This dataset is the baseline survey prior to monthly Needs and Population Monitoring assessment Round 8. The baseline survey covers all locations hosting Rohingya population in Cox’s Bazar District in Bangladesh and records the number of Rohingya population by location.
Updated February 13, 2018
| Dataset date: Jun 1, 2017
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
The dataset contains the location (lat/lon), type, and estimated population of the Rohingya Refugees in and around Cox's Bazar, Bangladesh. The dataset is updated regularly. The source is the Inter-Sector Coordination Group in Cox's Bazar.
The Global Food Prices Database has data on food prices (e.g., beans, rice, fish, and sugar) for 76 countries and some 1,500 markets. The dataset includes around 500,000 records and is updated monthly. The data goes back as far as 1992 for a few countries, although most of the price trends start in 2000-2002.
These datasets are derived from the 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. These datasets have been vetted by staff at The Carl Vinson Institute of Government's Office of Information Technology Outreach Services (ITOS) according to their COD assessment protocol found in the COD Technical Support Package (https://sites.google.com/site/commonoperationaldataset/geodata-preparation-manual/itos-process).
Acknowledge PSA and NAMRIA as the sources. 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. The dataset can only be considered as indicative boundaries and not official.
* For administrative level 4 (Barangay) please contact the contributor (OCHA Philippines) via this page.
This COD replaces https://data.humdata.org/dataset/philippines-administrative-boundaries
Philippines administrative levels:
(1) Region (Filipino: rehiyon)
(2) Provinces (Filipino: lalawigan, probinsiya) and independent cities (Filipino: lungsod, siyudad/ciudad, dakbayan, lakanbalen)
(3) Municipalities (Filipino: bayan, balen, bungto, banwa, ili) and component cities (Filipino: lungsod, siyudad/ciudad, dakbayan, dakbanwa, lakanbalen)
These shapefiles are suitable for database or ArcGIS joins to the sex and age disaggregated population statistics found on HDX here.
Updated March 28, 2016
| Dataset date: Aug 29, 2014-Mar 23, 2016
Total number of probable, confirmed and suspected Ebola cases and deaths in Guinea, Liberia, Sierra Leone, Nigeria, Senegal, Mali, Spain USA, UK and Italy according to Ebola Data and Statistics.
The resources contain two references to Liberia: Liberia and Liberia 2. Liberia contain reported before May 9, 2015. Liberia 2 refers to cases reported after May 9, 2015.
The resources contain two references to Guinea: Guinea and Guinea 2. Guinea contain reported before May 9, 2015. Guinea 2 refers to cases reported after March 23, 2016.
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.
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 20, 2017
| Dataset date: Jan 1, 2015
This is the full Administrative dataset for Bangladesh. It includes administrative level 0 (nation), 1 (division), 2 (district), 3 (upazila), and 4 (union) polygons and lines. All units have two PCODES. For each administrative level 'X', the 'admXcode_old' is the original 'GEOCODE11' PCODE and the one most commonly used in country. The GEOCODE15 incorporates Mymensingh Division which was created in 2015. Also note that administrative level 4 (union) is available in two versions. One includes inland water areas, the other includes only land areas.
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.
Updated February 12, 2018
| Dataset date: Feb 5, 2018
Tonga administrative level 0 (country), 1 (division), 2 (district), and 3 (village) boundary polygons
These boundary files are suitable for GIS or database joins to the Tonga administrative level 0, 1, 2, and 3 population statistics
United Nations Development Programme - November 24, 2015
[Source: United Nations Development Programme] Average number of years of education received by people ages 25 and older, converted from education attainment levels using official durations of each level.
Updated February 24, 2018
| Dataset date: Feb 24, 2018
This dataset contains key figures (topline numbers) on the world's pressing humanitarian crisis as shown on ReliefWeb's Crises app. The data includes key figures such as the number of affected population and funding status. The data are curated by ReliefWeb's editorial team based on their relevance to the humanitarian community. Descriptions of the files and columns within the files are included in the Additional Metadata.xlsx file.
Ukraine administrative level 0 (country), 2 (raion), 3 (council and community), and 4 (settlement) boundary polygons, lines, and representative points, in geodatabase, shapefile, and live service formats, and tabular data in .xlsx format. The data is in geographic coordinate system WGS84
The flood layer prepared by IOM and UNHCR, combined a mix method:
High quality drone imagery from December 2017, was analysed to verify non-usable locations and to determine the extent of their boundaries. A buffer of approximately 2-3 m was added to the outlines of these areas to accommodate the differing conditions during the rainy season. These outlines were then validated onsite and where needed, adjusted to reflect a more realistic flooding scenario, taking inputs from local residents as needed. The onsite verification revealed that in most instances, the assumptions were either accurate or slightly underestimated thus requiring only minor expansion of the boundaries in some cases, but no reductions were made.
The main river flood levels, with an enormous and complicated catchment area, was calculated empirically, based upon field measurements taken of high water levels as indicated from people who have lived in the area for over 20 years. The river was surveyed, both in section and longitudinally, and the volume of water was back calculated based on the river arrangement where the high water level was known. For small tributaries, with well-defined and small catchment areas, rainfall intensity data (from International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May 2015) for Chittagong, with a 10 year return period was used to calculate the volume of water. The height of the flood waters for the smaller tributaries was based on the channel sections, the slope and volume of water calculated from the rainfall intensity.
Landslide Layer prepared ADPC, UNHCR and IOM:
The impact area is calculated based on the fact that slopes of more than 35 degrees have a risk of failure. The slope was calculated based on the DEM dataset at 0.5 meter spatial resolution, gathered by IOM drone imagery. The DEM was adjusted match a geographical point of reference, and trees and buildings removed. The additional area of susceptibility of landslide was extended by manually drawing polygons by UNHCR and ADPC, with the support of DEM topography and contour lines. These polygons are extensions of 40 degrees and above until reaching the base of the respective slope. Spatial analysis was carried out in order to provide the statistical results of the population at risk.
Risk Management Criteria and assumptions made:
• The crucial landslide trigger factor is pore pressure
• The land slide failure would be sudden
• When it does fail, it will have an aspect ratio of 1 to 1
• 35 degrees slope and above, a risk of failure
• 40 degree slope and above has a 50% chance of failure
• 45 degree slope and above 85% chance of failure
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 November 25, 2015
| Dataset date: Jan 1, 2012
Polling station locations
Abstract: This data comes from http://vote.iebc.or.ke
Use the _sphericalmercator versions with TileMill, will be much faster. Otherwise, you're probably fine with the unprojected versions.
There are simple endpoints for requesting json encoded data. download.py iterates, caches, and builds the output
Instructions: Polling station location Shapefile can be downloaded from https://github.com/mikelmaron/kenya-election-data/tree/master/output
Updated December 19, 2017
| 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.
Alhasan Systems Private Limited is proudly sharing with you Pakistan's complete Union Council Boundaries on the 70th Independence Day of Pakistan.
This data will remain the only public version of UCs boundaries for all Pakistan citizens and organization both public and private and researchers around the world until a more authentic version become available from the concerned institution.
Dear GIS/ IM professionals. This UC boundary is compiled from data mining of public sources, and you are encouraged to use it in your projects. We request you to bring enhancement to this dataset and make them public on HDX as a newer version for the larger benefit.