified9b01dc44e898 features stone_age_stone_tools_classified9b01dc44e898 EPSG:2393 CRS:84 -28.039581371140518 84.08208583093764 49.88915665233486 72.9316398653882 geonode:sva_ekologinenkestavyys The data describes the ecological sustainability of blue-green areas in urban regions within the Tampere City Region. The dataset is one of the result datasets from Essi Höysniemi’s Master’s thesis “Sustainability of blue-green areas in the Tampere City Region”. The thesis is written in Finnish. The data values range from 0 to 4, where higher values indicate better sustainability. Values 0–1 represent low sustainability, 1–2 represent moderate sustainability, 2–3 represent good sustainability, and values above 3 represent excellent sustainability. Pixel size is 2 meters. The data was produced using a multi-criteria analysis, combining two indicators measuring the ecological sustainability of blue-green areas: spatial connectivity of blue-green areas proportion of ecologically valuable blue-green areas blue-green Region sva_ekologinenkestavyys WCS GeoTIFF Sustainability Tampere City EPSG:3067 CRS:84 23.421476239020855 24.41552611068147 61.28718517429129 61.69825652873208 other other text/xml other other other other geonode:sva_kestavyysprofiilit The data represent the sustainability profiles of blue-green areas in the Tampere City Region. The dataset is one of the result datasets from Essi Höysniemi’s Master’s thesis “Siniviheralueiden kestävyys Tampereen kaupunkiseudulla” (“Sustainability of Blue-Green Areas in the Tampere Urban Region”). The thesis is written in Finnish. The data are classified into whole-number values ranging from 0 to 6, where each number corresponds to a specific sustainability profile. The profiles are as follows: 1 = Low profile – represents areas with the weakest overall sustainability levels. 2 = Economic profile – represents areas where the level of economic sustainability is higher compared to social and cultural sustainability; economic sustainability plays a key role. 3 = Cultural profile – represents areas where the level of cultural sustainability is higher than social and economic sustainability; cultural sustainability is emphasized. 4 = Social profile – represents areas with a high level of social sustainability but low economic and cultural sustainability; social sustainability is emphasized. 5 = Balanced profile – represents areas with good or moderate sustainability levels overall, but with a lower level of cultural sustainability. 6 = Most sustainable profile – represents the most sustainable blue-green areas. blue-green Region WCS GeoTIFF sva_kestavyysprofiilit Sustainability Tampere City EPSG:3067 CRS:84 23.255838144119444 24.530571009978797 61.21167052303272 61.77317322298059 other other other other text/xml other other geonode:sva_kulttuurinenkestavyys The data describes the cultural sustainability of blue-green areas in urban regions within the Tampere City Region. The dataset is one of the result datasets from Essi Höysniemi’s Master’s thesis “Sustainability of blue-green areas in the Tampere City Region”. The thesis is written in Finnish. The data values range from 0 to 4, where higher values indicate better sustainability. Values 0–1 represent low sustainability, 1–2 represent moderate sustainability, 2–3 represent good sustainability, and values above 3 represent excellent sustainability. Pixel size is 2 meters. The data was produced using a multi-criteria analysis, combining two indicators measuring the cultural sustainability of blue-green areas: valuable cultural environments and landscapes within blue-green areas cultural heritage sites located in blue-green areas. blue-green Region sva_kulttuurinenkestavyys WCS GeoTIFF Sustainability Tampere City EPSG:3067 CRS:84 23.255838144119444 24.530571009978797 61.21167052303272 61.77317322298059 other other other other text/xml other other geonode:sva_sosiaalinenkestavyys The data describes the social sustainability of blue-green areas in urban regions within the Tampere City Region. The dataset is one of the result datasets from Essi Höysniemi’s Master’s thesis “Sustainability of blue-green areas in the Tampere City Region”. The thesis is written in Finnish. The data values range from 0 to 4, where higher values indicate better sustainability. Values 0–1 represent low sustainability, 1–2 represent moderate sustainability, 2–3 represent good sustainability, and values above 3 represent excellent sustainability. Pixel size is 2 meters. The data was produced using a multi-criteria analysis, combining two indicators measuring the social sustainability of blue-green areas: accessibility of blue-green areas used for recreation, relative share of public or open blue-green areas used for recreation blue-green Region sva_sosiaalinenkestavyys WCS GeoTIFF Sustainability Tampere City EPSG:3067 CRS:84 23.37477479756998 24.439834766186816 61.26556950629229 61.705177650652324 other other other other text/xml other other geonode:sva_taloudellinenkestavyys The data describes the economic sustainability of blue-green areas in urban regions within the Tampere City Region. The dataset is one of the result datasets from Essi Höysniemi’s Master’s thesis “Sustainability of blue-green areas in the Tampere City Region”. The thesis is written in Finnish. The data values range from 0 to 4, where higher values indicate better sustainability. Values 0–1 represent low sustainability, 1–2 represent moderate sustainability, 2–3 represent good sustainability, and values above 3 represent excellent sustainability. Pixel size is 2 meters. The data was produced using a multi-criteria analysis, combining two indicators measuring the sustainability of blue-green areas: energy savings provided by blue-green areas carbon storage capacity of blue-green areas blue-green Region Area sva_taloudellinenkestavyys WCS GeoTIFF Sustainability Tampere City EPSG:3067 CRS:84 23.256433124521386 24.5302884120417 61.21166636313146 61.772935960615875 other other other other text/xml other other geonode:svakokonaiskestavyys The data describes the sustainability of blue-green areas in urban regions within the Tampere City Region. The dataset is one of the result datasets from Essi Höysniemi’s Master’s thesis “Sustainability of blue-green areas in the Tampere City Region”. The thesis is written in Finnish. The data values range from 0 to 4, where higher values indicate better sustainability. Values 0–1 represent low sustainability, 1–2 represent moderate sustainability, 2–3 represent good sustainability, and values above 3 represent excellent sustainability. Pixel size is 30 meter. The data was produced using a multi-criteria analysis, combining eight indicators measuring the sustainability of blue-green areas: spatial connectivity of blue-green areas proportion of ecologically valuable blue-green areas accessibility of blue-green areas used for recreation, relative share of public or open blue-green areas used for recreation energy savings provided by blue-green areas carbon storage capacity of blue-green areas valuable cultural environments and landscapes within blue-green areas cultural heritage sites located in blue-green areas. blue-green Region WCS GeoTIFF svakokonaiskestavyys "Finland" Sustainability Tampere City EPSG:3067 CRS:84 23.418471327405133 24.4182479922036 61.28576828075026 61.699831524903004 other other other other text/xml other other geonode:tandale_air_pollution_experiences_kernel_r150_p30_2023 This raster layer is a Kernel density surface of local community members' air pollution experiences in Tandale, Dar es Salaam. Local communities in Tandale and Kigogo shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University. The experiences were mapped as points and polygons. This Kernel density estimation surface has been produced using the point data. Parameters used: Radius 150m; pixel size 30m.  WCS tandale_air_pollution_experiences_kernel_r150_p30_2023 participatory mapping GeoTIFF EPSG:32736 CRS:84 39.23379845355676 39.25177440682422 -6.800421741738241 -6.787512535643154 other other other other text/xml other other geonode:tandale_climate_stressor_experiences_2023 This vector layer represents local community members' climate stressor experiences in Tandale, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University.  features participatory mapping tandale_climate_stressor_experiences_2023 EPSG:4326 CRS:84 39.235258 39.2507177343216 -6.799033733884452 -6.788993814154112 other other other text/xml other other other geonode:tandale_extreme_heat_experiences_kernel_r150_p30_2023 This raster layer is a Kernel density surface of local community members' extreme heat experiences in Tandale, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University. The experiences were mapped as points and polygons. This Kernel density estimation surface has been produced using the point data. Parameters used: Radius 150m; pixel size 30m.  tandale_extreme_heat_experiences_kernel_r150_p30_2023 WCS participatory mapping GeoTIFF EPSG:32736 CRS:84 39.23442479778203 39.25131775790383 -6.800330112829005 -6.787704630488402 other other other other text/xml other other geonode:tandale_flood_experiences_kernel_r150_p30_2023 This raster layer is a Kernel density surface of local community members' flood experiences in Tandale, Dar es Salaam. Local communities in Tandale and Kigogo wards shared their local knowledge on where they have most often encountered floods, extreme heat and air pollution via participatory mapping campaign conducted in October 2023. Their experiences were mapped with Maptionnaire-tool by Resilience Academy alumni students from Ardhi University. The experiences were mapped as points and polygons. This Kernel density estimation surface has been produced using the point data. Parameters used: Radius 150m; pixel size 30m.  tandale_flood_experiences_kernel_r150_p30_2023 WCS participatory mapping GeoTIFF EPSG:32736 CRS:84 39.234124237734655 39.25237706492166 -6.80095257816706 -6.787500343769151 other other other other other text/xml other geonode:ter_saami_language_1900 features ter_saami_language_1900 EPSG:3857 CRS:84 34.68595636370269 41.404140472412145 66.2202259877877 68.32154563592326 geonode:testdataset No abstract provided testdataset features EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 other other other other text/xml other other geonode:testdataset_kmzk No abstract provided features testdataset_kmzk EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 other other other other text/xml other other geonode:the_distribution_of_glacial_meltwater_routes_and_ac8999af5199c The dataset contains different types of subglacial meltwater routes and the related distribution of murtoos in the Finnish part of Fennoscandian Ice Sheet. features subglacial hydrology the_distribution_of_glacial_meltwater_routes_and_ac8999af5199c subglacial meltwater routes Fennoscandian ice sheet LiDAR Geomorphological mapping murtoos EPSG:5048 CRS:84 16.296479921922167 33.07558833194163 60.06990584583663 70.09402722183755 other other other other text/xml other other other other other other text/xml other other geonode:the_land_use__land_cover_data_from_iboya_tanzania_9c76f216c392 The Land Use & Land Cover (LULC) data from the Iboya region of Tanzania. There are six LULC-categories in this data (Scrubland, arable land, wetland, homestead, forest and grassland). This data was produced in a participatory mapping campaign and it was organized in Tanzania from February to March 2016. The owner of this dataset is Joni Koskinen (jonkosk@utu.fi), University of Turku. Tanzania landscape service participatory mapping landscape sustainability challenge SUSLAND the_land_use__land_cover_data_from_iboya_tanzania_9c76f216c392 features LULC EPSG:32736 CRS:84 34.99803817390607 35.08499659861456 -9.468788756448033 -9.386935481661292 other other other other text/xml other other geonode:the_land_use__land_cover_data_from_lulanzi_tanzanibd0d6a3b85cc The Land Use & Land Cover (LULC) data from the Lulanzi region of Tanzania. There are six LULC-categories in this data (Scrubland, arable land, wetland, homestead, forest and grassland). This data was produced in a participatory mapping campaign and it was organized in Tanzania from February to March 2016. The owner of this dataset is Joni Koskinen (jonkosk@utu.fi), University of Turku. landscape sustainability challenge participatory mapping the_land_use__land_cover_data_from_lulanzi_tanzanibd0d6a3b85cc features landscape service Tanzania SUSLAND LULC EPSG:32736 CRS:84 35.84709950901528 35.90654333946436 -8.012814811373405 -7.9432672681595475 other other other other other text/xml other geonode:the_land_use__land_cover_data_from_tungamalenga_taff6b71b1dd93 The Land Use & Land Cover (LULC) data from the Tungamalenga region of Tanzania. There are six LULC-categories in this data (Scrubland, arable land, wetland, homestead, forest and grassland). This data was produced in a participatory mapping campaign and it was organized in Tanzania from February to March 2016. The owner of this dataset is Joni Koskinen (jonkosk@utu.fi), University of Turku. Tanzania landscape service participatory mapping the_land_use__land_cover_data_from_tungamalenga_taff6b71b1dd93 SUSLAND landscape sustainability challenge features LULC EPSG:32736 CRS:84 35.06677870555522 35.12609863915747 -7.8774415506864734 -7.804831711384936 other other other other text/xml other other geonode:tomsk_region_selkup_language features tomsk_region_selkup_language EPSG:3857 CRS:84 81.23368713474508 88.11032334046328 58.58031671289283 59.04060781539865 geonode:tomsk_region_selkup_language_1900 features tomsk_region_selkup_language_1900 EPSG:3857 CRS:84 78.23500857787613 89.37974389373264 56.869263052874366 60.726048252862796 geonode:tundra_enets_language features tundra_enets_language EPSG:3857 CRS:84 83.30898060488452 94.70486650573221 69.08741601895522 71.8592725734891 geonode:tundra_enets_language_1900 features tundra_enets_language_1900 EPSG:3857 CRS:84 80.62842858200005 86.93102527244615 70.22600490138501 73.22784398683467 geonode:tundra_nenets_language features tundra_nenets_language EPSG:3857 CRS:84 48.21168053500028 87.0226097919698 64.33988754927574 72.97516510600025 geonode:tundra_nenets_language_1900 features tundra_nenets_language_1900 EPSG:3857 CRS:84 43.27367282199999 86.96414004255845 64.68778301457061 73.41043341123333 geonode:tundra_nenets_language_salminen features tundra_nenets_language_salminen EPSG:4326 CRS:84 49.21799033 87.02260979 64.28712784 72.97516511 geonode:tundra_nenets_language_salminen_1900 tundra_nenets_language_salminen_1900 features EPSG:4326 CRS:84 43.27367282 79.7930461 64.33654246 72.97516511 geonode:tungamalenga_servicepoints Landscape service (provisioning and cultural) point locations from Tungamalenga village located at the Southern highlands of Tanzania. This data was produced in a participatory mapping campaign and it was organized in Tanzania from February to March 2016. Tanzania tungamalenga_servicepoints Landscape services Tungamalenga_Servicepoints participatory mapping SUSLAND ecosystem services 1000 features EPSG:32736 CRS:84 35.069397455437006 35.12069653967331 -7.8715404527792865 -7.797496026061416 other other other other text/xml other other geonode:turclim_data__august_2006_and_2007 The data are based on a temperature measuring points in the Turku region. In addition to points, data contains information about environmental variables. Environmental variables can be used to explain the average temperatures of temperature points. Data were collected in August 2006 and 2007 The owner of this dataset is Juuso Suomi (juusuo@utu.fi), University of Turku. Land use features TURCLIM turclim_data__august_2006_and_2007 Mean temperature Urban heat island EPSG:3067 CRS:84 22.080096208125465 22.38606518663869 60.413036746737056 60.542642849346684 other other other other text/xml other other geonode:udmurt_language features udmurt_language EPSG:3857 CRS:84 49.987017499853266 58.00449674827845 54.19309269847818 58.59307656556224 geonode:ume_saami_language_1900 features ume_saami_language_1900 EPSG:3857 CRS:84 12.430918816000151 20.272603712014305 64.51280575705289 66.34378017730336 geonode:uralic_branches Distribution of Uralic branch at the beginning of the 20th century. The branches are visualised without overlapping extents. Map is based on the state-of-the-art distributions modified by the language experts. Branch labels of the Uralic languages. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). uralic_branches features 1900 traditional uralic languages EPSG:3857 CRS:84 9.544118686000115 113.42577367106715 45.48314228465055 75.69559561577407 other other other other other text/xml other geonode:uralic_languages Distribution of the Uralic languages at the beginning of the 20th century. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). uralic_languages 1900 traditional uralic languages features EPSG:3857 CRS:84 9.544118686000115 113.42577367106715 45.48314228465055 75.69559561577407 other other other other text/xml other other geonode:uralic_languages_branch_labels Branch labels of the Uralic languages. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). features 1900 traditional uralic_languages_branch_labels uralic languages EPSG:3857 CRS:84 21.079962366015 80.80885788434145 47.09170877961787 68.93673998942121 other other other other text/xml other other geonode:uralic_languages_labels Labels for the Uralic languages. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). uralic_languages_labels features uralic languages labels EPSG:3857 CRS:84 13.74875260113775 97.96773766968177 47.091708779743136 73.12203447687997 other other other other text/xml other other geonode:veps_language features veps_language EPSG:3857 CRS:84 34.047966075419566 36.44769944529537 59.744713205250925 61.48646637746745 geonode:veps_language_1900 Distribution of Veps language at the beginning of the 20th century. This dataset is produced by Timo Rantanen in the project BEDLAN (https://bedlan.net/) and published in the spatial data platform curated by URKO (https://sites.utu.fi/urko/). veps_language_1900 1900 traditional features veps language Finnic branch EPSG:3857 CRS:84 33.93090880956863 38.03927705039896 59.69833229767376 61.48646637746745 other other other other text/xml other other geonode:votic_language_1900 features votic_language_1900 EPSG:3857 CRS:84 28.25644238640934 29.104021693340126 59.486884482962985 59.723738709720074 geonode:votic_villages features votic_villages EPSG:3857 CRS:84 28.0289607570541 29.0530693618845 59.37385526042137 59.80210697341497 geonode:walking_path_slipping_risk_in_turku30462480cf82 This dataset contains information about slipping risk of the walking pathways in Turku and Kaarina. It is based on dataset "Turun ja Kaarinan ylläpitämät katualueet" (Road areas in Turku and Kaarina) https://www.avoindata.fi/data/fi/dataset/turun-ja-kaarinan-yllapitamat-katualueet. The original dataset has been modified in two ways. Firstly by adding nine attributes that describe slipping risk for pedestrians. Secondly, only walking pathways in the original dataset are included. This dataset is heavily based on other datasets in "Slippery datasets " group and is also part of the Bachelor's thesis of Iiro Seppä. Human health and safety Slipping Elderly person slipperiness Population distribution — demography walking_path_slipping_risk_in_turku30462480cf82 Transport networks features Pedestrians EPSG:3067 CRS:84 22.0790923717504 22.424409218852535 60.387314032727424 60.53679037983412 other other other other text/xml other other geonode:wards_boundaries_ebf14d329f6aae79a050fa4361ecac34 features wards_boundaries_ebf14d329f6aae79a050fa4361ecac34 EPSG:21037 CRS:84 38.891333709603174 39.1649356779137 -5.261868449802677 -4.961119657972812 geonode:west_and_south_mansi_language_1900 features west_and_south_mansi_language_1900 EPSG:3857 CRS:84 59.562640566552645 66.2474424157171 57.89199435796803 62.484606618130904 geonode:wintertime_sedimentation_in_the_coastal_area_of_the_northern_b Data describe sediment accumulation rate, organic and minerogenic matter, carbon and nitrogen content and their ratio (C/N) and mean sediment particle size (D50). Data have been collected during two perpendicular winter seasons in the Halikonlahti Bay, coastal area of the Northern Baltic Sea in 2018 - 2020. Data are based on sediment trapping and discussed more detailed in Salmela et al. 2021. Data ownership belongs to Fluvial and Coastal Research Group (https://sites.utu.fi/fcrg), Department of Geography and Geology, University of Turku. For more detailed information, please contact Jouni Salmela (jouni.salmela@utu.fi). Sedimentation Baltic Sea wintertime_sedimentation_in_the_coastal_area_of_the_northern_b features EPSG:5048 CRS:84 22.516764130977062 22.882550675865012 60.21010956977755 60.28655210894063 other other text/xml other other other other