My GeoServer WFSThis is a description of your Web Feature Server. The GeoServer is a full transactional Web Feature Server, you may wish to limit GeoServer to a Basic service level to prevent modificaiton of your geographic data.WFSWMSGEOSERVERWFS2.0.0NONENONEEurac ResearchAndrea VianelloBolzano39100Italyandrea.vianello@eurac.edu1.0.01.1.02.0.0text/xmlServiceIdentificationServiceProviderOperationsMetadataFeatureTypeListFilter_Capabilitiesapplication/gml+xml; version=3.2resultshitsapplication/gml+xml; version=3.2GML2KMLSHAPE-ZIPapplication/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvgml3gml32jsontext/csvtext/javascripttext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.1.1text/xml; subtype=gml/3.2nonelocalFALSE50000000nonelocalapplication/gml+xml; version=3.2urn:ogc:def:queryLanguage:OGC-WFS::WFSQueryExpressionALLSOMEresultshitsapplication/gml+xml; version=3.2GML2KMLSHAPE-ZIPapplication/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvgml3gml32jsontext/csvtext/javascripttext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.1.1text/xml; subtype=gml/3.2nonelocalapplication/gml+xml; version=3.2ALLSOMETRUETRUETRUETRUETRUETRUEFALSEFALSETRUETRUETRUETRUEFALSETRUEFALSEwfs:Querywfs:StoredQuerygeonode:4dmed_stationsHydrological station data collected in the 4DMED projects: https://www.4dmed-hydrology.org/ Data for these stations are available for project partners only, via API using the token.riverpiezometerfeatureswatersnow4dmed_stationshydrologyurn:ogc:def:crs:EPSG::4326-7.920587 31.12474635.05198851 46.687021geonode:indicator_adaptive_capacityAdaptive capacity indicator of the Vulnerability Map of Snow Tourism Destinations - BeyondSnow projectvulnerabilityindicator_adaptive_capacityAlpsEuracfeaturessnow tourism destinationsurn:ogc:def:crs:EPSG::30353.096737675840527 42.8437061768273817.507156809022316 50.563883087432174geonode:confine1Layer to represent the administrative border of Canton Ticino (CH).bordersurn:ogc:def:crs:EPSG::258328.380242819553075 45.8163378974242559.161036289908612 46.63249596296306geonode:alpinespace_eusalp_boundingboxBoundingbox layer for the alpine space EUSALP region.featuresalpinespace_eusalp_boundingboxurn:ogc:def:crs:EPSG::43263.68469595909119 42.991092681884817.1620101928711 50.5645637512207geonode:archivio_1Archivio Tirolese per la documentazione e l'arte fotografica di Lienz (TAP): - Collezione Lisl Gaggl-Meirer (Paesaggio, montagna; Tirolo Orientale; 1970-1990) - Collezione Klebelsberg, Istituto di Geologia, Università di Innsbruck (Paesaggio, montagna, militari; Dolomiti; 1907-1910) - Collezione Hans Peter Falkner (Città, Lienz; ca. 1965-1985) - Collezione Foto Baptist (Paesaggio, montagna; Tirolo Orientale; 1965-1975)fotoarchivioarchivefeaturespictureurn:ogc:def:crs:EPSG::432611.3055591583252 46.478080749511711.8031244277954 46.9187622070312geonode:auf_den_spuren_der_tracksAuf den spuren der tracks in Bletterback Parkparktrackfeaturesurn:ogc:def:crs:EPSG::432611.4030799865723 46.35974884033211.4245204925537 46.3696174621582EO_CDR:bdi_pop_adm2_isteebu_2019_polFrom the third general population and housing census of Burundi made by ISTEEBU Institute in 2008.bdi_pop_adm2_isteebu_2019_polfeaturesurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:bdi_pop_percommune_2021_isteebu_unfpa_pol_ppPopulation estimation by UNFPA with Institut de Statistiques et d'Etudes Economiques du Burundi (ISTEEBU). Burundi administrative level 0-2 2021 sex and age disaggregated projections from 2008 population census statisticsPopulationdistributionurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:bdi_adm_adm0_igebu_ocha_itos_2017_utm35sThe dataset represents the international boundaries of Burundi.bdi_adm_adm0_igebu_ocha_itos_2017_utm35sfeaturesurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:bdi_adm_adm1_igebu_ocha_2017_utm35sThe dataset represents the provinces of Burundi.featuresbdi_adm_adm1_igebu_ocha_2017_utm35surn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:bdi_adm_adm2_igebu_ocha_2017_utm35sThe dataset represents the communes of Burundi.bdi_adm_adm2_igebu_ocha_2017_utm35sfeaturesurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:bdi_adm3_reference_v01The dataset represents the collines of Burundi.collinesburundiurn:ogc:def:crs:EPSG::3273528.99034059460317 -4.48327481812088330.866003467679295 -2.2971208117248914EO_CDR:bdi_health_facilities_accessAccessibility to nearest health facility on drivable roads in Burundi.bdi_health_facilities_accessfeaturesurn:ogc:def:crs:EPSG::432628.9989585876465 -4.4598059654235830.8481159210205 -2.31337428092957EO_CDR:bdi_haz_landslides_pol_s1_eurac_pp_geounits_l2Lithological units of Burundi (second step of aggregation).bdi_haz_landslides_pol_s1_eurac_pp_geounits_l2geological unitsfeaturesurn:ogc:def:crs:EPSG::3273528.989911892900004 -4.480635926050398530.867526925912554 -2.2909966611646606EO_CDR:bdi_haz_landslides_pol_s1_eurac_pp_geounits_l3Lithological units of Burundi (third step of aggregation).geological unitsfeaturesbdi_haz_landslides_pol_s1_eurac_pp_geounits_l3urn:ogc:def:crs:EPSG::3273528.989911892900004 -4.480635926050398530.867526925912554 -2.2909966611646606EO_CDR:bdi_haz_landslides_pol_s1_eurac_pp_geounits_l4Lithological units of Burundi (fourth step of aggregation).geological unitsfeaturesbdi_haz_landslides_pol_s1_eurac_pp_geounits_l4urn:ogc:def:crs:EPSG::3273528.989911892900004 -4.480635926050398530.867526925912554 -2.2909966611646606EO_CDR:hotosm_bdi_airports_pointsAirports of Burundi (OSM).hotosm_bdi_airports_pointsfeaturesurn:ogc:def:crs:EPSG::432629.3160305023193 -4.0406599044799830.2529296875 -2.53808760643005EO_CDR:bdi_trans_roads_bridges_osm_ln_pBridges of Burundi (OSM). OSM Download from September 2020.bdi_trans_roads_bridges_osm_ln_pfeaturesurn:ogc:def:crs:EPSG::432629.0333423614502 -4.4484238624572830.5743503570557 -2.51653790473938EO_CDR:bdi_drive_roadsYear-round drivable roads of Burundi from OpenStreetMap (OSM).featuresbdi_drive_roadsurn:ogc:def:crs:EPSG::432629.0003051757812 -4.4481234550476130.8521842956543 -2.31427049636841EO_CDR:hotosm_bdi_education_facilities_pointsEducation facilities of Burundi (OSM).hotosm_bdi_education_facilities_pointsfeaturesurn:ogc:def:crs:EPSG::432628.9909915924072 -4.3474659919738830.4682559967041 -2.46590089797974EO_CDR:hotosm_bdi_health_facilities_pointsHealth facilities in Burundi (OSM).hotosm_bdi_health_facilities_pointsfeaturesurn:ogc:def:crs:EPSG::432629.0927639007568 -4.3551406860351630.5599479675293 -2.43888235092163EO_CDR:bdi_osm_discr_classOpenStreetMap intrinsic complete analysis by discrete classification of its collines using terrain ruggedness and gridded population estimates as auxiliary predictors.osmintrinsiccompletenessbdi_osm_discr_classfeaturesurn:ogc:def:crs:EPSG::3263528.999605878237837 -4.47243799640669330.85669176910313 -2.3079406850409434EO_CDR:bdi_all_roadsAll roads and footways of Burundi from OpenStreetMap (OSM).featuresbdi_all_roadsurn:ogc:def:crs:EPSG::432629.0021057128906 -4.4667696952819830.8435230255127 -2.32193064689636EO_CDR:hotosm_bdi_sea_ports_pointsSea ports of Burundi (OSM).hotosm_bdi_sea_ports_pointsfeaturesurn:ogc:def:crs:EPSG::432629.3438987731934 -3.3776702880859429.343900680542 -3.37767004966736EO_CDR:bdi_pop2020_worldpop_aggregated_collinesbcg2020100m population distribution of Burundi by Worldpop aggregated to colline level.bdi_pop2020_worldpop_aggregated_collinesbcg2020featuresurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:burundi_gridElectricity transmission network of Burundi (World Bank+REGIDISO). https://energydata.info/dataset/burundi-electricity-transmission-network-2007burundi_gridfeaturesurn:ogc:def:crs:EPSG::432629.0175933837891 -4.3529877662658730.5604152679443 -2.57117891311646EO_CDR:bdi_powerplantsPower plants in Burundi with total installed generating capacity 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). https://datacatalog.worldbank.org/dataset/burundi-power-plantsbdi_powerplantsfeaturesurn:ogc:def:crs:EPSG::432629.1174983978271 -3.9553000926971429.622501373291 -2.88469982147217EO_CDR:bdi_trans_roads_ln_dsnisv3_minesanteThis layer contains primary and secondary roads in Burundi catagorising the roads into three classes and providing information on surface and usability. The data originates from the Burundi Ministry of Health.bdi_trans_roads_ln_dsnisv3_minesantefeaturesurn:ogc:def:crs:EPSG::3273529.008266566672038 -4.45221449796089930.860276900313213 -2.327149312649401EO_CDR:bdi_haz_landslides_pol_s4_eurac_pp_prio_areasTBDbdi_haz_landslides_pol_s4_eurac_pp_prio_areasfeaturesurn:ogc:def:crs:EPSG::3273529.048020236705977 -3.99076209385862829.61073820677718 -2.583050102649156EO_CDR:bdi_topology_indicators_edgesThe layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality.bdi_topology_indicators_edgesfeaturesurn:ogc:def:crs:EPSG::432628.9989280700684 -4.4598016738891630.8541622161865 -2.31421232223511EO_CDR:bdi_topology_indicators_main_edgesThe layer contains the Betweenness Centrailty indicator computed on the edges of the OpenStreetMap (OSM) main roads (up to tertiary).bdi_topology_indicators_main_edgesfeaturesurn:ogc:def:crs:EPSG::432629.0330066680908 -4.457653045654330.8493995666504 -2.33004927635193EO_CDR:bdi_topology_indicators_edges_adm1The layer contains two topologic indicators computed on the nodes (roads intersections and dead-ends) of the OpenStreetMap (OSM) drivable roads dataset: i) Betweenness Centrality, and ii) Current Flow Betweenness Centrality. The data has been computed separately on each province of Burundi, then merged on the same file.bdi_topology_indicators_edges_adm1featuresurn:ogc:def:crs:EPSG::432629.0000648498535 -4.4362740516662630.8481121063232 -2.3134913444519EO_CDR:bdi_bldg_taxonomy_s3_idom_pp_communeTaxonomy of buildings in Burundi, per each commune (IDOM).burunditaxonomybuildingsurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113EO_CDR:bdi_bldg_taxonomy_s3_idom_pp_provinceTaxonomy of buildings in Burundi, per each province (IDOM).burunditaxonomybuildingsurn:ogc:def:crs:EPSG::3273528.990337223920097 -4.48327482735183730.8666880903388 -2.2971189544811113geonode:Biotop_BletterbachBiotope area of the Bletterbach geological ParkfeaturesBiotop_Bletterbachurn:ogc:def:crs:EPSG::2583211.355511718845097 46.3442156774447311.44656497771153 46.36794974878706EO_CDR:bdi_energy_dams_aquastat_ppDam locations in Burundi extracted from Aquastat Dam database for Africa. AQUASTAT gathers detailed information about dams in each country, especially on location, height, reservoir capacity, surface area and main purpose. http://www.fao.org/aquastat/en/databases/damsbdi_energy_dams_aquastat_ppfeaturesurn:ogc:def:crs:EPSG::432629.2189311981201 -3.92366385459930.7956008911133 -2.37557315826416EO_CDR:bdi_settl_extents_built_up_area_polBuilt-up areas (BUAs) A built-up area (BUA) is generally an area of urbanisation with moderately-todensely- spaced buildings and a visible grid of streets and blocks. Built up areas are characterized by contours with an area greater than or equal to 400,000 meters square that maintains a building density of thirteen or more across the entire area. The settlement extents and classification are derived solely from the building footprints, and no ancillary datasets are used. The centerpoints of building footprint features are converted to a 3 arc-second raster grid of building densities. Shell-up contours are then generated using the building density grid to delineate settled vs non-settled areas. The shell-up method includes contours that start at the lower bounds, but includes all grid cells with building densities to the upper bounds of the grid. For example, a shell up contour of 10 would include all grid cells with a building density of 10 or more. Contours with a building density of one or more are used to create the settlement extent polygons. The GRID3 Burundi settlement extents characterised building density into three (3) classes: built-up areas (bua_extents), small settlement areas (ssa_extents), and hamlets (hamlet_extents)built-up areasettlementsburundiurn:ogc:def:crs:EPSG::432629.0579662322998 -4.4393219947814930.8474140167236 -2.32789397239685EO_CDR:bdi_settl_extents_hamlets_polA hamlet is a collection of several compounds or sleeping houses in isolation from small settlements or urban areas. Hamlets are characterized as a collection of lowdensity settlements between one and 50 buildings and falls within 65 meters of one another.settlementsburundiurn:ogc:def:crs:EPSG::432628.9923496246338 -4.4744429588317930.8565845489502 -2.30216836929321EO_CDR:bdi_settl_extents_small_settlement_area_polA small settlement (SSA) is a settled area of permanently inhabited structures and compounds of roughly a few hundred to a few thousand inhabitants. The housing pattern in SSAs is an assemblage of family compounds adjoining other similar habitations. Small settlement areas are characterized by having 50 or more buildings and are not a BUA.bdi_settl_extents_small_settlement_area_polfeaturesurn:ogc:def:crs:EPSG::432629.0200309753418 -4.4683399200439530.8530731201172 -2.3096296787262EO_CDR:bdi_env_protectedareas_wdpa_polThis layer shows protected areas in Burundi according to the World Database of protected areas.burundinatureprotectedurn:ogc:def:crs:EPSG::432629.1872291564941 -4.3235459327697830.8539199829102 -2.31806802749634EO_CDR:hotosm_bdi_populated_places_pointsThis layer contains populated places extracted from OpenStreetMap 01 July 2021.settlementsburundiplaceurn:ogc:def:crs:EPSG::432628.9910621643066 -4.4875583648681630.8698329925537 -2.34265041351318EO_CDR:bdi_stle_places_nga_12jul2021_pGeographic names of populated places in Burundi by NGA Geonet Names Server (NGA). Last updated: 12. July 2021.settlementsburundiurn:ogc:def:crs:EPSG::432629.0131530761719 -4.4638314247131330.8449687957764 -2.31636881828308geonode:CDD_Nuts_RG_01M_2021_4326_level_0Cooling Degree Days at country level (NUTS level 0) is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. CDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded CDD is aggregated and subsequently presented on NUTS-0 level.cctNuts0CDDurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:CDD_Nuts_RG_01M_2021_4326_level_2Cooling Degree Day (CDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.cctfeaturescoolingNuts2urn:ogc:def:crs:EPSG::4326-63.1511917114258 -21.388851165771555.8357810974121 71.1841659545898geonode:CE_demo_casesThe layer shows the following information about Plus Energy Buildings (PEB) demo cases: 1) context, 2) key features, 3) owner, 4) Location, 5) Technologies integration, 6) Demo community and 7) partnership.CE_demo_casesfeaturesurn:ogc:def:crs:EPSG::326321.5068385444318086 44.5480996192976711.761424739771575 59.974288898987695geonode:climate_class_nut0Climate classification in european countries. The climates were extracted by the Koppen-Geiger classificationfeaturesclimatecctclimate_class_nut0urn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:DinAlpConnect_Project_areaThis data set represents the considered area between the Alps and Dinaric mountains to analyze the situation of ecological connectivity. Link to map: https://maps.eurac.edu/maps/1140/view File name: DinAlpConnect_Project_area.shp Project website: https://dinalpconnect.adrioninterreg.eu/DinAlpConnect_Project_areaDinaric Alpsfeaturesurn:ogc:def:crs:EPSG::303510.335858508973606 37.84255880025878626.637194357492536 47.18583888378881geonode:DinaricAlps_SACA1_Ecological_Conservation_AreThis data set shows important Ecological Conservation Areas (SACA1), bigger than 100ha, in the Dinaric Alps. These areas are expected to have a high biological value and that ecological connectivity is functioning well. Strategic Connectivity Areas (SACAs) derive from the Continuum Suitability Index. This approach is a way to display via GIS the most important sites for the overall ecological network on a macro-regional level (SACA1). Here, the most important ones were selected by expert evaluation. Filename: DinaricAlps_SACA1_Ecological_Conservation_Areas.shp Project website: https://dinalpconnect.adrioninterreg.eu/DinaricAlps_SACA1_Ecological_Conservation_Areasfeaturesurn:ogc:def:crs:EPSG::303510.398384077426597 38.0954455311613524.702475755900682 47.18604998536828geonode:DinaricAlps_SACA2_Regional_ecological_linkageEcological linkages are least cost paths, connecting the most important Ecological Conservation Areas (SACA1). They are part of the ecological intervention areas (SACA2). File name: DinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessment.shp Project website: https://dinalpconnect.adrioninterreg.eu/Dinaric AlpsDinaricAlps_SACA2_Regional_ecological_linkages_LCP_Assessmentfeaturesurn:ogc:def:crs:EPSG::303510.552505441795116 38.22468766926192524.360636685862627 47.132254747017775geonode:DinaricAlps_SACA3_Ecological_BarriersEcological Restoration Areas represent important barriers and have a low continuum suitability index (CSI). Ecological Restoration Areas (SACA3) are those ones, where ecological movements are not possible at the current stage and where it is necessary to implement restoration measures. These areas are currently the main barriers. For the calculation of these areas, all areas with a CSI of 1-4 were selected. File name: DinaricAlps_SACA3_Ecological_Barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/Dinaric AlpsDinaricAlps_SACA3_Ecological_Barriersfeaturesurn:ogc:def:crs:EPSG::303510.472431390595078 37.956595950296326.452004182057124 47.115243580561994geonode:DinaricAlps_SACA1_Ecological_Stepping_StonesStepping stones are representing areas with a high ecological value, important for ecological linkages. They are calculated by the Continuum Suitability Index (CSI) and the Strategic Connectivity Areas, considering small and less important Ecological Conservation Areas. File name: DinaricAlps_SACA1_Ecological_Stepping_Stones.shp Project website: https://dinalpconnect.adrioninterreg.eu/DinaricAlps_SACA1_Ecological_Stepping_StonesDinaric Alpsfeaturesurn:ogc:def:crs:EPSG::303510.388997878107789 37.8502551096170226.621376881816488 47.137552205525736geonode:DinaricAlps_SACA2_motorway_barriersThis layer is showing motorway barriers with potential ecological linkages in the Dinaric Alps. File Name: DinaricAlps_SACA2_motorway_barriers.shp Project website: https://dinalpconnect.adrioninterreg.eu/Dinaric AlpsDinaricAlps_SACA2_motorway_barriersfeaturesurn:ogc:def:crs:EPSG::303510.999996929878943 38.3504950532099124.260964629142034 46.8316999386691geonode:DinaricAlps_SACA2_Reg_ecological_corridorsThis layer shows the designed width of ecological corridors, that connect Ecological Conservation Areas. An approximate width of 2km was designed by truncating the normalized cost-weighted distances of the corridors at 40.000 km. File name: DinaricAlps_SACA2_Reg_ecological_corridors.shp Project website: https://dinalpconnect.adrioninterreg.eu/DinaricAlps_SACA2_Reg_ecological_corridorsDinaric Alpsfeaturesurn:ogc:def:crs:EPSG::303510.531176098953953 38.1535821971972724.43679038703587 47.150503421831125geonode:ecological_network_red_deer_south_tyrolDieser Datensatz zeigt ein ökologisches Netzwerkmodell für den Rothirsch in Südtirol. Es umfasst sowohl bestehende als auch potenzielle Querungsmöglichkeiten und soll eine Orientierung für die Definition von konkreten Korridoren darstellen. Auf lokaler Ebene kann das Modell von der Realität abweichen, weshalb die Nutzung durch Wildtiere vor Ort kontrolliert werden muss. Das Modell besitzt keine rechtliche Gültigkeit und ist noch keinem wissenschaftlichen Peer-Review-Prozess unterzogen worden.   Questo dataset mostra un modello di rete ecologica per il cervo in Alto Adige. Include sia i passaggi esistenti che quelli potenziali, e può essere usato come riferimento per definire corridoi concreti. A livello locale, il modello può differire dalla realtà, pertanto è necessario controllare l'utilizzo da parte della fauna selvatica in loco. Il modello non ha validità giuridica e non è stato ancora sottoposto ad alcun processo di revisione "Peer-review" scientifica.   File: Ecological_network_Red_deer_South_Tyrol.shp Bericht zur Erstellung des Modells (Englisch): https://www.datocms-assets.com/31538/1737642672-d2-3-1_project-of-ecological-network-south-tyrol.pdf  Bericht zu technischen Verbesserungsvorschlägen: https://www.datocms-assets.com/31538/1770718944-d-2-5-1_technischer-vorschlag_okologisches-netzwerk_hirsch_sudtirol.pdf  Proposta tecnica: https://www.datocms-assets.com/31538/1757008589-d-2-5-1_proposta-tecnica_rete-ecologica-per-il-cervo_alto-adige.pdf  Eurac project website: https://www.eurac.edu/en/institutes-centers/institute-for-regional-development/projects/plantoconnect Red DeercorridorfeaturesSouth-Tyrolecological_network_red_deer_south_tyrolEcological Connectivityurn:ogc:def:crs:EPSG::385710.186462982232102 46.0579049402589312.70138918219613 47.24890361234763geonode:electricity_priceElectricity prices components for household consumers -annual data (from 2007 onwards). Annual values taken from the Eurostat dataset with a national level resolution.HouseholdpriceElectrictyurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:energy_culturesThe information provided intends to support a profiling exercise of users’ domestic energy use and how these variations are translated into different domestic energy-intensity practices across EU territories. This layer includes descriptive information on the energy demand dynamics at household level by means of taking into account the cultural-climatic aspects which characterise the EU climatic areas as part of this research.energy_culutral_driversfeaturescctenergyhouseholdsurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:IEQThis layer contents specific Indoor Environmental Quality (IEQ) data sets provided which are rereferred to the closest possible locations of the Cultural-E demo cases. Specific locations and coordinates are included for each one in the IEQ related files that have been made available for downloading. Each geo-referred location contains IEQ related information and graphics, in the specific: 1. Reference year -.epw file-. 2. Climatic statistical data -.txt file-. Weather data plots, elaborated with Climate consultant and merged in a unique document -.pdf file-. 4. Weather data summary elaborated -.xls file-. Relevant information is provided in relation to: data source, used tool, implemented comfort tool, weather stations spec, software download links. This aims to illustrate the accuracy and "data fairness" of the data provided.householdsweatherIEQcctfeaturesurn:ogc:def:crs:EPSG::43262.39999985694885 44.529998779296911.3000001907349 59.9000015258789geonode:indicator_exposureExposure indicator for the Vulnerability Map of Snow Toursim Destinations - BeyondSnow projectvulnerabilityindicator_exposureAlpsEuracfeaturessnow tourism destinationsurn:ogc:def:crs:EPSG::30353.096737675840527 42.8437061768273817.507156809022316 50.563883087432174geonode:gas_priceLayer about Gas prices components for household consumers - annual data, derived by Eurostat datasets at country level.featuresgaspriceurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:GDPLayer about Gross Domestic Product prices components for household consumers - annual data, derived by Eurostat datasets at country level.GDPfeaturesurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:HDD_Nuts_RG_01M_2021_4326_level_0Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-0 level.HDDcctNuts0energyurn:ogc:def:crs:EPSG::4326-180.0 -90.0180.0 90.0geonode:HDD_Nuts_RG_01M_2021_4326_level_2Heating degree day (HDD) index is a weather-based technical index designed to describe the need for the heating energy requirements of buildings. HDD is derived from meteorological observations of air temperature, interpolated to regular grids at 25 km resolution for Europe. Calculated gridded HDD is aggregated and subsequently presented on NUTS-2 level.HDDcctNUTtempurn:ogc:def:crs:EPSG::4326-63.1511917114258 -21.388851165771555.8357810974121 71.1841659545898geonode:cooking_household_gwhThis Layer shows the share of fuels in the final energy consumption in the residential sector for coocking. The Frequency is annual.householdsenergycookingurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:space_cooling_gwhThe Layers shows the share of final energy consumption in the residential sector for space cooling. The frequency of data is annual.ccthousholdcoolingSpaceurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:space_heating_gwhThe Layers shows the share of fuels in the final energy consumption in the residential sector for space heating. The frequency of data is annual.HouseholdcctSpaceheatingurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:electricity_household_gwhLayer about Electricity Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.featureshouseholdsElectrictyurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:gas_household_gwhLayer about Gas Consumption in Households at nation level. The frequency of data is annual. The dataset is taken from Eurostat dataset.featuresgashousholdurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:in_der_bletterbachschl_trackIn Der Bletterback park tracktrackfeaturesurn:ogc:def:crs:EPSG::432611.3951244354248 46.361019134521511.4171085357666 46.382453918457geonode:lighting_appliances_percThis layer describes the energy consumption of lights and appliances on National Level for the households. The frequency of data is annual and it is connected with the Nuts0 Level.consumptionAppliancesLightingenergyurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:Locali_convenzionati_01092021_90pcmatched0This layer shows 90 % of the locations of bars/restaurants where the Eurac lunchcard can be used. As of: 01.09.2021.restaurantlunchurn:ogc:def:crs:EPSG::3263210.3 46.012.3676700592041 47.0502319335938geonode:MEC_VENT_BUILD_summerIn this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.IndoorEnvironmentalQualityThermalcomfortIEQThermalfeelingurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:MEC_VENT_BUILD_winterIn this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.IndoorEnvironmentalQualityThermalcomfortIEQThermalfeelingurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:timeseries_sosStations and timeseries of the MONALISA-SOS service. Environmental timeseries collected in the SouthTyrol province by the MONALISA project partners. Query the features and click the link to view last values collectedfeaturestimeseries_sosurn:ogc:def:crs:EPSG::432610.5798807144165 46.304702758789112.2728328704834 47.0376014709473geonode:metadataLayer with informations about meteorological stations for Trentino-Alto Adige region, Austria and Switzerland. The layer describe the Climate-Database of Eurac Research that contains meteorological time series of daily temperature (maximum, minimum and mean) and daily total precipitation for more than 250 station sites.metadatacctmeteoclimateurn:ogc:def:crs:EPSG::432610.0 45.739189147949212.6146259307861 47.2610015869141geonode:comuni_TN_BZLayer to display municipalities labels in Trento and Bolzano provincesfeaturesmunicipalitycomuni_TN_BZlabelurn:ogc:def:crs:EPSG::3263210.356887450269356 45.6385785990404412.50402823778642 47.12766182576525geonode:NATURALLY VENTILATED BUILDINGS in summerIn this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.IndoorEnvironmentalQualityThermalcomfortIEQThermalfeelingurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:NAT_VENT_BUILD_winterIn this layer, the user can find a prediction of the occupants' thermal feeling (TF) according to specific scenarios recommended in the Standard EN 16798-1, and values of Operative Temperature referring to the four Indoor Environmental Quality categories.IndoorEnvironmentalQualityThermalcomfortIEQThermalfeelingurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:OBM_v02This layer aims at creating a knowledge base related to research on Occupant Behaviour Modelling. The main Cultural-E contribution referrers to the translation into a GIS format of the provision of open-access available review tables focused on occupants' perception and behaviour in buildings (OPA)(*). In contrast to tables often found in supplementary materials, which are static, the tables used are dynamically growing with new evidence appearing in scientific literature. Authors of original research are welcome to add their published and peer-reviewed research items to these tables. (*) Schweiker M, Andersen RK, Berger C, Carlucci S, Chinazzo G, Edappilly LP, Favero M, Mahdavi A, Piselli C, Bourikas L, Hong T, Dong B, Syndicus M and Hahn J (2021) Dynamic review tables for topical reviews on occupants’ perception and behaviour in buildings. OSF. Available at: osf.io/gnvp2featuresbuildingenergyurn:ogc:def:crs:EPSG::4326-31.6483516693115 27.420751571655345.200813293457 71.4018936157227geonode:PEB_guidelinesThis layer provides exemplary national initiatives and guidelines materials for the design and experimentation for high standards of energy efficient buildings such as PEB, ZEB and NZEB. The different national initiatives referenced intends to represent the 4 EU climates which are object of study of CULTURAL-E.PEB_guidelinesfeaturescctenergyhouseholdsurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:simulation3Simulation for the START project of the path of 3 person in the Park site. In the future this simulation will be replaced by a near real time updated layer. You can see on the map different position at different time using the arrow of the timeline tool on the map.personsimulationfeaturespathurn:ogc:def:crs:EPSG::432611.3964538574219 46.35826873779311.4368095397949 46.381778717041geonode:alpineconvention_2f19551311fb14f3075b85124055c19dPerimeter of the Alpine Convention2023alpine conventionperimeteralpineconvention_2f19551311fb14f3075b85124055c19dfeaturesurn:ogc:def:crs:EPSG::326324.573826984976227 43.2600897671010316.60201848772918 48.591311460909935geonode:eusalp_alpspace_perimeter_001d862197332066b6fd6f7566b83908Perimeter of the Interreg Alpine Space Programme (2022)eusalp_alpspace_perimeter_001d862197332066b6fd6f7566b83908featuresalpine spaceurn:ogc:def:crs:EPSG::30353.092552726499899 42.843652424378317.507259104151906 50.56388852168553geonode:cntr_bn_01m_2020_3035_897b59ec32758c492f9e2d5d379c1677europefeaturescntr_bn_01m_2020_3035_897b59ec32758c492f9e2d5d379c1677urn:ogc:def:crs:EPSG::3035-180.0 -90.0180.0 90.0geonode:lcp_regional_linkages_and_distance_local_linkages0db696d087f4This layer shows the Least Cost Path (LCP), defining regional linkages and linkages less than 2.5 km.  “A Least-Cost-Path is defined as the pathway that offers the least resistance to an animal moving from one patch to another (Cushman et al., 2013) and is represented as the linear element (least-cost pathway) that connects two patches.” (Lumia et al., 2023) File name: LCP_Regional_Linkages_and_distance_local_linkages.shp Project website: https://www.alpine-space.eu/project/plantoconnect/AlpsfeaturesSpatial planningleast cost patheusalplcp_regional_linkages_and_distance_local_linkages0db696d087f4urn:ogc:def:crs:EPSG::30353.2229602301352998 42.9638165060880117.27155946627389 50.530868205896795geonode:plantoconnect_motorway_barriersThis layer is showing motorway barriers with potential ecological linkages in the Alps. File Name: PlanToConnect_Motorway_barriers.shp Project website: https://www.alpine-space.eu/project/plantoconnect/AlpsEcological ConnectivityUrban planningfeaturesplantoconnect_motorway_barriersMotorway barriersurn:ogc:def:crs:EPSG::30353.5668296746016512 43.1247524710532617.11866253941121 50.29899865357443geonode:plantoconnect_potential_ecological_network_eusalpThis data set shows regional potential ecological network in EUSALP areas. Filename: PlanToConnect_Potential_ecological_network_EUSALP.shp Project website: https://www.alpine-space.eu/project/plantoconnect/AlpsEcological Connectivityfeaturesplantoconnect_potential_ecological_network_eusalpSpatial planningeusalpurn:ogc:def:crs:EPSG::30353.0970600465027394 42.89481224252217.467303409930103 50.56381664296285geonode:population_densityThe ratio between the annual average population and the land area. The land area concept (excluding inland waters) should be used wherever available; if not available then the total area, including inland waters (area of lakes and rivers) is used. The frequency is annual.PopulationDensityurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:indicator_potential_impactsPotential impacts indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow projectvulnerabilityAlpsEuracfeaturesindicator_potential_impactssnow tourism destinationsurn:ogc:def:crs:EPSG::30353.096737675840527 42.8437061768273817.507156809022316 50.563883087432174geonode:rund_um_dolomiten_tracksRund um dolomiten tracks in the Bletterback parktrackfeaturesurn:ogc:def:crs:EPSG::432611.4057569503784 46.347019195556611.4552335739136 46.3721160888672EO_CDR:st_hzd_pred_landslides_pol_s4_p_polygonizedPolygonized time-series of landslide susceptibility (%) over South Tyrol.susceptibilityst_hzd_pred_landslides_pol_s4_p_polygonizedlandslidesurn:ogc:def:crs:EPSG::3263210.373172280345136 46.1900535213421512.504228171807831 47.1276189831466EO_CDR:st_tran_rds_ln_s4_osm_pp_drive_250tessDrivable roads from OpenStreetMap over South Tyrol (IT) onto an hexagonal tessellation of ~250m.st_tran_rds_ln_s4_osm_pp_drive_250tesstessellationsouth tyrolfeaturesdriveurn:ogc:def:crs:EPSG::2583210.415660344605858 46.2032872521162512.421923199647185 47.093709182372045geonode:indicator_sensitivitySensitivity indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow projectvulnerabilityAlpsEuracfeaturesindicator_sensitivitysnow tourism destinationsurn:ogc:def:crs:EPSG::30353.096737675840527 42.8437061768273817.507156809022316 50.563883087432174geonode:soltherm_household_gwhNo abstract providedfeaturessoltherm_household_gwhurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898EO_CDR:municipalities_polygonThis layer shows the municipality boundaries for Southtyrol, the Autonomous Province of Bolzano/Bozen.municipalityadministrativeboundariesurn:ogc:def:crs:EPSG::2583210.362601105547137 46.1828711238340112.51496584959338 47.13258268966183EO_CDR:sudagoost_tran_rds_ln_s4_osm_pp_driveDrivable roads from OpenStreetMap over the trans-national area composed of Alto Adige / Südtirol, Agordino (Belluno, Italy), and Osttirol (Lienz districit, Austria).driveosmsudagoost_tran_rds_ln_s4_osm_pp_drivefeaturesurn:ogc:def:crs:EPSG::432610.4131050109863 46.197963714599612.936466217041 47.1242942810059EO_CDR:transalp_tran_rds_ln_s4_osm_pp_drive_250tess_betwcentrBetweenness centrality topologic indicator calculated on the OSM drivable roads over the trans-national area of South Tyrol (IT), Agordino (Veneto, IT) and East Tyrol (AU). Roads have been projected onto a 250m regular hexagonal tessellation before analysis.featuresosmdrivebetweenness centralitytransalp_tran_rds_ln_s4_osm_pp_drive_250tess_betwcentrtessellationurn:ogc:def:crs:EPSG::2583210.41531799932096 46.1737614233543312.946662081065503 47.163772338652734EO_CDR:transalp_landuse_corine_pol_pp_2018The Copernicus "CORINE Land Cover" dataset of 2018, clipped over the TRANSALP project cross-border test area.transalp_landuse_corine_pol_pp_2018featuresurn:ogc:def:crs:EPSG::303510.380924871849189 46.1449551590322712.98498543784949 47.18582972020293EO_CDR:transalp_exposure_assets_pol_s3_250mAggregated data from exposed assets over the TRANSALP project cross-border study area onto an hexagonal tessellation of ~250m.exposuretransalp_exposure_assets_pol_s3_250mfeaturestessellationurn:ogc:def:crs:EPSG::2583210.370206836694845 46.13727631017419412.99450554094802 47.20420318312805geonode:weighted_vulnerabilityVulnerability indicator for the Vulnerability Map of Snow Tourism Destinations - BeyondSnow projectvulnerabilityAlpsEuracweighted_vulnerabilityfeaturessnow tourism destinationsurn:ogc:def:crs:EPSG::30353.096737675840527 42.8437061768273817.507156809022316 50.563883087432174geonode:water_heating_percThe Layer of share of final energy consumption in the residential sector for water heating. The frequency of data is annual.waterenergyheatingcctconsumptionurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:bletterbachshclucht_tracksBletterbachshclucht Parck tracksparktrackfeaturesurn:ogc:def:crs:EPSG::432611.4073238372803 46.347019195556611.4552335739136 46.3723526000977geonode:hydro_station_ado_32632Hydrological stations with discharge values for ADO projectdischargeriverhydro_station_ado_32632featureswaterccturn:ogc:def:crs:EPSG::326323.5870695659532816 43.6484541021450516.97423184722133 50.05916807444189geonode:lau_rg_01m_2021_3035_alpinespace405ca54aa9cefeatureslau_rg_01m_2021_3035_alpinespace405ca54aa9ceAlpsvulnerabilityEuracsnow tourism destinationsurn:ogc:def:crs:EPSG::30353.096737675840527 42.8437061768273817.507156809022316 50.563883087432174geonode:local_policiesThe information provided in this layer gives an overview of the legislation and requirements in each country and shows how they impact the spread of PEB concepts, with the help of a practical example. Different policies and related boundary conditions in each country have a great influence on the successful implementation of plus energy concepts.Therefore, national funding schemes and local policies are analysed in regard to support renewable energy generation in buildings and favour the connection with the electric grid and other district buildings (e.g. direct delivery of power to neighbour buildings, grid feed-in) as well as the local energy market (e.g. energy prices, feed-in tariff) and foreseen developments and environmental aspects.local_policiescctboundary_conditionsPEBurn:ogc:def:crs:EPSG::4326-31.2679100036621 27.638479232788144.8203735351562 71.1841659545898geonode:nuts2_simplifiedNUTS region, level 2 for the EUSALP area. The border are simplified respect to the original data source to get a lighter version.featuresnuts2_simplifiedurn:ogc:def:crs:EPSG::43263.69093990325928 43.028438568115217.1608009338379 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