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.
Updated August 31, 2017
| 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
This assessment covers all locations hosting Rohingya population in Cox’s Bazar District in Bangladesh and present needs overview and population information as of 7 December. All information and findings are included in the attached products including the raw dataset for further reference and analysis.
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.
Updated December 14, 2017
| Dataset date: Nov 26, 2017
This dataset provides numbers of suspected cholera cases, deaths, case fatality rates (%) and attack rates (per 1,000) by Governorate in the Yemen cholera outbreak since 27 April 2017.
The data are manually extracted from the Yemen cholera outbreak epidemiology updates produced by WHO Yemen. The updates are posted on the Yemen Situation Reports page on the WHO Regional Office for the Eastern Mediterranean site. This dataset contains data from the daily and weekly updates up to the weekly report published on 26 November 2017.
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 layers are up-to-date as of the second quarter of 2016. 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
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.
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
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 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 April 6, 2017
| Dataset date: Jan 1, 2015-Dec 31, 2015
The overall INFORM risk index identifies countries at risk from humanitarian crises and disasters that could overwhelm national response capacity. It is made up of three dimensions - hazards and exposure, vulnerability and lack of coping capacity.
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 January 18, 2018
| Dataset date: Jan 18, 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.
Between 15th September and 15th October 2017 a team of 4 enumerators collected 1,360 interviews in Cox’s Bazar district about push factors and dynamics of the Rohingya exodus after August 25 , 2017. The data set includes detailed testimonies on the events and incidents/abuses they had either experienced personally or had witnessed before or during the journey to Bangladesh.
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