Djibouti GIS data

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.

Djibouti Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

Current (April 2015) food security outcomes and forward-looking analysis representing the most likely food security outcomes for the near term (April 2015 - June 2015) and medium term (July 2015 - September 2015) periods.

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.

The dataset represents suitable areas for offshore mariculture of cobia (rachycentron canadum) in Djibouti, produced under the scope of the Hand-in-Hand initiative, using publicly accessible data. Cobia is distributed in warm marine waters worldwide displaying a very large potential suitable area. The geospatial analysis adopts Kapetsky and Aguilar-Manjarrez study (Kapetsky et al., 2013(*)) methodology, where suitability is estimated based on the assumptions: 1. Activity develops within the exclusive economic zones (EEZs). 2. Uses cages for fish. 3. Employs species with proven mariculture technologies and established markets or identified as having good prospects for future development. Analytical criteria and thresholds are defined based on technical limits on cages, cost-effective farming, and species habitat: 1. Depths (25–100 m). 2. Current speeds (10–100 cm/s). 3. Cost limits on distance from shore to offshore installations (25 nm (46.3 km)). 4. Reliable access to a port. 5. Habitat of Cobia (rachycentron canadum). (*) Kapetsky, J. M., Aguilar-Majarrez, J., & Jenness, J. (2013). A global assessment of offshore mariculture potential from a spatial perspective. In FAO Fisheries and Aquaculture Technical Paper (Issue No. 549, pp. 0–181). Food and Agriculture Organization of the United Nations. **Data creation:** 2024-04-17 **Contact points:** Metadata Contact: [FAO-Data](mailto:FAO-data@fao.org) **Data lineage:** lineage source statement The assessment analysis is developed using publicly accessible data. FAO Map Catalogue data 1. FAO aquatic species distribution map of cobia (rachycentron canadum 2. Cultured Aquatic Species Information Programme of cobia (rachycentron canadum 3. Regions with HYCOM current speeds at 30m depth between 1 - 10, 10 - 100 and > 100 cm/s for all months in the year - Based on HYCOM NCODA (HYbrid Coordinate Ocean Model (HYCOM) and Navy Coupled Ocean Data Assimilation (NCODA)) 8.9 km 2004-2008 Web available data 4. Sea Ports - OpenStreetMap 5. EEZ - VLIZ Flanders Maritime Institute - Maritime boundaries geodatabase version 5 (2009 6. Bathymetry – GEBCO – General Bathymetric Chart of the oceans - Gridded bathymetric data sets are global terrain models for ocean and land 2021 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zipped shapefile for Offshore Mariculture Suitability for Cobia - Djibouti

Railway and rail transport network of Djibouti showing the extent and classification of rail infrastructure. Includes passenger and freight lines. Use this layer for rail transport planning, freight logistics analysis, infrastructure assessment, and multimodal transport mapping.

Point locations of airports, airstrips and airfields in Djibouti. Each feature includes the facility name, type, elevation and runway information where available. Use this layer for aviation planning, accessibility analysis, emergency response logistics, and transport network mapping.

Point locations of educational facilities across Djibouti, including primary schools, secondary schools and other learning institutions. Each feature records the facility name, type and administrative location. Use this layer to map education access, identify underserved areas, plan school placement, and analyse catchment areas and travel distances to schools.

Polygon boundaries of second-level administrative units in Djibouti, with names and codes for the country, first-level region, and second-level district. Each feature represents one district. Published by Orion Terrametrics.

Line boundary of the country of Djibouti, with country name and code. Each feature represents the national boundary. Published by Orion Terrametrics.

A 100-metre raster of population for Djibouti for 2015, where each cell value represents the number of people. Published by Orion Terrametrics.

Spatial distribution of estimated pregnancies across Djibouti at 1km resolution. Each grid cell shows the estimated number of pregnancies including live births, stillbirths and abortions. Use this layer for maternal health planning, antenatal care programme design, health facility placement, and reproductive health analysis.

Spatial distribution of estimated births across Djibouti at 1km resolution. Each grid cell shows the estimated number of live births. Use this layer for maternal health planning, antenatal care facility siting, health resource allocation, and reproductive health programme design.

Point locations of health facilities in Djibouti including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.

Water resources and water infrastructure in Djibouti including rivers, lakes, boreholes, wells and water points. Use this layer for water access analysis, WASH programme planning, drought monitoring, flood risk assessment, and water resource management.

Gridded population density of Djibouti for the year 2020 at 1km resolution, adjusted to match UN national population estimates. Each grid cell shows the estimated number of people per square kilometre. Use this layer for settlement pattern analysis, infrastructure planning, service delivery optimisation, and identifying high and low density areas.

Records road line features in Djibouti, with each row representing a road segment from OpenStreetMap. Columns describe road type, surface, smoothness, lanes, width, urban area and predicted surface class. Published by Orion Terrametrics.

Polygon boundaries of first-level administrative regions in Djibouti, with names and codes for the country and region. Each feature represents one region. Published by Orion Terrametrics.

Djibouti administrative level 0-2 boundaries (COD-AB) dataset. The date that these administrative boundaries were established is unknown. This COD-AB has NOT been reviewed for accuracy and necessary changes in the last 12 months. Sourced from www.gadm.org Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the Djibouti COD-PS. No edge-matched (COD-EM) version of this COD-AB has yet been prepared. Please see the COD Portal. Administrative level 1 contains 6 feature(s). The normal administrative level 1 feature type is ""currently not known"". Administrative level 2 contains 20 feature(s). The normal administrative level 2 feature type is ""currently not known"". Recommended cartographic projection: Africa Albers Equal Area Conic This metadata was last updated on January 9, 2025.

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.

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The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Djibouti Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011