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.
Updated September 19, 2018
| Dataset date: Oct 17, 2017
The Admin boundary shapefiles for Rwanda since 2006. The shapefile was created in 2006 and updated by the 2012 Census mapping.
It was updated again on 17 Oct 2017.
19 October 2018 update to Administrative levels 0 - 3:
Standardized P-codes, based on the pre-existing numeric codes added
Lake Kivu extent removed
Corrections made to two administrative level 1 names in the administrative level 3 (sector) shapefile
(Administrative level 4 untouched)
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.
This dataset presents the result of the NPM Round 12 Baseline exercise, which collected information related to the Rohingya refugee population distribution and needs during the month of August 2018.
The data collection for NPM baseline survey was conducted between 9 August and 4 September 2018: it provides an update about the population distribution and movements.
The data collection for NPM Site Assessment survey will be conducted from 23 September 2018: in addition to an update about the population figures, this includeds a multi-sectoral needs assessment.
The full maps and GIS packages by camp produced based on NPM Baseline and Site Assessment 12 are available at the links below:
Please click here to access the data by camp as of August 2018.
Rohingya refugee population distribution by para in Teknaf upazila.
- Please click here.
NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights.
SW Map package: for mobile use, this enables users to visualize the site maps and boundaries on their own mobile. Together with the relevant files, users can also find a manual showing step by step how to copy files from their own computer to SW Map running on another portable device.
KMZ file: for desktop use, this enables users to visualize the site maps and boundaries on Google Earth. By adding or removing layers, it is possible to visualize each location assessed by NPM Baseline 10. These files are available on HDX.
Historical UAV imagery of Rohingya settlements in Cox Bazar in GIS, KML Google Earth, Mbtiles (SW Maps), format. Updates of imagery will be added on top of the list.
NPM has also produced individual packages by camps:
Please click here to access the data by camp as of July 2018.
Please click here to access the data by camp as of June 2018.
Please click here to access the data by camp as of May 2018.
Please click here to access the data by camp as of April 2018.
All majhee blocks shapefiles are also available at the following link:
Please click here to access the most current majhee block shapefiles, as well as all historical versions.
FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
This dataset lists project funding allocations from OCHA's Central Emergency Response Fund (CERF). CERF allocations are made to ensure a rapid response to sudden-onset emergencies or to rapidly deteriorating conditions in an existing emergency and to support humanitarian response activities within an underfunded emergency.
Updated September 19, 2018
| Dataset date: Aug 4, 2018-Sep 16, 2018
This Ebola epidemic dataset contains figures on the Ebola cases, deaths and cures in the North Kivu Ebola outbreak of August 2018 in the Democratic Republic of the Congo (DRC).
The data in the dataset is manually extracted from the Ebola epidemic situation reports issued by the DRC Ministry of Health.
The majhee block system represents an important aspect of communities within the Rohingya refugees settled in collective or camp-like settings in Cox's Bazar district. A majhee is a community leader belonging to the Rohingya refugee population, while a block is the area for which he is responsible.
The NPM majhee blocks mapping exercise was first conducted during NPM Baseline 9, between 5 and 20 February 2018. As part of the majhee interview process (key informants), enumerators walked the perimeter of each majhee block with guidance from the KI. As they walked, the field team traced their path, marking up the boundary on the tablet or paper map. Upon returning to NPM office, details of these boundaries were finalized on the paper maps.
The paper maps were then received by the NPM digitizing team. This team carefully digitized the Mahjee zone perimeters in GIS, using high-resolution NPM UAV imagery as an underlying reference. Boundaries were assigned the NPM Block_ID attribute, which represents a unique identifier for each Site Assessment Location. In this manner, boundaries can be uniquely linked back to a mahjee.
The majhee blocks mapping exercise is now embedded in NPM regular assessment activities (baseline and site assessment), hence regularly updated with a frequency of approximately three weeks.
The majhee block system is not an official form of governance. The scope of this exercise is purely descriptive and not prescriptive. Names and boundaries adopted in this exercise do not imply official endorsement or acceptance by IOM.
On behalf of the Global WASH Cluster, with funding provided by USAID and ECHO (September-December 2017) and UNICEF (January 2018 onwards), REACH initiated a rapid infrastructure mapping exercise in Rohingya refugee settlements in Cox's Bazar District.
Updated September 18, 2018
| Dataset date: Jun 30, 2018-Jul 21, 2018
Data collected in Bangladesh between June-July, 2018. Their analysis contributed to the Xchange Foundation's “The Rohingya Amongst Us”: Bangladeshi Perspectives on the Rohingya Crisis Survey.
The survey sample consisted of 1,697 Bangladeshi adults living in Teknaf (56%) and Ukhia (44%), the two southernmost subdistricts of Cox’s Bazar, and home to the majority of the Rohingya population. The survey was conducted in Bengali with the use of a questionnaire distributed through an online data collection application across more than 71 (up to 97) villages. Respondents were provided with anonymity and verbal consent was ensured before proceeding with each survey. The results of the survey are generalisable to the total adult Bangladeshi population residing in Ukhia and Teknaf upazilas (on a 95% confidence level, the margin of sampling error was 2.37).
To read the full report go to: http://xchange.org/bangladeshi-perspectives-on-the-rohingya-crisis-survey/
The Site Management sector, with the support of SM partners (IOM, UNHCR, ADRA and Solidarités International) conducted a mapping exercise during the months of April and May 2018 in the areas of Teknaf upazila currently hosting Rohingya refugees. The purpose of the exercise was to identify and map the boundaries of local paras, namely group of houses.
The mapping exercise aimed to better define the areas of responsibility of the newly established Para Development Commitees, and to support RRRC’s CiCs in the process of defining the new camp boundaries in Teknaf.
IOM Needs and Population Monitoring (NPM) provided technical support to better identify the para names and demarcation.
The para mapping exercise was conducted in two rounds. The first exercise took place from 1 to 3 of April 2018 in the areas of Alikhali, Leda, Nayapara, Muchoni, Jadimura and Domdumia. A second exercise took place on 21 and 22 April 2018 covering the areas of Hakimpara, Jamtoli, Putibonia, Shamlapur and Unchiprang.
This dataset presents the figures of the Rohingya refugee population in Teknaf, by para.
These data were collected during NPM's regular data collection exercises (Baseline and Site Assessment), which usually capture information on a majhee block level. The two systems are currently coexisting and overlapping. In order to better visualize how the two systems interact, NPM produced a set of maps available at this link.
Updated September 18, 2018
| Dataset date: Aug 31, 2018
Zimbabwe administrative level 0 (country), 1 (province), 2 (district) and 3 (ward) boundary polygon, line, and point shapefiles and KMZ files, and gazetteer.
Processed by ITOS 2018 09 17.
ITOS live service deployment anticipated shortly.
These shapefiles are suitable for linkage by P-code to the Zimbabwe administrative levels 0 - 2 population statistics CSV population statistics tables.
Updated September 18, 2018
| Dataset date: Aug 31, 2018
Zimbabwe administrative levels 0 (country), 1 (province), 2 (district) population statistics, source document, and levels 0 - 3 (ward) gazetteer
These CSV tables are suitable for linkage by P-code to the Zimbabwe administrative levels 0-3 boundaries shapefiles.
The “Who does What, Where” database, or 3W, is vital for efficient coordination. It maintains updated information on WHO (which organizations) are doing WHAT (which activities), WHERE (in which locations) to enable organizations and donors to improve the targeting of beneficiaries to ensure that humanitarian needs are met.
Update 15/09 (POST-EVENT)
Now that the typhoon has passed the country, the model is not run with forecasted wind speeds and typhoon track any more, but with actual estimated wind speeds and typhoon track. They come from the same source (Tropical Storm Risk - UCL), and are of the exact same format. All output (map in PDF, data in Excel and in Shapefile) is of the exact same format and interpretation.
1. Based on existing Priority Index model: 510 has previously developed the Priority Index model for typhoons in the Philippines
One day after a typhoon has passed the Philippines ..
.. the model predicts ‘% of completely damaged houses’ per municipality
Based on 12 large typhoons in the last 5 years in the Philippines, for which detailed damage reports were available through NDRRMC (https://www.ndrrmc.gov.ph/)
For these same events, we also collected possible explanatory indicators, such as wind speed (event-specific) and building materials of houses (PH national census).
We built a statistical model, which could explain differences in damage on the basis of differences in wind speed and building materials (etc.)
When dividing all municipalities in 5 equal damage classes (class 1 being the 20% municipalities with lowest damage; class 5 the 20% with highest damage) ..
.. we found that in 73% of the cases we are at most 1 class off.
2. Mangkhut methodology: In the case of typhoon Mangkhut, we are dealing with an upcoming typhoon, which is still awaiting landfall on Saturday 15/09. This is a new situation, which requires the following noteworthy changes in methodology.
Our wind speed source (UCL Tropical Storm Risk) has – in addition to post-event wind speed data as used above – also forecast wind speed data for 5 days ahead.
This forecasted wind speed (and typhoon track) are plugged as input into the above-mentioned prediction model, which - still in combination with building materials - lead to the predicted damage class per municipality that can be seen in the map.
Note that the results are strongly dependent on the input of windspeed, which is itself still an unknown. (see accuracy below).
3. How to use this product:
The map contains damage classes (1-5) per municipality. As such, we advise to put priority on municipalities in damage class 5, and depending on available resources continue with class 4, etc.
This damage class is based on ‘% of houses that are completely damaged’. As priority might also be based on exposure and vulnerability, we have added to the Excel a couple of relevant indicators, from the Community Risk Assessment dashboard.
PRC can decide if and how to combine these various features. If needed, 510 can be asked for assistance of course.
4. Important notes:
ACCURACY: it should be realized that during the course of the coming 3 days, the typhoon might change course, or increase/decrease in terms of strength. This will affect the quality of these predictions. The accuracy figure of 73% that is mentioned in the post-event case should be seen as an upper bound. Given the added inaccuracy of wind speed, the overall accuracy will be lower.
This damage prediction is only about completely damaged houses, not about partially damaged houses.
We only included municipalities that are within 100km of the forecasted typhoon track, as we have seen from previous typhoons (with comparable wind speeds) that damage figures outside of this area are low.
The wind speed is provided by Tropical Storm Risk (University College London). It is the ‘maximum 1-minute sustained wind speed’. An average of this is calculated per municipality. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time)
Typhoon track (from which ‘distance to typhoon track’ per municipality is calculated), is provided by UCL as well. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time)
Additionally, various wall and roof type categories from the Philippines national census. The model uses 2010 census data, as it was developed using this data (2015 census data on municipality level only became available in 2018). The 2015 census data could not be easily plugged in, because of some differences in roof/wall categories. We believe that this would not change the result much though, as even if there are large differences from 2010 to 2015, these would still be dominated by wind speed effects in the model.
All additional indicators, that are added to the Excel table (population, poverty) are derived from the Community Risk Assessment dashboard (Go to this link and click ‘Export to CSV’ on top-right.) The sources for these indicators can be found in the dashboard itself.
The dataset contains IDPs, returnees at sub national level. The dataset also has reason of displacement, origin and dates of multiple displacements.
The context of displacement in Mali remains complex and fluid. Movements of IDPs currently residing in the southern regions to the northern regions continue to be reported. While some have indicated that they have returned definitively, other IDPs say they travel back and forth between the place of travel and the place of origin. New displacements also continue to be reported, inter-community conflicts, insecurity and clashes, or the prospect of a possible clash between armed groups being among the reasons given for these new displacements.
In order to meet the needs of the internally displaced, repatriated and returned populations, the Population Movement Commission (CMP) collects and analyzes information on population movements within Mali, in order to provide a complete picture of population movements and at the request of its partners. The members of the Commission are: the General Directorate of Civil Protection (Ministry of Internal Security), UNHCR, OCHA, WFP, UNICEF, ACTED, NRC, DRC, Handicap International, Solidarity International, CRS, IOM, and DNDS. Several other entities regularly participate in Commission meetings.
Updated September 17, 2018
| Dataset date: Oct 12, 2017
Argentina administrative level 0 (country), 1 (province, national territory, or federal district; provincia, territorio nacional, o distrito federal), and 2 (department or part; departamento o partido) boundaries
Updated September 17, 2018
| Dataset date: Jan 1, 2011
Cambodia administrative levels 0 (country), 1 (province / khaet and capital / reach thani), 2 (municipality, district), and 3 (commune / khum, quarter / sangkat) population statistics with full sex and age disaggregation
These CSV tables are suitable for database or GIS linkage to the Cambodia administrative level 0-3 boundaries shapefiles.
NOTE caveats below.
September 17, 2018 update: Sex and age disaggregation data (computed from administrative level 1 data) included.