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.
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
[Source: World Bank] Gini index measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution.
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.
Some APIs (e.g. http://openweathermap.org/current) don't have search by countryname, but do have search for data within a geographical bounding box. This list should help in grabbing country-specific data from those APIs.
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
The UNHCR Population Statistics database currently contains data about UNHCR's populations of concern from the year 2000 up to 2013. The data is the same available in UNHCR's Yearbook Statistical Yearbook publications.
In this dataset it is possible to investigate different aspects of the populations of concern: their general composition by location of residence or origin, their status (refugees, asylum seekers, internally displaced persons, etc.), their evolution over time, and so on.
This dataset includes facility exposure to flood and landslide hazards for camps south of Kutupalong (Camps 14, 15, 16, Chakmarkul, Jadimura, Unchiprang, Leda, & Neyapara). Data is from REACH's Round 4 infrastructure mapping . For further information regarding flood and landslide methodology please refer to the metadata caveats section. For access to media hyperlink files please contact firstname.lastname@example.org or the ISCG in CXB.
Following an outbreak of violence on 25 August 2017 in Rakhine State, Myanmar, a new massive influx of Rohingya refugees to Cox’s Bazar, Bangladesh started in late August 2017. Most of the Rohingya refugees settled in Ukhia and Teknaf Upazilas of Cox’s Bazar, a district bordering Myanmar identified as the main entry area for border crossings. Full report available on humanitarianresponse.info.
Data as of June 11, 2015. The "Syria Refugee Sites" dataset contains verified data about the geographic location (point geometry), name, and operational status of refugee sites hosting Syrian refugees in Turkey, Jordan, and Iraq. Only refugee sites operated by the United Nations High Commissioner for Refugees (UNHCR) or the Government of Turkey are included. Compiled by the U.S Department of State, Humanitarian Information Unit (INR/GGI/HIU), each attribute in the dataset (including name, location, and status) is verified against multiple sources. The name and status are obtained from the UNHCR data portal (accessible at http://data.unhcr.org/syrianrefugees/regional.php). The locations are obtained from the U.S. Department of State, Bureau of Population, Refugees, and Migration (PRM) and the National Geospatial-Intelligence Agency's GEOnet Names Server (GNS) (accessible at http://geonames.nga.mil/ggmagaz/). The name and status for each refugee site is verified with PRM. Locations are verified using high-resolution commercial satellite imagery and/or known areas of population. Additionally, all data is checked against various news sources. Locations are only accurate down to the city level. The designation field gives the type of site and the status of the site. Sites can be "Official Camps", camp settlements that are officially established and maintained by the United Nations or host country. Sites can also be "Transitional Camps", which is a typical camp structure but designed to be temporary or used on as needed basis. There can also be "Transitional Facilities"; these are facilities that are being used to temporarily house refugees. Status of these sites can be Planned, Under Construction, Staged, Open, or Closed. The data contained herein is entirely unclassified and is current as of 11 June 2015. The data is updated as needed.
In response to the need for accurate information on internally displaced persons (IDPs) in Nigeria, the International Organization for Migration (IOM) began implementing the Displacement Tracking Matrix (DTM) project in July 2014. The project is supporting the Government of Nigeria and other humanitarian response partners to conduct IDPs assessments in a systematic way as well as to establish a profile of the IDP population.
This dataset comprises of 644 facilities that were classified as not exposed to a flood or landslide hazard within the 21 Kutupalong Refugee Camps to assess which facilities would be optimal for further shelter upgrades and reinforcement. An index was created for prioritization and of these 644 sites, 224 were identified as having optimal indicators for further site visits. Corresponding maps for these 224 sites can be found on the REACH Resource Centre or ReliefWeb. It should be noted that ALL 644 facilities not exposed to a flood or landslide hazard should be explored as viable options for awareness raising to the local Camp/Majhee populations. For further information regarding the indicators used for the analysis please see the caveats section below.
Updated November 24, 2015
| Dataset date: May 21, 2015
Total rainfall distribution in Nepal for a year and the months of April through October. The dataset was created in 2003 using observed data from about 200 stations over a 20 year period (1980-2000). All measurements in millimeters (mm).
Updated December 13, 2017
| Dataset date: Nov 1, 2015
Administrative boundary datasets for levels 0, 1 and 2 (international, governorate, and district) for Yemen approved for use by OCHA Yemen Country Office in November 2015.
Admin Level 1= Governorate = Mohafaza
Admin Level 2 = District = Modeeriyyah
PCODES are those used by Yemen Central Statistical Office. "YE" is added as a prefix for the codes.
The process of clearing Admin 3 (sub-district) has not been finished and this level is not included. Additionally, the other layers (0, 1, and 2) may undergo minor modification in the coming months.
Admin Level 0, 1 (Region) and 2 (District) Boundaries of Ghana
The dataset represents the International, Regions and Districts boundaries of Ghana with harmonized PCODES of ROWCA and Humanitarian Response.
This dataset contains official statistics on crime at national and regional levels. Included in the dataset are statistics on crimes such assault, kidnapping, theft, robbery, burglary, motor vehicle theft and sexual violence.
Updated July 6, 2017
| Dataset date: Dec 31, 2012
Zambia Administrative Boundaries Level 1 (Provinces) and Level 2 (Districts) joined to Census 2010 population .
District shapefile downloaded from ISCGM in 2012. Province shapefile merged from District shapefile by UN OCHA ROSEA.
Final Census 2010 population from the Zambia Central Statistics Office (CSO) report joined to shapefiles by UN OCHA ROSEA.
Codes for new Muchinga Province and Ikelenga District added by UN OCHA ROSEA.
HASC Codes added from Statoids website (http://www.statoids.com/yzm.html)