Filter by:
1597 results matching current filter selection Apply or Reset ?
  • OCHA Nigeria
    Updated July 26, 2017 | Dataset date: Feb 23, 2017
    The dataset represents the Common Operational Data (COD) for administrative boundaries of Nigeria. Each administrative unit contains the p-code and name. Admin COD datasets (Admin 0 – 2) for Nigeria are endorsed by the Office of the Surveyor General of the Federal Republic of Nigeria (OSGOF) and the IMWG Feb 2017. See metadata for description of methodology for admin level 3 and the cleaning and processing performed by ITOS. Levels 0 - 3 are polygonal administrative units, Level 3 only covers Adamawa, Borno and Yobe States and are for operational purposes only.
  • Cette carte illustre les zones d'incendies observées à partir d'images satellites Sentinel-2 couvrant des secteurs à l'ouest et à l'est des wilayas de Médéa et d'Ain Defla dérivées des images Sentinel-2 du 12 juillet 2017 et du 09 Juillet 2017. Sur l'emprise de cette carte, environ 3353 ha semblent avoir brulé, essentiellement dans les communes de Ouled Antar (1154ha) et Ouled Bouachra (854 ha) dans la wilaya de Médéa et 335ha incendiés dans la commune d'Oued Djemaa dans la wilaya d'Ain Defla. 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.
  • Cette carte illustre les zones d'incendies observées à partir d'images satellites Sentinel-2 couvrant des secteurs des wilayas de Tizi Ouzou et Boumerdès en utilisant une différence de ratios de brûlure normalisé (NBR) dérivée des images Sentinel-2 du 29 Juin 2017 et du 09 Juillet 2017 ainsi que les foyers d'incendies détectés entre le 01 Juillet et le 13 Juillet 2017 à partir des données MODIS. Sur l'emprise de cette carte, environ 113ha de zones végétalisées semblent brûlées principalement des zones forestières. 35 foyers incendiés ont été détectés durant cette période. 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 la densité des foyers d'incendies détectés à partir des données du spectro radiomètre d'imagerie de résolution modérée (MODIS) de la NASA, accessible via NASA FIRMS, dans le nord de l'Algérie entre le 1er et le 13 juillet 2017. 813 foyers ont été détectés durant cette période avec un pic de 318 foyers enregistrés le 11 juillet 2017 sur la zone de la carte qui s'étale de la wilaya de Chlef à la frontière Est. 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.
  • This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 30 June 2017, UNITAR - UNOSAT identified a total of 5,536 affected structures within this part of city. This marks an overall increase of 37% in damage affected structures from the assessment fourteen days prior on 16 June. Approximately 490 of these were destroyed (9% of the total affected buildings), 3,310 severely damaged (60% of the total affected buildings) and 1,736 moderately damaged (31% of the total affected buildings). This marks a 150% increase in destroyed buildings, 0% in moderately damaged buildings and 57% severely damaged buildings from the 16 June assessment. The most heavily impacted area appears to be the Ammu Baqqal neighbourhood (see inset). Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 16 June 2017, UNITAR - UNOSAT identified a total of 4,040 affected structures within this part of city. This marks an overall increase of 56% in damage affected structures from the assessment five days prior on 11 June. Approximately 196 of these were destroyed (5% of the total affected buildings), 2,107 severely damaged (52% of the total affected buildings) and 1,737 moderately damaged (43% of the total affected buildings). This marks a 28% increase in destroyed buildings, 17% in moderately damaged buildings and 121% severely damaged buildings from the 11 June assessment. The most heavily impacted area appears to be the Bab al-Tub neighbourhood (see inset). Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 11 June 2017, UNITAR - UNOSAT identified a total of 2,589 affected structures within this part of city. Approximately 153 (6% of the total affected buildings) of these were destroyed, 950 (37% of the total affected buildings) severely damaged and 1,486 (57% of the total affected buildings) moderately damaged. The most heavily impacted location appears to be the Bazaar area (see inset). Due to the densely constructed nature of this part of the city, these values might be underestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected areas of displaced person settlements in the Daraa and Quneitra Governorates, Syria. Using satellite imagery collected between 3 November 2016 and 7 June 2017, covering an analyzed area of 2,513 sq km (251,362 ha), UNOSAT identified 766 locations where possible displaced persons shelters are visible. Approximately a total of 5,516 shelter structures are estimated to be within the 777 ha of settlement areas. Due to the characteristics of the possible IDP settlements in the area, being the majority of them scattered structures in agricultural fields, the number of possible IDP shelters might have been overestimated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected areas of displaced person settlements in the Idlib, Lattakia and Aleppo Governorates, Syria. Using satellite imagery collected between 7 September 2016 and 10 February 2017, covering an analyzed area of 9,039 sq km (903,920 ha), UNOSAT identified 267 locations where possible displaced persons shelters are visible. Approximately a total of 63,771 shelter structures are estimated to be within the 1,501 ha of settlement areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected damage in the city of Daraa, Syrian Arab Republic. Using satellite imagery acquired 07 February and 06 January 2017, 19 April 2016, 04 June 2015, 01 January 2014, and 07 September 2013, UNITAR - UNOSAT identified a total of 1,503 affected structures within the city. Approximately 224 of these were destroyed, 498 severely damaged, and 781 moderately damaged. While some of the city was damaged by 19 April 2016, 419 structures were newly damaged and 7 structures experienced an increase in damage between that date and 07 February 2017. This analysis does not include pre-war military bases and facilities. This analysis was done as part of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates the satellite-detected water extent in Maguindanao and Cotabato provinces, Phillipines. The UNITAR-UNOSAT analysis used a Sentinel-1 satellite image acquired on the 07 June 2017 and could observe areas with standing waters mainly affecting agricultural fields. Within the map extent, the UNITAR-UNOSAT analysis identified ~34,000 ha of those areas and ~127 km of potentially affected road mainly local roads. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • This map illustrates satellite-detected possible damaged shelter structures in Kutupalong Refugee Camp, Palong Khali Union and Cox Bazar District, Chittagong Division, Bangladesh. The UNITAR-UNOSAT analysis used Pleiades satellite imagery acquired the 6th and the 7th June 2017 as post-images. The UNITAR-UNOSAT analysis identified 1,105 possible damaged shelter structures within the extent of the camp. Kindly note that the number of possible shelter damaged structures could have been under or overestimated in some areas due to the cloud cover and the delimitation of the camp extent. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR – UNOSAT.
  • This map illustrates satellite-detected potentially affected shelters in Nayapara refugee camp in Teknaf Union, Cox s Bazar District, and Chittagong Division of Bangladesh. About 12,500 people are registred in this camp. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired the 05th of June 2017 and could identify 560 possible damaged shelter structures within the extent of the camp. Kindly note that the number of damaged shelters could have been underestimated as some groups of shelter structures might be identified as one structure.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected potentially affected shelters in Leda refugee camp in Nhilla Union, Cox s Bazar District, and Chittagong Division of Bangladesh. About 18,350 people are registered in this camp. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired 05 June 2017 and could identify 215 possible damaged shelter structures within the extent of the camp. Kindly note that the number of damaged shelters could have been underestimated as some groups of shelter structures might be identified as one structure. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • Syria Administrative boundaries for levels 0 - 4, with Arabic Names, English Names, and p-codes. Geodatabase maintains Arabic names better than shapefile Note that Admin 4 is the populated places layer. Admin Level 1= Governorate = Mohafaza Admin Level 2 = District = Mantika Admin Level 3 = Sub-district= Nahya Admin Level 4 = Populated places = City or Village or Farm or Camp
  • OCHA ROWCA
    Updated July 18, 2017 | Dataset date: Jul 6, 2017
    Admin Level 1 Boundaries (Departments) and Admin Level 2 Boundaries (Districts) of Congo The dataset represents the departments and districts of Congo with harmonized PCODE of ROWCA and Humanitarian Response pcodes
  • OCHA ROLAC
    Updated July 17, 2017 | Dataset date: Jan 1, 2009
    Admin COD datasets endorsed by OCHA Colombia on December 2015; see metadata for description of cleaning and processing performed by ITOS. Colombia Administrative Boundaries Level 0 (Colombia), Level 1 (Departments), Level 2 (Municipalities) This dataset contains a link to GeoNode Colombia and a zipped file. Colombia Admin Level 2 Boundaries
  • OCHA Burundi
    Updated July 9, 2017 | Dataset date: Jul 9, 2017
    This data set is an updated version of previously uploaded in https://data.humdata.org/dataset/burundi-admin-level-1-boundaries. The zip file contains Admin1 boundaries for Burundi in shapefile format. The second file contains Admin1 boundaries for Burundi in topojson format.
  • OCHA ROCCA
    Updated July 9, 2017 | Dataset date: Jan 1, 2000
    SRTM DEM Data: Resolution 90m; There are five 5 x 5 deg tiles to cover the whole country. Version 3 of the CSI-SRTM data (srtm.csi.cgiar.org) with improved hole-filling algorithms which make use of ancilliary data sources where they are available. The data originate in the NASA Shuttle Radar Topographic Mission (SRTM) data held at the National Map Seamless Data Distribution System . The data have been processed by Dr. Andrew Jarvis of the CIAT Land Use project , in collaboration with H.I. Reuter, A. Nelson and E. Guevara to fill in data voids and produce a seamless mosaic.
  • OCHA Burundi
    Updated July 9, 2017 | Dataset date: Jul 9, 2017
    The zip file contains Health Districts (Admin2) boundaries for Burundi in shapefile format. The second file contains Health Districts (Admin2) boundaries for Burundi in topojson format. First health level boundaries correspond to Admin 1 level (provinces) boundaries that can be found here: https://data.humdata.org/dataset/burundi-admin-level-1-boundaries-2017
  • OCHA Burundi
    Updated July 9, 2017 | Dataset date: Jul 9, 2017
    This data set is an updated version of previously uploaded at https://data.humdata.org/dataset/burundi-admin-level-2-boundaries. The zip file contains Admin2 boundaries for Burundi in shapefile format. The second file contains Admin2 boundaries for Burundi in topojson format.
  • OCHA ROWCA
    Updated June 26, 2017 | Dataset date: Jan 1, 2009
    Administrative Boundaries Level 0 (International), 1 (regions) and 2 (departments) These dataset represents the international, first and second Administrative boundaries of Cameroon: pays, regions and departments.
  • OCHA South Sudan
    Updated June 26, 2017 | Dataset date: Feb 16, 2016
    Administrative boundaries (Level 1 - States), (Level 2 - Counties including disputed Abyei region) and Undetermined boundary lines. Digitised from Russian Topo maps 200k (1970). Also includes a list of Admin level 3 - Payams. Data source: South Sudan Inter Cluster Information Management Working Group (ICIWG), National Bureau of Statistics (NBS) and OCHA. PCodes and cleaned by OCHA and ITOS.
  • OCHA Côte d'Ivoire
    Updated June 26, 2017 | Dataset date: Aug 29, 2016
    Administrative Level 0, 1 (districts), 2 (regions) and 3 (departments) boundaries of Côte d'Ivoire Ces fichiers représentent les différents Districts, régions, departements et sous-préfectures de la Côte d'voire. Ces fichiers ont été mise à jour par OCHA-CI en collaboration avec le CNTIG et ITOS
  • OCHA ROMENA
    Updated June 26, 2017 | Dataset date: Nov 30, 2016
    Administrative boundaries