Algeria GIS data

geoBoundaries ADM2 boundaries for Algeria: 48 units, representing 2012. Source: geoBoundaries, Wikimedia Commons.

geoBoundaries ADM0 boundaries for Algeria: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

Digital Elevation Model (DEM) of Algeria, The NASA Shuttle Radar Topographic Mission (SRTM). Resolution: 250 m. Source: USGS/NASA-SRTM

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Algeria. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Algeria. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

Algeria administrative level 0-2 boundaries (COD-AB) dataset version 01. This dataset is structured into 2 levels: - Admin 1: 48 - Admin 2: 1541 Dates associated with this dataset: - 19 December 2024: dataset reviewed for accuracy and completeness - 20 January 2021: valid for use by the humanitarian community - 28 April 2020: last edit to the dataset before publishing - 28 April 2020: boundaries created by the source Contributed by OCHA Middle East and North Africa (ROMENA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

**UNOSAT code FR20230724DZA, GDACS Id: 1015944** Cette carte illustre la densité des foyers d'incendies détectés à partir des données VIIRS acquises le 24 juillet 2023. Les wilayas de Bouira, Bejaia et Skikda semblent compter le plus grand nombre de foyers d'incendie avec respectivement 398; 320 et 108. En se basant sur les données démographiques Worldpop, environ 44,000 personnes dans la wilaya de Skikda; 20,000 personnes dans la wilaya de Bejaia et 14,000 personnes dans la wilaya de Bouira vivent à proximité des foyers d'incendie. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

**UNOSAT code FR20230724DZA, GDACS Id: 1015944** Cette carte illustre les zones incendiées telles qu'observées à partir d'une image Pléiades acquise le 27 juillet 2023 à 11:21 heure locale dans les communes de Melbou et Mansouria. Sur les 70 km² de la zone observée, environ 15km² et 8m² de végétation semblent avoir brûlé dans les communes de Melbou et Ziama Mansouria. En se basant sur les données démographiques Worldpop, dans les communes de Melbou et Ziama Mansouria respectivement environ 2,600 et 5,000 personnes sont potentiellement impactées par les feux de forêt. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

**UNOSAT code FR20230724DZA** Live web map showing analysis related to fire in Algeria.

**UNOSAT code FL20230526DZA, GDACS Id: 1102025** This map illustrates satellite-detected flash floods in Khemisti, Bou Ismail and Bouharoun Communes, Wilaya of Tipaza, Algeria as observed using Sentinel-2 satellite imagery acquired on 27 May 2023 at 11:36 local time. Within the surface analysed of 12,000 ha, about 63 ha of potentially affected area observed. UNOSAT identified 2 damaged ports, 794 affected and potentially affected structures & 31 affected and potentially affected roads. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code FR20230724DZA, GDACS Id: 1015944** Cette carte illustre les zones incendiées telles qu'observées à partir d'une image Sentinel-2 acquise le 25 juillet 2023 à 18:56 heure locale dans la Wilaya de Bejaia, Algérie. Sur les 540 km² de la zone observée, environ 180 km² de végétation semblent avoir brûlé dans la Wilaya de Bejaia. En se basant sur les données démographiques Worldpop, dans les communes de Semaoune, Beni Djellil et Timzrit respectivement environ 7,200, 4,800 et 4,600 personnes sont potentiellement impactées par les feux de forêt. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

**UNOSAT code FR20230724DZA, GDACS Id: 1015944** Cette carte illustre les zones incendiées telles qu'observées à partir d'une image Sentinel-2 acquise le 25 juillet 2023 à 18:56 heure locale dans les communes de Melbou et Mansouria. Sur les 44 km² de la zone observée, environ 11 km² et 7 km² de végétation semblent avoir brûlé dans les communes de Melbou et Ziama Mansouria. En se basant sur les données démographiques Worldpop, dans les communes de Melbou et Ziama Mansouria environ 1,900 et 5,800 personnes sont potentiellement impactées par les feux de forêt. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

**UNOSAT code FL20230526DZA, GDACS Id: 1102025** Cette carte illustre l’étendue ainsi que l'impact des crues subite tels qu’observés à partir de l’image satellite Sentinel-2 acquise le 27 mai 2023 à 11:36 heure locale dans les communes de Khemisti, Bousmail et Bouharoun dans la wilaya de Tipaza en Algérie. Dans la zone analysée d’environs 12,000 ha, une zone d’une soixantaine d’hectares semble avoir subi des dégâts importants. Dans cette zone, UNOSAT a identifié 2 ports endommagés, 794 structures affectées et potentiellement affectées ainsi que 31 sections de routes ayant subi des dégâts. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Merci de bien vouloir adresser vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

**UNOSAT code: FR20210811DZA** Cette carte illustre les zones incendiées telles qu'observées à partir d'une image Sentinel-2 acquise le 15 août 2021 à 10:50 UTC dans la commune d'El Amra. Sur les 22,300 ha analysés, environ 2,500 ha de couverture végétale/forêts apparaissent comme brûlés. Ceci est analyse préliminaire et n'a pas encore été validée sur le terrain. N'hésitez pas à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

**UNOSAT code: FR20210811DZA** Cette carte illustre la densité des foyers d'incendies détectés à partir des données VIIRS acquise entre le 11 et le 13 août 2021. Les wilayas de Bejaia, Tizi Ouzou et Ain-Defla semblent compter le plus grand nombre de foyers d'incendie avec respectivement 1,080; 573 et 190. En se basant sur les données démographique Worldpop, environ 70,000 personnes dans la wilaya de Tizi Ouzou; 25,000 personnes dans la wilaya de Bejaia et 13,000 personnes dans la wilaya de Skikda vivent à proximité des foyers d'incendie. Ceci est une analyse préliminaire et n'as pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires au Centre Satellitaire des Nations Unies (UNOSAT).

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

Cette carte illustre la densité des foyers d'incendies détectés à partir des données MODIS de la NASA, accessible via NASA FIRMS avec une résolution de 1 km, dans le nord de l'Algérie entre le 17 et le 19 juillet 2017. 54 foyers ont été détectés durant cette période alors que pour la période allant du 14 au 16 juillet leur nombre s'élevait à 99. Cette carte confirme une tendance à la baisse du nombre de foyers d'incendies sur cette période. Les aperçus illustrent des zones incendiées observées à partir d'images satellitaires, Alsat-2B du 06 juillet 2017 sur une zone à l'est de Tamalous (W. de Skikda) et Landsat-8 acquises le 25 juin et le 11 juillet 2017 couvrant les massifs situés à la limite des wilayas de Jijel et de Bejaia. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires à UNITAR-UNOSAT.

Cette carte illustre les zones d'incendies observées à partir d'images satellitaires couvrant les secteurs des wilayas de Tizi Ouzou et de Bejaia en utilisant une différence de ratios de brûlure normalisé (NBR) dérivée des images Sentinel-2 du 29 Juin 2017 et du 19 Juillet 2017 ainsi que les foyers d'incendies détectés entre le 01 Juillet et le 19 Juillet 2017 à partir des données MODIS. Sur l'emprise de cette carte, environ 1.106 ha semblent avoir brulé essentiellement dans les communes de Ifigha (230 ha), Yakourene (188 ha), et Idjeur (179 ha) dans la wilaya de Tizi Ouzou et dans la commune de Akfadou (163 ha) dans la wilaya de Bejaia. La surface totale incendiée pourrait être sous-estimée étant donné que certaines zones semblent avoir déjà subi des incendies. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Ne pas hésiter à envoyer vos commentaires à UNITAR-UNOSAT.