In order to use the standard color legend for Romanian soil type maps in the ESRI ArcMap-10 electronic format, a dataset consisting a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files have been prepared (ESRI, 2016). The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend. This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background (ESRI, 2016). The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB , is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international soil classification system WRB-2014. The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colourcode_srts_wrb.lyr, and legend_colourcode_wrb.lyr. The first two of them are built using as value field the ‘Soil_codes’ field, and as labels (explanation texts) the ‘Soil_name’ field (storing the soil types according to SRTS/WRB classification), respectively, the ‘WRB’ field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the ‘colour_code’ field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification. In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_colour_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification. The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and colour_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

place IN ('isolated_dwelling','town','village','hamlet','city')

Features may have these attributes:

• source
• population
• place
• is_in
• name

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

The map shows the twenty-sixth highest Ozone (O3) value in Europe based on daily max 8-hour averages with at least 75% of valid measurements, in µg/m3 (source: EEA, AirBase v.8 & AQ e-Reporting)Thresholds used in the map for the twenty-sixth highest 8-hour max and other annual values [µg/m3]:≤ 80> 80 ≤ 100: (100 µg/m3, limit value for 8 hour mean, as set out in the WHO air quality guideline for O3)> 100 ≤ 120: (120 µg/m3, limit value and long term objective for human health, as set out in the Air Quality Directive, 2008/50/EC)> 120 ≤ 140> 140Source: AirBase v.8 & AQ e-ReportingAirBase is the European air quality database maintained by the EEA through its European topic centre on Air pollution and Climate Change mitigation. It contains air quality monitoring data and information submitted by participating countries throughout Europe.The air quality database consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants. It also contains meta-information on those monitoring networks involved, their stations and their measurements.The database covers geographically all EU Member States, the EEA member countries and some EEA collaborating countries. The EU Member States are bound under Decision 97/101/EC to engage in a reciprocal exchange of information (EoI) on ambient air quality. The EEA engages with its member and collaborating countries to collect the information foreseen by the EoI Decision because air pollution is a pan European issue and the EEA is the European body which produces assessments of air quality, covering the whole geographical area of Europe.

Romania administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

This COD-AB was most recently reviewed for accuracy and necessary changes in December 2024. The COD-AB does not require any update.

Sourced from ANCPI - Agenția Națională de Cadastru și publicitate imobiliară

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Romania COD-PS.

An edge-matched (COD-EM) version of this COD-AB is available on HDX here.

Please see the COD Portal.

Administrative level 1 contains 8 feature(s). The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 42 feature(s). The normal administrative level 2 feature type is ""currently not known"".

Recommended cartographic projection: Europe Albers Equal Area Conic

This metadata was last updated on January 13, 2025.

Harta a fost realizată în cadrul proiectului „Restaurarea zonelor umede și turbăriilor din Regiunea de Nord-Vest” (NWPEAT), derulat în intervalul 16 decembrie 2021 – 30 aprilie 2024. Promotor de proiect - Facultatea de Geografie din cadrul Universității Babeș-Bolyai; Partener 1 - Norwegian Institute for Nature Research.

Proiectul este finanțat printr-un grant acordat de Islanda, Liechtenstein și Norvegia, Programul RO-Mediu „Mediu, Adaptare la Schimbările Climatice și Ecosisteme”, Apelul de propuneri Restaurarea zonelor umede și turbăriilor, al cărui Operator de Program este Ministerul Mediului, Apelor și Pădurilor.

Romania administrative level 0-2 sex and age disaggregated 2022 population statistics

REFERENCE YEAR 2022

These tables are suitable for database or GIS linkage to the Romania - Subnational Administrative Boundaries layers.

This dataset contains shoreline positions derived from available Landsat satellite imagery (1984 to 2023) for the Romanian Black Sea coast. The shoreline positions were derived on transects 50 m apart using the open-source toolbox, CoastSat described in Vos and others, 2019a and 2019b. Landsat coastal imagery was classified, and shoreline positions were detected at the sub-pixel scale. Transects starting and end coordinates as well as all shoreline points along the transects for each date for which a Landsat image was processed are delivered in a comma delimited CSV table. Significant uncertainty can be associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. Since the Black Sea is a micro-tidal environment (less than 10 cm tidal range), the expected positional shoreline root means square error (RMSE) is less than 10% of Landsat pixel size of 30 m. These data can be used with any GIS software for shoreline evolution analysis, or other software to assist identifying and assessing possible areas of change and vulnerability, along with appropriate inclusion of uncertainty.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.4242 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.1112 and 0.1025 (in million kms), corressponding to 26.2097% and 24.1636% respectively of the total road length in the dataset region. 0.2105 million km or 49.6267% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0004 million km of information (corressponding to 0.1878% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Fisierul este realizat de catre Ministerul Mediului Apelor si Padurilor si pot fi descarcate de pe siteul www.mmediu.ro sectiunea Date GIS

The map shows the annual mean concentrations of Sulphur Dioxide (SO2) in Europe based on daily averages with at least 75% of valid measurements, in µg/m3 (source: EEA, AirBase v.8 & AQ e-Reporting)Thresholds used in the map for annual values [µg/m3]:≤ 5> 5 ≤ 10> 10 ≤ 20: (20 μg/m3, limit value for protection of vegetation, as set out in the Air Quality Directive, 2008/50/EC)> 20 ≤ 25> 25Source: AirBase v.8 & AQ e-ReportingAirBase is the European air quality database maintained by the EEA through its European topic centre on Air pollution and Climate Change mitigation. It contains air quality monitoring data and information submitted by participating countries throughout Europe.The air quality database consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants. It also contains meta-information on those monitoring networks involved, their stations and their measurements.The database covers geographically all EU Member States, the EEA member countries and some EEA candidate countries. The EU Member States are bound under Decision 97/101/EC to engage in a reciprocal exchange of information (EoI) on ambient air quality. The EEA engages with its member and collaborating countries to collect the information foreseen by the EoI Decision because air pollution is a pan European issue and the EEA is the European body which produces assessments of air quality, covering the whole geographical area of Europe.

You can find here a list of hospitals in Bucharest city and Ilfov county (Romania), according to free data, joined and post-processed. Although we tried to make it as precise as we can, given the limitations of public datasets we don't guarantee that all attributes are accurrate so we don't decline any responsability in using this dataset as an authoritive data source, especially since subjective classifications of importance or seismic vulnerability are included.

This dataset has been compiled and used in various studies: - Toma-Danila D., Tiganescu A., D’Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science 10:834052, doi: 10.3389/feart.2022.834052 (please use this as main citation, given that is significantly different than previous versions) - Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5):1421-1439, doi: 10.5194/nhess-20-1421-2020 - Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111

If you are also interested in using the dataset as a feature service, it's on ArcGIS online, at https://services8.arcgis.com/SXiEEy1skwB5SrYh/arcgis/rest/services/Bucharest_Ilfov_hospitals/FeatureServer

In order to use the Romanian color standard for soil type map legends, a dataset of ESRI ArcMap-10 files, consisting of a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files (https://desktop.arcgis.com/en/arcmap/10.3/map/ : saving-layers-and-layer-packages, about-creating-new-symbols, what-are-symbols-and-styles-), have been prepared. The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend.

This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background. The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB, is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international system WRB-2014.

The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colorcode_srts_wrb.lyr, and legend_colorcode_wrb.lyr. The first two of them are built using as value field the “Soil_codes” field, and as labels (explanation texts) the “Soil_name” field (storing the soil types according to SRTS/WRB classification), respectively, the “WRB” field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the “color_code” field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification.

In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_color_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification.

The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and color_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

The presented file set may be used to directly implement the Romanian color standard in digital soil type map legends, or may be adjusted/modified to other specific requirements.

The map shows annual mean concentrations of Particulate Matter (PM2.5) in Europe based on daily averages with at least 75% of valid measurements, in µg/m3 (source: EEA, AirBase v.8 & AQ e-Reporting)Thresholds used in the maps for annual values [µg/m3]:≤ 10: (10 μg/m3, as set out in the WHO air quality guideline for PM2.5)> 10 ≤ 20: (20 μg/m3, limit value as set out in the Air Quality Directive, 2008/50/EC)> 20 ≤ 25: (25 μg/m3, target value as set out in the Air Quality Directive, 2008/50/EC)> 25 ≤ 30> 30Source: AirBase v.8 & AQ e-ReportingAirBase is the European air quality database maintained by the EEA through its European topic centre on Air pollution and Climate Change mitigation. It contains air quality monitoring data and information submitted by participating countries throughout Europe.The air quality database consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants. It also contains meta-information on those monitoring networks involved, their stations and their measurements.The database covers geographically all EU Member States, the EEA member countries and some EEA collaborating countries. The EU Member States are bound under Decision 97/101/EC to engage in a reciprocal exchange of information (EoI) on ambient air quality. The EEA engages with its member and collaborating countries to collect the information foreseen by the EoI Decision because air pollution is a pan European issue and the EEA is the European body which produces assessments of air quality, covering the whole geographical area of Europe.

You can find here GIS and spreadsheet data of firestations in Bucharest city and Ilfov county (Romania), according to official sources, geocoded and manually verified. Although we tried to make it as precise as we can, we don't guarantee that all attributes are accurrate so we don't decline any responsability in using this dataset as an authoritive data source.

This dataset has been compiled and used in various studies: - Toma-Danila D., Tiganescu A., D’Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science 10:834052, doi: 10.3389/feart.2022.834052 (please use this as main citation, given that is significantly different than previous versions) - Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5):1421-1439, doi: 10.5194/nhess-20-1421-2020 - Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111

If you are also interested in using the dataset as a feature service, it's on ArcGIS online, at https://services8.arcgis.com/SXiEEy1skwB5SrYh/arcgis/rest/services/Bucharest_Ilfov_firestations/FeatureServer

Context

The dataset analyses the impact of the COVID-19 pandemic in Romania.

Content

The dataset contains 4 columns: * date - the date of each record, starting from 26 February 2020 * cases - the cumulative number of cases reported each day, in the first days of the pandemic there were multiple press releases about the number of cases, but the sum per day is already aggregated * recovered - the cumulative number of recovered cases * deaths - the cumulative number of deaths * tests - number of tests performed by the date, for the dates with no information, the difference split equally in that interval

Acknowledgements

This data was collected from: * https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Romania * https://www.digi24.ro/stiri/actualitate/informatii-oficiale-despre-coronavirus-in-romania-1266261 * https://stirioficiale.ro/informatii

Other great data souces: * http://www.ms.ro/comunicate/ * http://www.cnscbt.ro/ * https://instnsp.maps.arcgis.com/apps/opsdashboard/index.html#/5eced796595b4ee585bcdba03e30c127

Thank you for the photo: * https://playtech.ro/stiri/o-minciuna-despre-coronavirus-il-va-costa-ani-grei-de-inchisoare-ce-a-facut-un-barbat-din-campia-turzii-95782

Inspiration

Thanks, https://www.kaggle.com/bjoernjostein/corona-virus-in-norway!

Unitatile teritorial administrative de rang 3 - NUTS3Contine informatii referitoare la Codurile SIRUTA, si Codurile NUTS3Scara de referinta 1:100.000Codurile SIRUTA au fos adaugate de pe siteul Ministerului Dezvoltarii Regionale, Administratiei Publice si Fondurilor Europene

Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.

INFP, CRMD and UCL have developed a framework capable of analyzing the implications of natural hazards on transportation networks, also in a time-dependent manner. This is currently embedded into an ArcGIS toolbox entitled Network-risk, which has been successfully tested for Bucharest, contributing to an insightful evaluation of emergency intervention times for ambulances and firefighters, in the case of an earthquake. The files and the user manual allow a replication of our recent analysis in Toma-Danila et al. (2022) and a download of results (such as affected roads and unaccesible areas in Bucharest), in various formats. Some of the results are also presented in an ArcGIS Online app, called "Riscul seismic al Bucurestiului" (The seismic risk of Bucharest), available at https://tinyurl.com/yt32aeyx. In the files you can find: - the Bucharest road network used in the article; - facilities for Bucharest and Ilfov, such as hospitals, firestations, buildings with seismic risk or tramway lines accesible by emergency vehicles - results of the analysis: unaccesible roads and areas, service areas around facilities, closest facilities for representative points - Excel calculator for Z elevation from OpenStreetMap data - the user manual and a ArcGIS toolbox.

Main citation: - Toma-Danila D., Tiganescu A., D'Ayala D., Armas I., Sun L. (2022) Time-Dependent Framework for Analyzing Emergency Intervention Travel Times and Risk Implications due to Earthquakes. Bucharest Case Study. Frontiers in Earth Science, https://doi.org/10.3389/feart.2022.834052

Previous references: - Toma-Danila D., Armas I., Tiganescu A. (2020) Network-risk: an open GIS toolbox for estimating the implications of transportation network damage due to natural hazards, tested for Bucharest, Romania. Natural Hazards and Earth System Sciences, 20(5): 1421-1439, https://doi.org/10.5194/nhess-20-1421-2020 - Toma-Danila D. (2018) A GIS framework for evaluating the implications of urban road network failure due to earthquakes: Bucharest (Romania) case study. Natural Hazards, 93, 97-111, https://link.springer.com/article/10.1007/s11069-017-3069-y

Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.

Through time, both natural and cultural heritage have unfortunately been under threat due to multiple environmental and human-induced factors, which are likely to trigger various hazards such as soil erosion, landslides, or land collapse. The analysis of old cartographic material, aerial imagery, and satellite imagery has been used in multiple studies to observe and understand the changes that archaeological sites have undergone over the last centuries. These efforts are intended, among other things, to raise awareness of the threats affecting cultural heritage and prevent damages and preserve tangible evidence of the distant past. In this study, historical maps and satellite imagery were analyzed to observe how the landscape in the Mostiștea Valley (Romania) has been used over the last 230 years and how the land use has affected the cultural heritage. Land cover and land use (LCLU) changes in the Mostiștea Valley have occurred due to numerous natural and anthropic forces. These changes have resulted in the damage of tangible heritage in the area with varying degrees of intensity. The results of this study allow the quantification of the magnitude of these changes and their impact on different sites in the region.