These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
refugee = 'yes' AND boundary = 'refugee_camp' OR boundary = 'administrative' OR landuse = 'residential'
Features may have these attributes:
name:en
landuse
start_date
boundary
name
access
official_name
operator
loc_name
place
operator:type
type
admin_level
capacity
refugee
population
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity = 'mobile_money_agent'
Features may have these attributes:
addr:place
start_date
addr:subcounty
addr:village
amenity
name
addr:point
addr:zone
opening_hours
addr:block
network
addr:district
addr:parish
addr:settlement
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity = 'marketplace'
Features may have these attributes:
addr:place
start_date
addr:subcounty
name
amenity
opening_hours
addr:block
addr:district
addr:parish
addr:settlement
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity = 'fuel'
Features may have these attributes:
addr:place
fuel
addr:subcounty
name
amenity
opening_hours
addr:block
operator
addr:district
addr:parish
addr:settlement
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity IN ('water_point','drinking_water') OR emergency = 'water_tank' OR natural = 'spring'
Features may have these attributes:
addr:subcounty
addr:village
amenity
addr:point
fee
addr:settlement
lit
natural
name
addr:block
addr:county
addr:district
pump
addr:parish
addr:place
start_date
water
addr:zone
man_made
water_source
operational_status
emergency
operator
operator:type
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)
-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding
Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area.
These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding
Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel,
adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area.
These are produced using the unconstrained top-down modelling method.
Data for earlier dates is available directly from WorldPop.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674
This data and report examine perceptions and the impact of COVID-19 in 12 countries throughout sub-Saharan Africa. Topics covered include greatest concerns surrounding coronavirus, preventative measures being taken, changes in food market operability and food security, consumer behavior changes, and trust in governments to prevent the spread of coronavirus. This dataset includes data from 10 of the markets. Please contact us for access to data from all markets, the questionnaire, and with any other questions.
The South Sudan situation is currently the largest refugee situation on the African continent. There are over 2.2 million refugees spread across Sudan, Uganda, Ethiopia, Kenya, the Democratic Republic of the Congo (DRC), and the Central African Republic (CAR) a further 1.8 million people are displaced internally in South Sudan. An estimated 140,000 South Sudanese spontaneously returnees are reported to have returned to South Sudan from November 2018 to date.
The South Sudan situation continues to be characterized as a children's tragedy with children constituting over 65 percent of the refugee population. The Revitalised Agreement for the Resolution of the Conflict in the Republic in South Sudan (R-ARCSS) foresees the formation of a Government of National Unity (GNU) with all the parties in agreement including the leader of the SPLA IO and first vice president by May 2019.
In November 2018, it was agreed during the Kampala Representatives meeting that intention surveys should be conducted for South Sudanese refugees in all countries of asylum. This was further concretized in March 2019, during the EHA/GLR planning meeting; here it was decided that UNHCR country representations of CAR, Kenya, Uganda, DRC, Sudan, Ethiopia would ensure that a rapid intention survey of South Sudanese refugees in their respective asylum countries is carried out before May 2019, in line with the agreed calendar of the R-ARCSS
The Intention Survey was a cross-sectional survey conducted among the over 2.2 million South Sudanese refugees living in six countries of asylum using a stratified random sampling approach to survey 6,964 refugees (heads of households) in 15 camps selected across the region. In each location, sample size estimation assumed a 95 per cent confidence level, and a margin of error of 7 per cent; sample was drawn taking into account the location, place of origin, ethnicity, year of arrival to the country of asylum and gender of the head of household. The confidence intervals were taken into consideration in all the tables and analysis. Security, access and logistical constraints restricted sampling in some locations, therefore weighting was applied to adjust for unequal selection probabilities in each of the 15 locations. The findings of this report are representative of the return intentions of refugee households in these 15 camps. Data was collected through in-person interviews using a harmonised survey that was conducted concurrently in the six countries in May 2019 with a mobile data collection tool (KoBo Toolkit). Questionnaires were administered to consenting refugees aged 12 years and above. Children below 12 years of age were excluded from the survey.
Uganda is hosting over 1 million refugees with about 114,716 (OPM Nov 2019) of them settled in Kyangwali refugee settlement. This rapid influx of refugees has put pressure on basic social services including education, food, shelter and WASH infrastructure. In order to efficiently and effectively improve WASH service delivery in the settlement, there is need for accurate and reliable information on which to base programmatic decisions.
Kyangwali settlement has had a number of interventions by different partners, and in as much as there were access indicators obtained regularly by the partners that provide extremely useful average figures at settlement level, there has been a gap in the in-depth understanding of the situation at household level and to account for disparities within the settlement so as to measure the impact of the interventions.
The survey mainly utilized 2 methods: Household questionnaire survey and documentary review. The survey covered all the five zones of the settlement, with samples drawn from all the villages in the different zones. Sample sizes for each zone were calculated using the UNHCR sample size determination tool. A sample of 403 (only refugees) was interviewed using household questionnaire survey administered through Kobo collect and Open Data Kit (ODK) tool. Reviewed documents included: partners periodic updates, minutes of WASH meetings.
The Database and dashboard covers the regional humanitarian situation including targets, results and funding all as of December 2019 for entire Eastern and Southern Africa. Annual 2019 Database.
In late December 2017, the northeaster Ituri province of DRC experienced inter-ethnic violence which resulted in displacement of tens of thousands of civilians crossing the border to Uganda. Close to 60,000 refugees arrived in the Kyangwali settlement in a few months’ time, creating a humanitarian emergency which was aggravated by the outbreak of cholera. This called for a number of WASH agencies to begin operating in the settlement in response to the emergency with the objective to improve access to potable water supply and improved hygiene and sanitation facilities. The WASH forum decided to conduct a KAP survey to gauge the level of WASH services against acceptable standards and assess existing gaps to facilitate evidence based planning of future programs. The survey includes 384 refugee households in the Kyangwali refugee settlement.
The WASH KAP Uganda Palorinya survey 2018 was implemented by NRC to provide a benchmark on the status of the WASH situation of Refugees in Zone 3 of the settlement. This part had an estimated 69000 people, and for this part, NRC was selected as UNHCR's implementing partner for WASH activities. The sample includes 402 households, randomly selected from the west and east of Zone 3.
These datasets contain OpenStreetMap data related to the Refugee Response in northern Uganda. Data model coordinated with UNHCR. The source is surveys and mapping in northern Uganda performed by HOTOSM and partners.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
railway IN ('rail','subway','station')
Features may have these attributes:
operator:type
name
railway
addr:city
addr:full
layer
source
ele
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D’Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra
Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again.
Resolution: geotiff is provided at Bing Tile Level 20
The AFDB Statistical Data Portal has been developed in response to the increasing demand for statistical data and indicators relating to African Countries. The Portal provides multiple customized tools to gather indicators, analyze them, and export them into multiple formats.
With the Data Portal, you can visualize Socio-Economic indicators over a period of time, gain access to presentation-ready graphics and perform comprehensive analysis on a Country and Regional level
These are point locations of Gas/Petro Stations,Power Stations,Substations and Transformers,which were mapped by Uganda Bureau of Statistics between 2009 to 2012 under the National Census Mapping Programme.
More detail about this dataset and more downloadable formats can be found here: http://ubos.geo-solutions.it/layers/geonode:energy_layer
These are commercial centres of Uganda which include markets (daily,Weekly & Monthly types),Trading Centres and major shops.The layer was produced by UBOS between 2009 to 2012 under the National Census Mapping Programme.
More detail about this dataset and more downloadable formats can be found here: http://ubos.geo-solutions.it/layers/geonode:commercial_facilities
The Database and dashboard covers the regional humanitarian situation including targets, results and funding all as of December 18 2018 for entire Southern and Eastern Africa.
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application.
Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.
A description of the modelling methods used for age and sex structures can be found in
Tatem et al and
Pezzulo et al. Details of the input population count datasets used can be found here, and age/sex structure proportion datasets here.
Both top-down 'unconstrained' and 'constrained' versions of the datasets are available, and the differences between the two methods are outlined
here. The datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The unconstrained datasets are available for each year from 2000 to 2020.
The constrained datasets are only available for 2020 at present, given the time periods represented by the building footprint and built settlement datasets used in the mapping.
Data for earlier dates is available directly from WorldPop.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646
