Data Grid Completeness
11/27 Core Data 36 Datasets 14 Organisations Show legend
What is Data Grid Completeness?
Data Grid Completeness defines a set of core data that are essential for preparedness and emergency response. For select countries, the HDX Team and trusted partners evaluate datasets available on HDX and add those meeting the definition of a core data category to the Data Grid Completeness board above. Please help us improve this feature by sending your feedback to hdx@un.org.
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Affected People
8 Datasets
Internally-Displaced Persons
International Organization for Migration
Returnees
International Organization for Migration
Humanitarian Profile Locations
International Organization for Migration
Humanitarian Needs
Casualties
Armed Conflict Location & Event Data Project (ACLED)
Coordination & Context
11 Datasets
Food Security & Nutrition
3 Datasets
Food security
Integrated Food Security Phase Classification (IPC)
Global Acute Malnutrition Rate
Severe Acute Malnutrition Rate
Food Prices
WFP - World Food Programme
Geography & Infrastructure
8 Datasets
Administrative Divisions
Populated Places
Humanitarian OpenStreetMap Team (HOT)
Roads
Humanitarian OpenStreetMap Team (HOT)
OCHA Sudan
Airports
Humanitarian OpenStreetMap Team (HOT)
Health & Education
6 Datasets
Health Facilities
Global Healthsites Mapping Project
Humanitarian OpenStreetMap Team (HOT)
Affected Schools
Population & Socio-economy
3 Datasets
Baseline Population by Age & Sex
Poverty Rate
Oxford Poverty & Human Development Initiative
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  • 50+ Downloads
    Updated April 29, 2020 | Dataset date: Jan 1, 1990-Dec 31, 2017
    This dataset updates: Every year
    The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.
  • 60+ Downloads
    Updated April 21, 2020 | Dataset date: Jan 1, 2019-Dec 31, 2019
    This dataset updates: As needed
    This page includes information on violent and threatening incidents affecting aid operations, civilians, education, health care, refugees and IDPs in the Sudan to ensure staff safety and better response outcomes.
  • 60+ Downloads
    Updated April 9, 2020 | Dataset date: Feb 29, 2020
    This dataset updates: Every year
    Refugee population of Sudan by UNHCR
  • 30+ Downloads
    Updated April 9, 2020 | Dataset date: Jan 31, 2020
    This dataset updates: Every year
    Returnee population of Sudan through DTM - IOM
  • 40+ Downloads
    Updated April 9, 2020 | Dataset date: Feb 29, 2020
    This dataset updates: Every year
    IDP population in Sudan through Displacement Tracking Matrix by IOM
  • 40+ Downloads
    Updated April 9, 2020 | Dataset date: Jun 30, 2019
    This dataset updates: As needed
    Health Facilities locations across Darfur (Sudan)
  • 20+ Downloads
    Updated April 9, 2020 | Dataset date: Mar 30, 2020
    This dataset updates: As needed
    UNHAS air field locations
  • 30+ Downloads
    Updated April 9, 2020 | Dataset date: Mar 30, 2020
    This dataset updates: As needed
    Shape file for Bording crossing points
  • 70+ Downloads
    Updated April 4, 2020 | Dataset date: Jan 1, 2018-Dec 31, 2018
    This dataset updates: Never
    The data set contains the beneficiaries reached for the period January - December 2018 by state.
  • 100+ Downloads
    Updated April 2, 2020 | Dataset date: Jan 1, 2019-Dec 31, 2019
    This dataset updates: Every year
    This dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners in Sudan. This dataset contains the total number of people reached per locality and per sector in Sudan for 2019 Humanitarian Response Plan. The original data is available on https://hpc.tools.
  • 100+ Downloads
    Updated March 30, 2020 | Dataset date: Feb 20, 2020
    This dataset updates: Every year
    Camps in Sudan for IDP - IOM
  • 30+ Downloads
    Updated March 30, 2020 | Dataset date: Feb 20, 2020
    This dataset updates: Every year
    IOM through DTM registration of IDPs in Sudan
  • 40+ Downloads
    Updated March 30, 2020 | Dataset date: Mar 30, 2020
    This dataset updates: Every year
    Sudan IDP Settlements by Risk level data by locality and state wise.
  • 200+ Downloads
    Updated March 20, 2020 | Dataset date: Jan 1, 1990-Dec 31, 1990
    This dataset updates: Every year
    Contains data from the DHS data portal. There is also a dataset containing Sudan - National Demographic and Health Data on HDX. The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
  • 400+ Downloads
    Updated March 20, 2020 | Dataset date: Jan 1, 1990-Dec 31, 1990
    This dataset updates: Every year
    Contains data from the DHS data portal. There is also a dataset containing Sudan - Subnational Demographic and Health Data on HDX. The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
  • 200+ Downloads
    Updated March 16, 2020 | Dataset date: Dec 31, 2018
    This dataset updates: As needed
    Methodology The survey used the Simple Spatial Survey Method (S3M), an area-based sampling methodology that uses settlement locations for sample selection. The survey was designed to be spatially representative of the whole country its smaller administrative units up to the locality level with the exception of few inaccessible areas . An even distribution of primary sampling units (PSUs) (i.e., villages/city blocks) was selected from across the country. This approach was used as it is most suited to assessing indicators over wide areas to detect and map heterogeneity of indicators which is the primary objectives of this national survey (Gilbert 1987; Elliot et al. 2000; Pfeiffer 2008). PSUs (i.e. villages/city blocks) were selected based on their proximity to centroids of a hexagonal grid laid over the entire country. The resulting sample is a triangular irregular network (Pfeiffer 2008; E. H. Isaaks and Srivastava 1989). A variable density sampling approach (E. H. Isaaks and Srivastava 1989) was used to achieve a sample that draws a minimum number of PSUs from localities and from urban areas so that they can provide estimates for each of these areas with useful precision. A sample of up to n = 32 mother and child pairs in m = 3027 PSUs was taken (see Figure 2.1). Across Sudan, a total of 93,882 households and 145,002 children below 5 years of age were surveyed. Preparations, data collection and analysis: Planning of the S3M II survey and in particular, the timeframe of activities, was based on the previous experience of undertaking S3M-I but also influenced the inputs of stakeholders at federal and state level, particularly members of the S3M-II technical committee at federal level, which included WHO, WFP, the Ministry of Education (MOE), the Ministry of Security and Social Welfare (MOSSW) and the Ministry of Agriculture (food security directorate), besides the Ministry of Health and UNICEF. FMoH and UNICEF, through the S3M II technical committee, regularly engaged with key stakeholders (one to two times per week during the planning phase) and ensured their involvement in the analysis stage. All technical committee members were invited to participate in the data analysis workshops. Other stakeholders - like the Central Bureau of Statistics (CBS) were consulted and informed of the progress achieved during each of the key stages (preparation, data collection and data analysis) while stakeholders such as donors were informed of progress. In addition, in every state, state level technical committees were formed and functioned to support the planning, data collection and analysis processes. The Ethical approval request was prepared by the FMOH and submitted to the ethics committee at the research department at the FMOH. Ethical approval was granted for both S3M-II and the nested micronutrients survey from FMOH. All other relevant approvals related to undertaking the survey were obtained including visas for international consultants supporting the survey, travel permits etc. Detailed maps for all states were obtained through a successful partnership between UNICEF, FMOH and CBS. A joint workshop was undertaken in May 2018 followed by field visits in June 2018 to verify all coordinates was conducted (workshop hosted at the CBS). This was followed with a joint field visits to obtain and physically collect missing coordinates. Through UNICEF Valid International with Brixton Health were contracted and the organisations worked closely with FMOH and UNICEF throughout the survey as planned. It should be noted that the individuals included in the institutional contract were the experts who developed and later refined the S3M methodology as well as provided support to the wider Sudan team with the undertaking of the S3M-I survey in 2013. Initial list of the indicators to be included in the survey was developed through wide consultation with various sectors and stakeholders including government line ministries (MoH, MoE, MSSW, MoA) and UN agencies (UNICEF, WHO and WFP) through the S3M technical committee. The proposed list was further refined and revised by an external firm (Valid international organization) who also provided technical support throughout the survey. 226 indicators were collected and reported (see annex 1 for details). Survey leads from UNICEF and MoH were identified and they further identified 9 UNICEF, 20 FMoH and 18 SMoH staff to supervise data collection and management. During Gezira pilot, the supervisors were trained to serve as data collectors to sharpen their skills to lead the data collection process in their respective states.. As a result, there were nine UNICEF, 20 Federal and 18 state level supervisors in addition to four supervisors from WHO who participated to oversee the collection of the micronutrient related data, as did their counterparts from the FMOH. Finally, a third-party ICT company was contracted by the FMOH to develop data collection digital tools and to provide ICT technical support and troubleshooting with regard to tablets and software issues, including presence of one ICT person in each state throughout the data collection. Sampling was carried out from 11 to 23 July 2018 by UNICEF and Ministry of Health staff in accordance with the S3M sampling methodology. This was based on the mapping of settlements across the entire country in villages and city blocks, carried out just prior to this (in 2.3.3 above), resulting in the assignment of correct GPS coordinates for more than 25,000 villages across the country. Based on the mapping of settlements, distribution of primary sampling units was selected for each locality. This was done based on random sample selection using a sampling software designed to undertake S3M variable density sampling. The approach and selection were approved for their rigor and appropriateness by the external technical experts from Valid International. From the selected villages, consultations with state authorities including State Ministries of Health (SMOH), Humanitarian Aid Commission (HAC) and the National Intelligence and Security Service (NISS) were held to ensure the accessibility and security of villages for the survey teams and in the case of inaccessible villages, a replacement was made using the same sampling software. Further selection of households was done during the actual survey data collection; under the oversight of the external technical experts. Master training was carried-out in Gezira state (pilot state) in July-August 2018 to test tools including digital data collection tools, laboratory testing for micronutrients indicators and logistics and to train survey supervisors (through a Training of Trainers). Upon the identification of staff, all supervisors including the nine UNICEF supervisors, the four WHO supervisors, the 18 state supervisors and the 20 FMOH supervisors (including nine from Gezira state) received training from July 16 to July 25 on the following topics:  S3M-II methodology.  S3M-II questionnaire.  Digital data collection tools (tablets).  Micronutrients samples collection and storage.  TORs of all personnel and groups.  Anthropometric measurements standardisation.  S3M-II monitoring tools.  S3M-II logistic, data collection plans and quality assurance.  Local calendar.  S3M-II sampling including urban sampling. In addition, 18 state nutrition directors received basic training on the S3M-II methodology, indicators, and the roles and responsibilities for committees involved. Further training for the supervisors for the micronutrient survey were done separately. This was followed by data collection in Gezira state which was carried-out by supervisors. Subsequent state level trainings were conducted prior to data collection for each state. Data from phase one states (North Darfur, East Darfur, West Kordofan, River Nile, Sennar, South Darfur, North Kordofan, Khartoum and Northern states) was collected in October 2018. Data from phase II states (White Nile, Kassala, Blue Nile, Central Darfur and West Darfur, Red Sea, South Kordofan and Gedaref states) was collected from November 2019 to January 2019. Close monitoring, supportive supervision and capacity-building to the Ministry of Health staff continued throughout the first phase. Technical assistance was provided from UNICEF S3M-II technical staff and Valid International throughout data collection.
  • 20+ Downloads
    Updated March 16, 2020 | Dataset date: Sep 30, 2019
    This dataset updates: As needed
    This data-set presents the US$ value of annual imports of medicines from 2013 published by the Central Bank of Sudan on a quarterly basis on its website www.cbos.gov.sd
  • 100+ Downloads
    Updated March 16, 2020 | Dataset date: Sep 30, 2019
    This dataset updates: As needed
    Rainfall distribution in Sudan for a 2010-2019. The data-set was created using observed data from the Sudan Meteorological Authority. All measurements in millimeters.
  • 100+ Downloads
    Updated March 12, 2020 | Dataset date: Dec 31, 2019
    This dataset updates: Every year
    The number of infectious disease outbreaks as reported by Sudan’s Ministry of Health in 2019. Outbreaks reported include Cholera, Dengue Fever, Diphtheria, Rift Valley Fever and Chikugunya.
  • 100+ Downloads
    Updated March 10, 2020 | Dataset date: Mar 23, 2015
    This dataset updates: Never
    The INFORM Greater Horn of Africa model is part of an initiative of Intergovernmental Authority on Development (IGAD) and OCHA to improve IGAD’s ability to analyse, visualise and disseminate information to support the prevention, preparedness and response to humanitarian crises in the region. The model will be updated regularly to support regional coordination and prioritise humanitarian, development, risk management and resilience investments.
  • 40+ Downloads
    Updated March 10, 2020 | Dataset date: Mar 10, 2020
    This dataset updates: As needed
    The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the United Nations. This file is for planning purpose only.
  • 30+ Downloads
    Updated March 10, 2020 | Dataset date: Mar 10, 2020
    This dataset updates: As needed
    The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the United Nations. This file is for planning purpose only.
  • 60+ Downloads
    Updated March 10, 2020 | Dataset date: Mar 10, 2020
    This dataset updates: As needed
    The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the United Nations. This file is for planning purpose only.
  • 70+ Downloads
    Updated March 10, 2020 | Dataset date: Mar 10, 2020
    This dataset updates: As needed
    The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the United Nations. This file is for planning purpose only.
  • 30+ Downloads
    Updated March 10, 2020 | Dataset date: Mar 10, 2020
    This dataset updates: As needed
    The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the United Nations. This file is for planning purpose only.