OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

highway IS NOT NULL

Features may have these attributes:

• highway
• surface
• name
• smoothness
• lanes
• width
• source
• layer
• oneway
• bridge

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.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay')

Features may have these attributes:

• name
• covered
• source
• width
• natural
• depth
• layer
• waterway
• water
• blockage
• tunnel

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.

OpenStreetMap contains roughly 750.5 thousand km of roads in this region. Based on AI-mapped estimates, this is approximately 72 % of the total road length in the dataset region. The average age of data for the region is 2 years ( Last edited 2 days ago ) and 9% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['highway'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer
• source
• name:vi

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.

OpenStreetMap is a project launched in 2004 to create a free world map. We collect data on roads, railways, rivers, forests, homes and anything else around the world, commonly seen on maps. Because we collect the data yourself and not distinguish from existing cards, we have all the rights to it. Open StreetMap data may be used free of charge by anyone and further processed at any time. This dataset contains the Berlin section of the Planet File. Other formats such as OSM-XML, shapefiles, SVG, Adobe Illustrator, Garmin GPS, GPX, GML, KML, Manifold GIS, grid graphics can be exported at http://wiki.openstreetmap.org/wiki/Export.

Open StreetMap-data questions can be discussed here: Http://forum.openstreetmap.org/viewforum.php?id=14

Note: updates to this beta layer are currently paused while we sync new versions of the OSM layers for Europe.This feature layer provides access to OpenStreetMap (OSM) highways data for Europe, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes highway features defined as a query against the hosted feature layer (i.e. highway is not blank).In OSM, a highway describes any kind of motorway, road, street or path. These features are identified with a highway tag. There are hundreds of different tag values for highway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular highway types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Streets level or 1:20k scale) to see the highway features display. You can click on a feature to get the name of the highway (if available). The name of the highway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this highway layer displaying just one or two highway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. highway is path), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. cycleway and pedestrian) that are ready to use, but not for every type of highway.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

highway IS NOT NULL

Features may have these attributes:

• smoothness
• name
• surface
• bridge
• source
• width
• oneway
• lanes
• layer
• highway

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.

This feature layer provides access to OpenStreetMap (OSM) tourist attraction point data for North America, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes tourism features defined as a query against the hosted feature layer (i.e. tourism is not blank).In OSM, tourism features are places and things of specific interest to tourists including places to see, places to stay, things and places providing information and support to tourists. These features are identified with a tourism tag. There are hundreds of different tag values used in the OSM database. In this feature layer, unique symbols are used for several of the most popular tourism types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Cities level or 1:160k scale) to see the tourism features display. You can click on a feature to get the name of the tourism feature. The name of the feature will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this tourism layer displaying just one or two tourism types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. tourism is ruin), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

OpenStreetMap contains roughly 594.6 thousand km of roads in this region. Based on AI-mapped estimates, this is approximately 32 % of the total road length in the dataset region. The average age of data for the region is 3 years ( Last edited 2 days ago ) and 8% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['highway'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer
• source
• name:ur

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.

Exports of the French administrative division at the departmental level (districts of departments) 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 content of “departments” 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 produced 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 departments, including the DOM and Mayotte.

For each region, the following attributes are added:

— code_insee: 2-character INSEE code of the department (e.g. 01, 2A) — name: name of department (e.g. Ain, South Corsica) — nuts3: European NUTS3 code corresponding to the departure (e.g. FR711, FR831) — Wikipedia: wikipedia entry (language code followed by article name) — Wikidata: wikidata ID of the department (from 2016) — surf_km2: area in km² of the department (from 2018)

Historical

— 20-12-2013: first generation of the file, based on the OSM communal cutting at 19-12-2013 — 06-03-2014: second generation of the file, based on the OSM communal cutting at 06-03-2014 — 18-02-2016: third generation of the file, based on the municipal division OSM at 18-02-2016 taking into account the change between the Loire-Atlantique and Maine-et-Loire following the merger of Le Fresne-sur-Loire (44) with Ingrandes (49) — 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 (including a commune changing department). — 01-01-2018: version including mergers of municipalities until 01-01-2018, including the change of department of two merged municipalities modifying the boundaries of Calvados, Manche, Loire-Atlantique and Maine-et-Loire.

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:

— live extraction by API — 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 French regions

1. OverviewRoad intersections are crucial nodes in urban networks, where transportation lanes converge and socioeconomic activity is concentrated. While methods to identify road intersections using raster maps, satellite images, and trace data have been explored, challenges in accuracy and consistency remain.This paper proposes a method for identifying intersections based on OpenStreetMap data, which records networks at the lane level. Unlike geometric line intersections in OpenStreetMap, the identified intersections “summarize” parallel lanes and incorporate branch information, such as counts and orientations. The proposed method uses a vector-raster fusion approach to initially locate intersections, followed by hierarchical geometric configuration to improve accuracy and extract branch data.Experimental results show that the method effectively handles complex road networks in various cities, accurately identifying intersections and their branches. Experiments conducted on OpenStreetMap data from 7 cities yielded over 98% precision and 97% recall, outperforming the popular OSMnx tool. Additionally, lane synthesis at intersections achieved 99.43% precision and 98.34% recall. Urban characteristics can be quantitatively analyzed based on the identified road intersections. For instance, the proportion of four-way road intersections in New York is 52.6%, whereas in London, it is 9.6%, which may be attributed to the differing urban histories of these cities.2. Instructions for dataThis dataset contains road intersection data of seven cities (Berlin, Beijing, London, Nanjing, New York City, Rome, Shanghai) identified by our proposed method.The data of each city are stored in the folder named by its abbreviation.In each folder, there will be five *.shp files:(1) Roadnetwork (roads.shp)This layer stores the OSM road network with tag attributes.(2) Intersection points (cross-res.shp)This layer stores the location of the intersection point and the general characteristics of the road layout, including the number of intersecting roads and layout type. This provides the necessary location information for mapping and spatial analyses.(3) Related road lines (fullLine-res.shp/fail-fullLine.shp)The original road lines related to intersections extracted from the road networks were stored in this layer, preserving the inherent attributes of the original dataset. In addition, matching information, indicating whether the lines represent the same road, was stored as an attribute.(4) Synthesized road lines (simpleLine-res.shp)The geometries of this layer store synthesized road lines representing road orientations, whereas the attributes store acquired characteristics, including roadway configurations and match indices, which allow the synthesized road lines to be connected to the original roads from which they were derived. This connection is achieved through “pt_id” linked with cross-res.shp and “match_id” linked with fullLine-res.shp.3. Instructions for codesThis code repository is organized into eight folders and two files:(1) Folder: candidateIdentifyThis folder contains code related to the identification of candidate junctions or intersections from the input data. Each script plays a specific role in the overall process:bufferThin.py: Implements a thinning algorithm to generate a skeleton of buffered road geometry.candidateIdentify.py: The core script for identifying candidate points for road intersections in a spatial dataset.fastJunction.py: Provides an optimized method for detecting junctions quickly in large datasets by leveraging a fast hit-or-miss operation.junctionGeo.py: Handles the geometric processing of junction points, focusing on transforming raster cells into geographical positions.(2) Folder: compareEvaluationclipByregion.py: Clips spatial data to a specific region, which is useful for limiting analysis to a predefined geographic area. This is typically used to obtain intersections identified within the randomly selected regions.(3) Folder: geometricConfigurationTemplateMatchThis folder includes tools for matching geometric templates of road intersections:templateMatcher.py: The main module that matches geometric configurations of road intersections to the determined templates.utils_g.py: Provides utility functions that assist in geometric operations and template matching processes.(4) Folder: maxRadiusBufferThis folder focuses on the code of a greedy algorithm that produces the largest nonoverlapping buffers for a given collection of points.:maxRadBuffer.py: Core script for this algorithm.(5) Folder: intersectionDistanceAnalysisThis folder contains scripts for analyzing distances between intersection points for parameter determination:distanceAnalysis.py: Calculates the distances between identified intersections and analyze their distribution.(6) Folder: proportionDrawerThis folder contains scripts using R to produce pie charts in Figure 14.(7) Folder: refineCandidateThis folder contains a script that refines the previously identified intersection candidates.refineCandidate.py: This core script refines the set of candidate intersections by applying further geometric or statistical methods.(8) Folder: resultAnalysisThis folder is used for analyzing the identified results for insights of urban characteristics:angleAnalysis.py: Compute and analyze angles between road branches at intersections.4. Instructions on how to reproduce each reported findingsThe file 'instructions_on_how_to_reproduce_each_reported_findings.md' details the steps to reproduce the figures and the tables shown in the paper.

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:

• is_in
• population
• name
• place
• source

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.

Geographical contours of the EPCI resulting from the crossing of the municipal boundaries of OpenStreetMap and the data of the Ministry of the Interior on EPCIs dating from 2013.

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

Descriptive of the content of “epci” files

Data Origin

The data come from the Ministry of the Interior crossed with the communal division from the OpenStreetMap cartographic database. The latter 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, it is the limits of the GEOFLA of IGN that have been used as well as the plot of the coasts from the aerial images of Bing.

Source for EPCI 2013: https://www.data.gouv.fr/fr/dataset/adhesion-des-communes-a-un-etablissement-public-de-cooperation-intercommunale-epci-a-fiscal-00000000

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

Format

These files are offered in shapefile format, in WGS84 projection with several levels of detail:

— simplification at 5 m — simplification at 50 m — simplification at 100 m

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

Content

These files contain all the EPCIs contained in the file of the Ministry of the Interior (see “Origin of the data”).

For each EPCI, the following attributes are provided:

— siren_epci: SIREN code assigned by INSEE to EPCI (source Min. Inland) — name_epci: name of EPCI (source Min. Interior) — ptot_epci: total population of the EPCI (source Min. Inland) — nb_comm: number of municipalities in the EPCI — surf_km2: area of the EPCI in km² on the WGS84 spheroid

Historical

— 20-12-2013: first generation of the file, based on the OSM communal cutting at 19-12-2013

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

See also:

— contours of EPCI 2014 — contours of the French municipalities — contours of the French arrondissements — contours of the French departments and SVG maps of the departments — contours of French regions

This data package contains extracts from open datasets to support
the tutorials available at https://github.com/nismod/snail/

This version of the data goes with v0.1 of the tutorials:

https://github.com/nismod/snail/releases/tag/v0.1

WRI Aqueduct Flood Hazard Maps

`flood_layer` contains data extracted and derived from the Aqueduct
Flood Hazard Maps (version 2, updated October 20, 2020).

See https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps

These data are shared under the CC-BY Creative Commons Attribution
License 4.0 - https://creativecommons.org/licenses/by/4.0/

Citation: Ward, P.J., H.C. Winsemius, S. Kuzma,
M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et
al. 2020. “Aqueduct Floods Methodology.” Technical Note.
Washington, D.C.: World Resources Institute. Available online at:
www.wri.org/publication/aqueduct-floods-methodology.

Ghana - Subnational Administrative Boundaries

`gha_admbnda_gss_20210308_shp` contains data from Ghana Statistical
Services (GSS) contributed to Humanitarian Data Exchange by the OCHA
Regional Office for West and Central Africa, updated 11 March 2021.

See https://data.humdata.org/m/dataset/ghana-administrative-boundaries

These data are shared under the Creative Commons Attribution for
Intergovernmental Organisations (CC BY-IGO) - https://creativecommons.org/licenses/by/3.0/igo/

Ghana OpenStreetMap Extract

`ghana-latest-free.shp` contains data extracted from OpenStreetMap
and downloaded from GeoFabrik.

The files in this archive have been created from OpenStreetMap data
and are licensed under the Open Database 1.0 License. See
www.openstreetmap.org for details about the project.

This file contains OpenStreetMap data as of 2021-03-22T21:21:57Z.

More recent updates will be made available daily here:

http://download.geofabrik.de/africa/ghana-latest-free.shp.zip

A documentation of the layers in this shape file is available here:

http://download.geofabrik.de/osm-data-in-gis-formats-free.pdf

Ghana Road Network

`GHA_OSM_roads.gpkg` contains data derived from the OpenStreetMap
extract above, and can be reproduced by running through nismod/snail
tutorial 01.

These data are shared under the same Open Database 1.0 License. See
www.openstreetmap.org for details about the project.

Natural Earth Country Boundaries

`ne_10m_admin_0_countries` contains Natural Earth 1:10m Cultural Vectors,
Admin ) - Countries version 4.1.0

See https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/

These data are declared to be in the public domain, and may be shared
and modified without restriction - https://www.naturalearthdata.com/about/terms-of-use/

QGIS project

`overview.qgz` is a QGIS project intended to help preview and explore
the data in this package.

It is shared under the CC-BY Creative Commons Attribution
License 4.0 - https://creativecommons.org/licenses/by/4.0/

Please cite it as part of this data package, by Tom Russell (2021).

Results

`results` contains the results of analysis that can be reproduced
by running through all the nismod/snail tutorials.

These are derived from all the data above, shared under the
combined terms of Open Database 1.0 License and CC-BY licenses as
applicable to derived, extracted and modified data.

Anguilla OSM barrierpoints file | OpenStreetMap transportation (1/2) shapefile provided by MAPZEN

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

This datatset contains a land use classification of Dakar (Senegal) at the street block level. It was created following the methodology presented in [1].

Description of the files:

"Dakar_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.

"Dakar_landuse_style.zip" : Files for style of the shapefile.

Attribute table content:

"CAT", "GID" : ID of the street block

"PROB_ACS" : Probability to belong to class ACS

"PROB_AGRI" : Probability to belong to class AGRI

"PROB_BARE" : Probability to belong to class BARE

"PROB_DEPR" : Probability to belong to class DEPR

"PROB_PLAN" : Probability to belong to class PLAN

"PROB_VEG" : Probability to belong to class VEG

"FIRST_LABE" : Class with the highest classification probability

"SEC_LABEL" : Class with the second highest classification probability

"FIRST_PROB" : Value of the highest classification probability

"SEC_PROB" : Value of the second highest classification probability

"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"

"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)

"MAP_LABEL" : Final classification label with uncertainty and different density classes

Legend classes label:

"AGRI" : Agricultural vegetation

"VEG" : Natural vegetation

"BARE" : Bare soils

"ACS" : Non-residential built-up (administrative, commercial, services, etc.)

"PLAN" : Planned residential built-up

"PLAN_LD" : Planned residential low density built-up

"DEPR" : Deprived residential built-up

"UNCERT" : Uncertain classification

References:

[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", ISPRS Int. J. Geo-Inf. 2018, 7(7), 246. https://doi.org/10.3390/ijgi7070246

[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. https://doi.org/10.5281/zenodo.1290637.

Funding:

This dataset was produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be) and REACT (http://react.ulb.be), funded by the Belgian Federal Science Policy Office (BELSPO).

Exports du découpage administratif français au niveau régional (contours des régions) issu d'OpenStreetMap produit dans sa grande majorité à partir du cadastre.

Ces données sont issues du crowdsourcing effectué par les contributeurs au projet OpenStreetMap et sont 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

Il s'agit d'un export semi-automatique avec des géométries allégées et vérifiées topologiquement (pas de chevauchement). A partir de 2016, les géométries sont d'origine, non simplifiées.

Descriptif du contenu des fichiers "regions"

Origine

Les données proviennent de la base de données cartographiques OpenStreetMap. Celles-ci ont été constituées à partir du cadastre mis à disposition par la DGFiP sur cadastre.gouv.fr. En complément sur Mayotte où le cadastre n'est pas disponible sur cadastre.gouv.fr, le tracé des côtes a été fait à partir des images aériennes de Bing.

Plus d'infos: http://prev.openstreetmap.fr/36680-communes

Format

Ces fichiers sont proposés au format shapefile, en projection WGS84 avec plusieurs niveaux de détails (jusqu'en 2015): - simplification à 5m - simplification à 50m - simplification à 100m

La topologie est conservée lors du processus de simplification (cf: http://prev.openstreetmap.fr/blogs/cquest/limites-administratives-simplifiees)

Contenu

Ces fichiers contiennent l'ensemble des régions françaises, y compris les DOM et Mayotte.

Pour chaque région, les attributs suivants sont ajoutés:

• code_insee: code INSEE à 2 chiffres de la région (ex: 42)
• nom: nom de la région (ex: Alsace)
• wikipedia: entrée wikipédia (code langue suivi du nom de l'article, ex: fr:Alsace)
• wikidata: identifiant wikidata de la région
• surf_km2: superficie de la région en km2 sur le sphéroid WGS84

Historique

• 01-01-2017 : version basée sur le découpage communal OSM au 01-01-2017 incluant la fusion de 566 communes en 178 communes nouvelles.
• 01-01-2018 : version basée sur le découpage communal OSM au 01-01-2018 (changement marginal de géométrie)

Versions prédécentes disponibles sur: http://osm13.openstreetmap.fr/~cquest/openfla/export/

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

Voir aussi:

• Cartes au format SVG
• Contours des communes françaises
• Contours des EPCI 2014 et Contours des EPCI 2013
• Contours des arrondissements français
• Contours des départements français et Cartes SVG des départements

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

highway IS NOT NULL

Features may have these attributes:

• layer
• name
• source
• bridge
• surface
• smoothness
• width
• oneway
• lanes
• highway

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.

As of January 1, 2016, some of the regions established in the 1960s merge to leave only 13. # Content Each department is delivered as a ZIP archive that contains several map layers at the fomat shapefile (the.doc of mapping): — places.shp: names of cities or neighborhoods — roads.shp: all paths from highway to pedestrian path — buildings.shp: built space — raillways.shp: railways — waterways.shp: the hydrolic network — points.shp: a list of points of interest — natural.shp: green areas — landuse.shp: land occupancy — admin-department.shp: the department In addition, a project file QGis is provided in the archive, in order to visualise the layer overlay in a free tool. # Origin The geofabrik site is well known for providing reference maps extracted from OpenStreetMap on the whole world, but it offers the old split, this data set is the up-to-date declination of the new regions! The data comes from the OpenStreetMap community and free cartographic database. The division by department comes from the ‘Contours of the French departments from OpenStreetMap’. Shapefiles are extracted according to the method exposed by Maxime Résibois’ excellent article on PortalSIG: http://www.portailsig.org/content/recuperer-des-donnees-openstreetmap-gdalogr The sources of automatic extraction processing are available on github. They rely on tuttle, a build system for data. # License This data comes from crowdsourcing carried out by the contributors to the OpenStreetMap project and is under ODbL license which requires an identical sharing and the mandatory attribution mention must be “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright # List of regions covered — Auvergne-Rhône-Alpes — Bourgogne-Franche-Comté — Brittany — Centre-Val de Loire — Corsica — Great East — Guadeloupe — Guyana — Up-de-France — Island of France — Martinique — Mayotte — Normandy — New-Aquitaine — Occitanie — Country of the Loire — Provence-Alpes-Côte d’Azur — The Reunion

Rivers in Crimea as traced on OpenStreetMap (https://www.openstreetmap.org) in SHP file format. See "HGC Description" document for details.