The ACU’s Information Management Unit conducted the 4th version of its annual research “Schools in Syria”, to highlight the impact of the Syrian conflict on education and the needs of students and school supplies. This is the most representative, nuanced iteration of this study to date, covering 4,079 schools within 99 sub-districts across 10 governorates, building upon 35,925 data e-forms with 31,846 forms on perception surveys. It has significant increase in the number of the functional schools addressed over its first version to the current one by 2,572 schools.
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.
Sex and age disaggregated population data by various administrative levels (1 to 4) based on 2015 Census with Philippines Standard Geographic Code (PSGC).
These CSV population statistics files are suitable for database or ArcGIS joins to the shapefiles found on HDX here.
Updated October 11, 2018
| Dataset date: Oct 11, 2018
The spreadsheet have 2 worksheet (Pop and Geoimpact). Geoimpact is the result of overlaying the shakemap with admin4 (village) boundaries from BPS. This processing and analysis was done by the World Food Programme (WFP).
South Sudan administrative levels 0 (country), 1 (state) and 2 (county) 2019 population estimates
This population statistics Common Operational Database (COD-PS) was endorsed by the South Sudan Inter Cluster Coordinating Group (ICCG) and Humanitarian Country Team (HCT) on August 14, 2018.
These tables are suitable for database or GIS link to the South Sudan administrative level 0-2 boundaries files on HDX.
Updated October 22, 2018
| Dataset date: Oct 19, 2018
Tanzania administrative level 0 (country), 1 (region), 2 (district), and 3 (ward) boundary and line shapefiles, KMZ files, geodatabase
These boundary files are suitable for database or GIS linkage to the Tanzania administrative level 0-3 population statistics tables.
These boundaries reflect the creation of Songwe region (TZ26) in January 2016 from the western half of Mbeya Region.
Updated October 16, 2018
| Dataset date: Oct 14, 2014
Cambodia administrative levels 0 (country), 1 (province / khaet and capital / reach thani), 2 (municipality, district), and 3 (commune / khum, quarter / sangkat) boundary polygon, line, and point shapefiles and KMZ files, and gazetteer
The datasets were obtained from the Department of Geography of the Ministry of Land Management, Urbanization and Construction in 2008 and unofficially updated in 2014 according to sub-decrees on administrative modifications. Data provided by WFP - VAM unit Cambodia.
These shapefiles are suitable for database or GIS linkage to the Cambodia administrative level 0-3 population statistics CSV tables. (However, six administrative level 3 records do not correspond to any of the level 3 shapefile features. These are listed in the caveats.)
Updated September 5, 2018
| Dataset date: Jan 1, 1950-Jan 1, 2010
[Source: United Nations Department of Economic and Social Affairs] Total Population - Both Sexes. De facto population in a country, area or region as of 1 July of the year indicated. Figures are presented in thousands.
This datasets are extracted from the government sources. Levels 1-4 (Province to village) of Central Sulawesi areas where hit by M 7.4 RS Earthquake on 28 September 2018.
Central Sulawesi administrative level 1 (province or special administrative area), 2 (Districts and Cities / Kabupaten and Kota), 3 ( Subdistricts / Kecamatan), and 4 (administrative Villages / Desa) boundary polygons.
Each original resource with Indonesian field names and codes is accompanied by a version that appends standard field names and P-codes.
Updated November 24, 2015
| Dataset date: Dec 31, 2011
Demography census data (2011) by district. For more information please refer to the United Nations Nepal Information portal.
The district profiles are contained in a ZIP file with one file per district.
Data is available for the following districts: Achham, Arghakhanchi, Baglung, Baitadi, Bajhang, Bajura, Banke, Bara, Bardiya, Bhaktapur, Bhojpur, Chitwan, Dadeldhura, Dailekh, Dang, Darchula, Dhading, Dhankuta, Dhanusha, Dolakha, Dolpa, Doti, Gorkha, Gulmi, Humla, Illam, Jajarkot, Jhapa, Jumla, Kailali, Kalikot, Kanchanpur, Kapilbastu, Kaski, Kathmandu, Kavre, Khotang, Lalitpur, Lamjung, Mahottari, Makawanpur, Manang, Morang, Mugu, Mustang, Myagdi, Nawalparasi, Nuwakot, Okhaldhunga, Palpa, Panchthar, Parbat, Parsa, Pyuthan, Ramechhap, Rasuwa, Rautahat, Rolpa, Rukum, Rupandehi, Salyan, Sankhuwasabha, Saptari, Sarlahi, Sindhuli, Sindhupalchowk, Siraha, Solukhumbu, Sunsari, Surkhet, Syangja, Taplejung, Tehrathum, Udayapur.
Updated September 29, 2018
| Dataset date: Aug 29, 2018
Number of confirmed, probable and suspected Ebola cases and deaths in the Equateur region Ebola outbreak in May 2018.in the Democratic Republic of the Congo (DRC) according to WHO Ebola situation reports. The figures are presented at national level as well as disaggregated to health zone level. Figures are cumulative.
On 1 August 2018, the Ministry of Health of the Democratic Republic of the Congo declared a new outbreak of Ebola virus disease in North Kivu Province. The data for this new outbreak is available here.
Updated September 26, 2018
| Dataset date: Oct 10, 2017
This dataset contains people in need and population by district ( admin2) and by governorate (admin1) disaggregated by sex and age. Also, it includes severity of needs per cluster and inter-cluster per district.
Newly displaced population due to conflict between 01 January 2018 and 09 October 2018, compiled by OCHA sub offices based on inter-agency assessment results. This data is a snapshot as of 14 October 2018 and the numbers are expected to change as new assessment figures become available.
Admin COD datasets for IRQ endorsed by IMWG and CO on October 2015; see metadata for description of cleaning and processing performed by ITOS. The dataset represents Admin Level 0 (International), Admin Level 1 (Governorate), Admin Level 2 (District).
Updated July 10, 2018
| Dataset date: Aug 4, 2017
Last update August 2017 (harmonization of codes following the IMWG meeting eg SN010101) according to the COD databases approved by the RO in January 2016; See metadata for description of ITOS cleaning and processing.
The dataset represents Admin level 0 (International), Admin Level 1 (Region), Admin Level 2 (Departement), Admin Level 3 (Arrondissement).
This dataset is the INDICATIVE boundaries of the Barangays as observed at the end of April 2016 as per the Philippine Geographic Standard Code (PSGC) dataset. It has been generated on the basis of the layer created by the Philippine Statistics Authority (PSA) in the context of the 2015 population census. LMB is still the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. These data cannot be used for commercial purposes.
Updated October 9, 2017
| Dataset date: Jan 1, 1950-Sep 30, 2017
The Oceanic Niño Index (ONI) has become the de facto standard that the National
Oceanic and Atmospheric Administration (NOAA) uses to identify El Niño (warm) and
La Niña (cool) events in the tropical Pacific. It is the three month mean SST
anomaly for the El Niño 3.4 region (i.e., 5°N-5°S, 120°-170°W). Events are defined as five
consecutive overlapping three month periods at or above the +0.5°C anomaly for warm (El
Niño), events and at or below the -0.5 anomaly for cold (La Niña) events. The threshold
is further broken down into Weak (with a 0.5 to 0.9 SST anomaly), Moderate (1.0 to 1.4)
and Strong (≥ 1.5) events. For an event to be categorized as weak, moderate or strong. it
must have equalled or exceeded the threshold for at least three consecutive overlapping
three month periods.