http://www.openstreetmap.org/images/osm_logo.png" alt=""/> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3 in both standard formats (OSM PBF, XML) and cloud-native formats optimized for analytics workloads.

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for French Polynesia in a GIS-friendly format.

The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly.

The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

OpenStreetMap data is open data, with a very permissive licence. You can download it and use it for any purpose you like, as long as you credit OpenStreetMap and its contributors. You don't have to pay anyone, or ask anyone's permission. When you download and use the data, you're granted permission to do that under the Open Database Licence (ODbL). The only conditions are that you Attribute, Share-Alike, and Keep open.

The required credit is “© OpenStreetMap contributors”. If you make a map, you should display this credit somewhere. If you provide the data to someone else, you should make sure the license accompanies the data

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for 21 Pacific Island Countries, in a GIS-friendly format. The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly and contains data for Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Republic of the Marshall Islands, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Guam, Northern Mariana Islands, French Polynesia, Wallis and Futuna, Tokelau, American Samoa as well as data on the Pacific region. The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. OSM is considered a prominent example of volunteered geographic information. Data are collected using manual survey, GPS devices, aerial photography, and other free sources. This crowdsourced data are then made available under the Open Database License.

Daily extract of Copper Subdistributors (SR) in Metropolitan France present in the OpenStreetMap (OSM) open and collaborative database [1]. The layer is available in MAGOSM — accessible via public WMS and WFS services — viewable, searchable and downloadable via the carto portal — analysable over the last 30 days via the Change Tracking Portal Data model The OSM attributes used to filter the data are: * Telecom =connection_point * telecom:medium=copper We find in the layer all the objects concerned whether they are mapped in the form of a node, path or relationship in OSM. For polygons and multi-polygons, the geometry provided corresponds to the centroid, the original geometry is available in EWKT format via the attribute ‘osm_original_geom’ and the original type in a column ‘osm_type’. Additional OSM attributes have been selected to enrich the main tags. All attributes prefixed by “osm” (e.g. osm_user, osm_id...) are common properties similar to meta-data on the OSM object. More information about: * the data model specific to ‘connection points’ on the Wiki page OSM — FR:Tag:telecom=connection_point [2] * the general OSM data model is documented on the OSM Wiki page — Map Elements [3]. * the modes and frequencies of use and combination of different attributes within the OSM community on the TagInfo France service [4] [1] https://wiki.openstreetmap.org/wiki/FR:Page_principale [2] https://wiki.openstreetmap.org/wiki/FR:Tag:telecom=connection_point [3] https://wiki.openstreetmap.org/wiki/FR:%C3%89l%C3%A9ments_cartographiques [4] https://taginfo.openstreetmap.org/tags/?key=telecom&value=connection_point#overview Credits © OpenStreetMap contributors http://www.openstreetmap.org/copyright This data is produced collaboratively under ODbL license which requires identical sharing and attribution mention “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

This dataset comes from the Open National Address Base project initiated by OpenStreetMap France.

For more information on this project: http://openstreetmap.fr/blogs/cquest/bano-banco

Origin of data

• OpenStreetMap
• data available in opendata
• address data collected on the land registry site (source DGFiP 2014)

Dissemination format

These files are offered in shapefile format, in WGS84 projection (EPSG :4326) as well as in CSV format and experimentally in the form of github project.

Description of content

For each address:

• id (unique): code_insee + codefantoir + number
• number: number in the lane with suffix (ex: 1, 1BIS, 1D)
• lane: lane name
• post_code: 5-character postal code
• city: name of the municipality
• source: OSM = data directly from OpenStreetMap, OD = data from local opendata sources, CAD = data directly from the cadastre, C+O = cadastre data in richie by OSM (lane name for example)
• lat: latitude in decimal degrees WGS84
• lon: longitude in decimal degrees WGS84

Update updated, corrections

To update and correct BANO data, simply make improvements directly in OpenStreetMap, they will be taken into account in the next update cycle.

A collaborative single window for reporting/correction will soon be set up to simplify the process of improving the content of the database. To participate in its co-construction, do not hesitate to contact us!

For any questions regarding the project or this dataset, you can contact bano@openstreetmap.fr

Exports of French administrative division at regional level (regions) from OpenStreetMap produced in the vast majority from the cadastre.

This data comes from crowdsourcing carried out by the contributors to the OpenStreetMap project and is under ODbL license which requires identical sharing and the mandatory attribution mention must be “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright

This is a semi-automatic export with lighter and topologically verified geometries (no overlap). From 2016, the geometries are original, unsimplified.

Descriptive of the contents of “regions” files

Origin

The data comes from the OpenStreetMap cartographic database. These were established from the cadastre made available by DGFiP on cadastre.gouv.fr. In addition on Mayotte where the cadastre is not available on cadastre.gouv.fr, the route of the coasts was made from the aerial images of Bing.

More info: http://prev.openstreetmap.fr/36680-communes

Format

These files are available in shapefile format, in WGS84 projection with several levels of detail (until 2015): — simplification at 5 m — simplification at 50 m — simplification at 100 m

The topology is retained during the simplification process (see: http://prev.openstreetmap.fr/blogs/cquest/limites-administratives-simplifiees)

Content

These files contain all the French regions, including the DOM and Mayotte.

For each region, the following attributes are added:

— code_insee: 2-digit INSEE code of the region (e.g. 42) — name: name of the region (e.g. Alsace) — Wikipedia: wikipedia entry (language code followed by the name of the article, e.g. en:Alsace) — Wikidata: wikidata identifier of the region — surf_km2: area area in km² on the WGS84 spheroid

History

— 01-01-2017: version based on the municipal division OSM as of 01-01-2017 including the merger of 566 communes into 178 new municipalities. — 01-01-2018: version based on OSM communal cutting at 01-01-2018 (marginal change in geometry)

Predecent versions available on: http://osm13.openstreetmap.fr/~cquest/openfla/export/

If you have any questions about these exports, you can contact exports@openstreetmap.fr

See also:

— cards in SVG format — contours of the French municipalities — contours of EPCI 2014 and Contours des EPCI 2013 — contours of the French arrondissements — contours of the French departments and SVG maps of the departments

OpenStreetMap is a project founded in 2004 with the aim of creating a free world map. Volunteers from many countries work on the further development of the software as well as the collection and processing of geodata. Data is collected about roads, railways, rivers, forests, houses and everything else that is commonly seen on maps. The OpenStreetMap data may be used free of charge and processed as long as the source is mentioned (see also: https://www.openstreetmap.org/copyright).

This data set contains an excerpt from the OpenStreetMaps “Planet-File”, which contains the relevant data for the administrative district of Münster. Other formats such as OSM-XML, shapefiles, SVG, Adobe Illustrator, Garmin GPS, GPX, GML, KML, Manifold GIS or raster graphics can be exported at http://wiki.openstreetmap.org/wiki/Export.

For questions about OpenStreetMap data, there is a German-speaking user forum: http://forum.openstreetmap.org/viewforum.php?id=14

This is a worship data from OSM of Pakistan. It is in SQL format for postgresql with postgis extension enabled.

This dataset comes from the Open National Address Base project initiated by OpenStreetMap France.

For more information on this project: http://openstreetmap.fr/blogs/cquest/bano-banco

Origin of data

• OpenStreetMap
• data available in opendata
• address data collected on the land registry site (source DGFiP 2014)

Dissemination format

These files are offered in shapefile format, in WGS84 projection (EPSG :4326) as well as in CSV format and experimentally in the form of github project.

Description of content

For each address:

• id (unique): code_insee + codefantoir + number
• number: number in the lane with suffix (ex: 1, 1BIS, 1D)
• lane: lane name
• post_code: 5-character postal code
• city: name of the municipality
• source: OSM = data directly from OpenStreetMap, OD = data from local opendata sources, CAD = data directly from the land register, C+O = data from the land register in Richie by OSM (lane name for example)
• lat: latitude in decimal degrees WGS84
• lon: longitude in decimal degrees WGS84

Update updated, corrections

To update and correct BANO data, simply make improvements directly in OpenStreetMap, they will be taken into account in the next update cycle.

A collaborative single window for reporting/correction will soon be set up to simplify the process of improving the content of the database. To participate in its co-construction, do not hesitate to contact us!

For any questions regarding the project or this dataset, you can contact bano@openstreetmap.fr

Context

Recently I came to know about OpenStreetMap from a friend of mine which inspired me to do this. OpenStreetMap, inshort OSM data is a completely crowd-sourced collection of data containing keys such as buildings, amenity, leisure, tourism etc. The data set is usually used for maps. Learn more about it.

Content

The raw data were obtained from Geofabrik which had all details about entities present in India. The CSV files are generated by a script that takes data from OSM(OpenStreetMap) file in a ProtocolBuffer Binary format(pbf).

Datasets

• building.csv - Details about buildings such as node id(since the OSM file uniquely identifies each entry by a node id), building name, type, location(longitude - latitude), other tags. Learn about the standard tags for buildings in osm file - key: building
• amenity.csv - Amenity type, name, location, other tags. Learn about the standard tags for amenity - key: amenity.
• leisure.csv - Leisure type, name, location, other tags. Learn about the standard tags for leisure- key: leisure.

Acknowledgements

Raw data source - Geofabrik

An Improved Global River vector Dataset based on Multi-Source River Data FusionContact: Yesen Liu (liuys@iwhr.com) and Yaohuan Huang (Huang(huangyh@igsnrr.ac.cn) for questionsDownload Instructions:- The data format is a shapefile file, compressed into zip format, and divided by continent (due to the limitation of shapefile files, Russia will be saved separately as a file)- GSriver-AF-shp.zip(911M): Rivers in Africa, including 166335 rivers- GSriver-ASandEU-shp.zip(1.35G): Rivers in the Asia Europe region (excluding Russia), including 209528 rivers- GSriver-NA-shp.zip(921M): Rivers in North America, including 134242 rivers- GSriver-OA-shp.zip(246M): Rivers in Oceania, including 45738 rivers- GSriver-RUS-shp.zip(735M): Rivers in Russia, including 95122 rivers- GSriver-SA-shp.zip(666M): Rivers in South America, including 97664 riversUnderlying sources:- OSM waterways: https://www.openstreetmap.org- HydroRIVERS: https://www.hydrosheds.org/products/hydrorivers- Global River Topology(GRIT): https://zenodo.org/records/11219313/filesCoordinate system-WGS84(World Geodetic System 1984)Spatial and Temporal Coverage:The dataset covers global river networks (divided by continents) and specified the temporal baseline of the input sources (HydroRIVERS v10, OSM Waterways 2022, and GRIT).Reference:- Liu Y., Wang J.H, Liu C.J, etc. Allen (in review): An Improved Global River vector Dataset based on Multi-Source River Data FusionVariables and Units(Name ,Type ,Description):- OBJECT ,integer ,ID Field in MDB Vector Layer Files- shape ,Binary ,Stores geometric information of river lines- HydroID ,LONG ,Upstream HydroRIVERS reach ID(from HYDRO_ID field), serving as the unique identifier for the river- Nextdown ,LONG ,Code of the receiving river(0 for level-1 rivers indicating sea/lake termination or inland rivers)- MAINRIVID ,LONG ,Watershed ID(from MAINRIV_ID field in HydroRIVERS)- Hydrocount ,LONG ,Number of original HydroRIVERS reaches merged into this river- Rclass ,INTEGER ,River classification level- RclassDISP ,INTEGER ,Display classification level(using 11 major rivers as level-1 reference)- Rlenth ,DOUBLE ,River length(km)- Hydrolenth ,DOUBLE ,Original HydroRIVERS reach length before fusion(km)- Rcatch ,DOUBLE ,Drainage area of this river(excluding tributary areas)(km²)- TCatch ,DOUBLE ,Total drainage area of all tributaries(km²)- OSMratio ,DOUBLE ,Percentage of vertices from OSM waterways(2 decimal places)- GRITratio ,DOUBLE ,Percentage of vertices from GRIT(2 decimal places)- Hydroratio ,DOUBLE ,Percentage of vertices from HydroRIVERS(2 decimal places)- Rname ,STRING ,River name(Some names have been modified based on other datasets)- NameList ,STRING ,All associated OSM waterway names- OrignCNT ,STRING ,Country of river originMethods of creation:We devised a multi-resolution vector data fusion framework by integrating high-precision coordinate information into existing river networks to produce SkyRivers. In this study, SkyRivers is generated by fusing HydroRIVERS with OSM waterways, while utilizing GRIT as a supplementary data source in regions where OSM waterways coverage is incomplete. To these three vector datasets, there are two fundamental technical challenges for data fusion:(1) establishing accurate correspondences between HydroRIVERS and their counterparts in OSM waterways or GRIT, and(2) effectively integrating the high-resolution coordinates from OSM waterways or GRIT into the HydroRIVERS river network topology. To overcome these challenges, our approach implemented several key solutions including HydroRIVERS reaches integration, OSM Waterways identification, multi-data fusion, GRIT supplement, and topology repair.Potential applications:- Flood forecasting- Development of Water Information Platform- Water resources mapping- Overlay analysis with high-resolution land use dataUncertainty Estimates:The accuracy of OSM data varies widely depending on contributor activity, which can introduce inconsistencies in sparsely mapped areas. For instance, the United States contains over 6.6 million of the 27 million total OSM river reaches, substantially exceeding other countries’ coverage. In GSriver, approximately 23.6% of river reaches remain unmodified and retain their original lower spatial accuracy. Additionally, while GRIT helps supplement sparsely mapped areas, it still inherits the limitations of DEM-based approaches in flatlands and deltaic systems. Validation also revealed a few residual topological inconsistencies in plain regions such as the Huai River Basin in China. To address these limitations, each river in GSriver includes metadata fields for source and fusion ratio to aid transparency and user interpretation. Users can assess the quality of individual river segments using the OSMratio, GRITpercent, and Hydropercent fields in the attribute table. Rivers with higher OSMratio generally exhibit higher spatial precision, while those with high Hydropercent retain the uncertainty inherent in DEM-derived networks.

Dataset Summary
This dataset provides the most accurate and comprehensive geospatial information on wind turbines in South Africa as of 2025. It includes precise turbine coordinates, detailed technical attributes, and spatially harmonized metadata across 42 wind farms. The dataset contains 1,487 individual turbine entries with validated information on turbine type, rated capacity, rotor diameter, commissioning year, and administrative regions. It was compiled by integrating OpenStreetMap (OSM) data, satellite imagery from Google and Bing, a RetinaNet-based deep learning model for coordinate correction, and manual verification.

Data Structure

• Format: GeoJSON

• Coordinate Reference System (CRS): WGS 84 (EPSG:4326)

• Number of features: 1,487

• Geometry type: Point (turbine locations)

• Key attributes:

  • id: Unique internal identifier

  • osm_id: Reference ID from OpenStreetMap

  • gid, country, type1, name1, type2, name2: Administrative region (based on GADM)

  • farm_name: Name of the wind farm

  • commissioning_year: Year the turbine was commissioned

  • number_of_turbines: Total number of turbines at the wind farm

  • total_farm_capacity: Total installed capacity of the wind farm (MW)

  • capacity_per_turbine: Rated power per turbine (MW)

  • turbine_type: Manufacturer and model of the turbine

  • geometry: Point geometry (longitude, latitude)

Publication Abstract
Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development. The dataset is publicly available.

Citation Notification

If you use this dataset, please cite the following publication:

Kleebauer, M.; Karamanski, S.; Callies, D.; Braun, M. A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis. ISPRS Int. J. Geo-Inf. 2025, 14, 232. https://doi.org/10.3390/ijgi14060232

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

This service was last updated September 2016. This map service draws attention to your thematic content by providing a neutral background with minimal colors, labels, and features. Only key information is represented to provide geographic context, allowing your data to come to the foreground. This light gray basemap supports any strong colors and labels for your theme, creating a visually compelling map graphic which helps your reader see the patterns intended. See these blog posts for more information on how to use this map: Esri Canvas Maps Part I: Author Beautiful Web Maps With Our New Artisan Basemap Sandwich and Esri Canvas Maps Part II: Using the Light Gray Canvas Map effectively. The map shows populated places, water, roads, urban areas, parks, building footprints, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority. This map was compiled by Esri using HERE data, DeLorme basemap layers, MapmyIndia data, and Esri basemap data. The basemap includes boundaries, city labels and outlines, and major roads worldwide from 1:591M scale to 1:72k scale. More detailed nationwide coverage is included in North America, Europe, Africa, South America and Central America, the Middle East, India, Australia, and New Zealand to be fully consistent with the World Street Map and World Topo map down to the 1:9k scale. Data for select areas of Africa and Pacific Island nations from ~1:288k to ~1:9k was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.In addition, some of the data in the World Light Gray Base map service has been contributed by the GIS community. You can contribute your data to this service and have it served by Esri. For details, see the Community Maps Program. For details on data sources in this map service, view the list of Contributors for the World Light Gray Base map.View the coverage map below to learn more about the levels of detail:World coverage map: Shows the levels of detail throughout the world. The World Light Gray Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the Light Gray Base with the Reference service drawn on top. This sample web map contains several examples of thematic content in the light gray canvas basemap with its reference overlay. Note: This map service is not supported in ArcGIS for Desktop 9.3.1 or earlier because it uses the mixed format cache format. Scale Range: 1:591,657,528 down to 1:9,028Coordinate System: Web Mercator Auxiliary Sphere (WKID 102100)Tiling Scheme: Web Mercator Auxiliary SphereMap Service Name: World_Light_Gray_Base

This dataset lists the intersections between named or numbered channels in OpenStreetMap data covering the French territory. The data is generated in the json streamed format expected by the addok geocoder as well as in the geojson format. Example: {“type”:“inter”,“name”:“Chemin de la carronnière/Route de Thoissey D64”,“context”:“L’Abergement-Clémenciat, Ain”,“citycode”:“01001”,“depcode”:“01”,“lon”:4.906772,“lat”:46.163201} name: contains the name and/or route number (e.g. D40, A6) of each track separated by “/” context: contains the name of the municipality, followed by the name of the department citycode: contains the INSEE code of the municipality depcode: contains the INSEE code of the department The code used to generate the file is available at https://github.com/cquest/osm-intersections

Additional file 4. This compressed folder contains the tables with metrics of urban form and IMD, for the six cities under study. (zip)

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures.

This dataset features a detailed map of material stocks for the whole of Austria on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Temporal extent The map is representative for ca. 2018.

Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

The dataset features

area and mass for different street types

area and mass for different rail types

area and mass for other infrastructure

area, volume and mass for different building types

Masses are reported as total values, and per material category.

Units

area in m²

height in m

volume in m³

mass in t for infrastructure and buildings

Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

Extrait quotidien et fichiers de mise à jour de l'intégralité des données OpenStreetMap sur la France métropolitaine.

Ces données sont issues du crowdsourcing effectué par les contributeurs au projet OpenStreetMap et sont donc sous licence ODbL qui impose un partage à l'identique et la mention obligatoire d'attribution doit être "© les contributeurs d'OpenStreetMap sous licence ODbL" conformément à http://osm.org/copyright

Ces données peuvent être utilisées pour produire des fonds de carte, mais aussi effectuer des calculs d'itinéraires, des extractions thématiques, des analyses spatiales, etc.

Données intégrales

Un extrait est généré quotidiennement par filtrage géographique sur l'emprise du territoire métropolitain.

Mises à jour "temps réel"

Chaque minute, un fichier de "diff" est mis à disposition afin de mettre à jour l'extrait France. Voir par exemple son utilisation pour la mise à jour des données de production d'un fond de carte: http://wiki.openstreetmap.org/wiki/Minutely_Mapnik

Format

Les données sont disponibles au format binaire PBF (ProtoBuff) d'OSM. Les fichiers "diff" de mise à jour sont au format XML d'OSM.

Contenu

Les données collectées par le projet OpenStreetMap sont très variées. Elles sont décrites géométriquement (noeuds, chemins, relations) et sémantiquement (attributs clé/valeur).

Pour explorer les attributs OpenStreetMap France met à disposition une instance de l'outil taginfo limitée à la France: http://taginfo.openstreetmap.fr/ Cet outil permet de consulter des statistiques sur le nombre d'objets présents dans les données en fonction des clés et valeurs des attributs.

Pour toute question concernant ces données, vous pouvez contacter exports@openstreetmap.fr

Voir aussi :

• Contours des communes françaises
• Contours des arrondissements français
• Contours des départements français et Cartes SVG des départements
• Contours de régions françaises