Key Figures
Data Completeness
2/26 Core Data 12 Datasets 10 Organisations Show legend
What is Data Completeness?
Data 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 Completeness board above. Please help us improve this feature by sending your feedback to hdx@un.org.
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Affected People
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Coordination & Context
4 Datasets
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1 Datasets
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3 Datasets
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  • Updated May 28, 2019 | Dataset date: Jan 1, 2019-Dec 31, 2019
    This dataset updates: Every year
    Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map population age and sex counts for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets. 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).
  • Updated May 28, 2019 | Dataset date: Jan 1, 2014-Dec 31, 2018
    This dataset updates: Every year
    The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of pregnancies to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age, age-specific fertility rates, still births and abortions to map the estimated distributions of pregnancies for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al and James et al.. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Myanmar 1km Pregnancies. Version 2.0 2015 estimates of numbers of pregnancies per grid square, with national totals adjusted to match national estimates on numbers of pregnancies made by the Guttmacher Institute (http://www.guttmacher.org) DOI: 10.5258/SOTON/WP00620
  • Updated May 28, 2019 | Dataset date: Jan 1, 2014-Dec 31, 2018
    This dataset updates: Every year
    The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al. and James et al.. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Myanmar 1km Births. Version 2.0 2015 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/). DOI: 10.5258/SOTON/WP00569
  • 80+ Downloads
    Updated May 28, 2019 | Dataset date: Jan 1, 2000-Dec 31, 2020
    This dataset updates: Every year
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets 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/WP00645
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Aug 3, 2016
    This dataset updates: Never
    Floods in Rakhine State, Myanmar-Situation Analysis Preliminary Report
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Jul 15, 2016
    This dataset updates: Never
    This map illustrates satellite-detected flood waters in the northwestern part of Rakhine State in the townships of Kyauktaw, Mrauk-U and Ponnagyun, Myanmar as imaged by the SENTINEL-1 satellite on 14 July 2016. Heavy rains at the onset of the monsoon season have caused flooding. The most affected lands seem to be mainly agricultural and/or paddy fields, many of which are of course frequently inundated at other times as well. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Sep 19, 2017
    This dataset updates: Live
    This map illustrates satellite-detected damage in the village of Chein Khar Li (Ku Lar), Koe Tan Kauk tract, Rathedaung township, Rakhine state, Myanmar. Using imagery collected on 31 August 2017, and comparing with imagery collected on 16 May 2017, UNOSAT identified 671 destroyed structures whitin the village. Analysis showed visible signs of scorching with darkened soils and burned vegetation. Almost the entire village appears destroyed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Sep 22, 2017
    This dataset updates: Live
    This map illustrates satellite-detected destroyed or otherwise damaged structures in Maungdaw and Buthindaung townships, Maungdaw District, Myanmar, as seen in satellite imagery collected on 16 September 2017. The analysis found a total area of more than 20 square kilometers of destroyed structures within the 2,000 square kilometers analyzed. According to other data on town locations it is likely that more than 160 towns are affected within the area analyzed. Additionally, 144 fires were detected within the area between 25 August and 21 September 2017 by the MODIS and VIIRS sensors, with recent fire detections indicating destruction is likely ongoing. Most of the detected fires are located in the proximity of the affected areas as observed in the imagery collected 16 September. Finally, heavy cloud cover during the period in question, and on 16 September especially, indicates that destruction and fire detections are likely underestimated in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Sep 27, 2017
    This dataset updates: Live
    This map illustrates satellite-detected destroyed or otherwise damaged structures in Maungdaw and Buthindaung townships, Maungdaw District, Myanmar. The analysis found a total area of more than 400 thousand square meters of destroyed structures occurring between 16 and 25 September 2017. This represents an increase of approximately 2% since last UNOSAT analysis with imagery collected on 16 September, when more than 20 square kilometers of destroyed structures were identified. Additionally, 122 fires were detected within the area between 16 and 25 September 2017 by the MODIS and VIIRS sensors, with recent fire detections indicating destruction is likely ongoing. Most of the detected fires are located in the proximity of the affected areas as observed in the imagery collected 25 September. Finally, heavy cloud cover during the period in question, and on 16 and 25 September especially, indicates that destruction and fire detections are likely underestimated in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 10+ Downloads
    Updated May 15, 2019 | Dataset date: Sep 27, 2017
    This dataset updates: Live
    This map illustrates satellite-detected destroyed or otherwise damaged structures in Maungdaw and Buthindaung townships, Maungdaw District, Myanmar. The analysis found a total area of more than 400 thousand square meters of destroyed structures occurring between 16 and 25 September 2017. This represents an increase of approximately 2% since last UNOSAT analysis with imagery collected on 16 September, when more than 20 square kilometers of destroyed structures were identified. Additionally, 122 fires were detected within the area between 16 and 25 September 2017 by the MODIS and VIIRS sensors, with recent fire detections indicating destruction is likely ongoing. Most of the detected fires are located in the proximity of the affected areas as observed in the imagery collected 25 September. Finally, heavy cloud cover during the period in question, and on 16 and 25 September especially, indicates that destruction and fire detections are likely underestimated in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • This map illustrates satellite-detected destroyed or otherwise damaged structures in Maungdaw and Buthindaung townships, Maungdaw District, Myanmar. The analysis found a total area of more than 160 thousand square meters of destroyed structures occurring between 25 September and 1 October 2017. This represents an increase of approximately 1% since last UNOSAT analysis with imagery collected on 25 September, when more than 20.5 square kilometers of destroyed structures were identified. Additionally, 6 fires were detected within the area between 25 September and 1 October 2017 by the MODIS and VIIRS sensors, with recent fire detections indicating destruction is likely ongoing. Most of the detected fires are located in the proximity of the affected areas as observed in the imagery collected 1 October. Finally, heavy cloud cover and haze during the period in question, indicates that destruction and fire detections are likely underestimated in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 30+ Downloads
    Updated May 15, 2019 | Dataset date: Nov 16, 2017
    This dataset updates: Live
    This map illustrates areas of satellite-detected destroyed or otherwise damaged settlements in Buthidaung, Maungdaw, and Rathedaung Townships in the Maungdaw and Sittwe Districts of Rakhine State in Myanmar. Analysis used satellite imagery collected on multiple dates between 31 August and 11 October 2017 and encompassed an area of about 4,800 square kilometers. Satellite analysis combined with information on settlement locations in Myanmar indicate that approximately 275 towns and villages were affected. This includes 34 in Buthidaung, 225 in Maungdaw, and 16 in Rathedaung. Note that these locations are indicated on the map though only a sampling are labeled due to the limitations of the map size and scale. Inset graphics show what is likely the village of Hpaw Ti Kaung, destroyed sometime between 16 September and 1 October 2017, with only a few structures and trees undamaged. Continued cloud cover and haze during the period in question means that destruction is likely underestimated in this analysis. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 30+ Downloads
    Updated May 15, 2019 | Dataset date: Nov 18, 2017
    This dataset updates: Live
    This map illustrates satellite-detected internally displaced persons shelters in the village tract of Nyaung Pin Gyi, Maungdaw Township, Rakhine State. Using satellite imagery collected on 6 November 2017 UNOSAT identified a total of 457 shelters in a beach area 7 km south of Maungdaw town, next to the mouth of the Naf River between Myanmar and Bangladesh. Several unidentified trucks are observed on the road located adjacent to the internally displaced persons settlement. As of 11 November 2017, 307 shelters were identified which represents a 33 percent decrease from 6 November. Additionally, several likely rafts are observed by the shoreline on 11 November 2017. Additional imagery analysis indicates this settlement began before 6 October and grew quickly, with shelters starting to decrease after 11 November as noted. A smaller camp is located 7 km southeast in the village tract of Ka Nyin Tan Alel Than Kyaw Ka Nyin Tan with approximately 200 shelters as of 11 November 2017. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 20+ Downloads
    Updated May 15, 2019 | Dataset date: Nov 30, 2017
    This dataset updates: Live
    This map illustrates areas of satellite detected fires in Buthindaung, Maungdaw, and Rathedaung Townships in the Maungdaw and Sittwe Districts of Rakhine State in Myanmar. Analysis used satellite- fire detections collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on multiple dates from 25 August to 25 November 2017. A total of 171 fires were detected in different areas across Rathedaung, Buthindaung and Maungdaw townships during this period. While fire detections were spread out across the entire period analyzed, some notable clusters occurred on 28 August, 29 August, 3 September, 15 September, 25 September, 9 October, and 6 November, as indicated in the map. Days of peak fire detection occurred on 28 August and 15 September as indicated in the chart. Note that due to cloud cover and satellite overpass times many fires occurring in the area during this period would not have been detected, and are generally only detected if the satellites are overhead while the fire is sufficiently active and clouds are not interfering. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 200+ Downloads
    Updated May 10, 2019 | Dataset date: Jan 2, 2019
    This dataset updates: As needed
    Note that village refers to all populated places. Place names from GAD with transliteration by MIMU. Coordinates by MIMU. The Rakhine village dataset is not currently available.
  • 300+ Downloads
    Updated May 10, 2019 | Dataset date: Dec 31, 2018
    This dataset updates: As needed
    Administrative boundaries of Myanmar. Admin 0-3 to refer to the following administrative boundaries with sub-regions: Admin 0: Myanmar international boundaries Admin 1: State/ Region/ Union territory with sub-regions (Shan North, East, South and Bago East, West) Admin 2 : District boundaries Admin 3: Township boundaries Place names from GAD with transliteration by MIMU. Coordinates by MIMU
  • 500+ Downloads
    Updated April 29, 2019 | Dataset date: Jan 1, 2015-Dec 31, 2017
    This dataset updates: Every year
    This dataset contains agency- and publicly-reported data on sexual violence and abuse against aid workers between January 2015 and December 2017.
  • 1000+ Downloads
    Updated April 21, 2019 | Dataset date: Jan 1, 2015-Dec 31, 2018
    This dataset updates: Every year
    This dataset contains verified submissions from our partner agencies and publicly-reported data for events in which an aid worker was assaulted or injured. Categorized by country.
  • 40+ Downloads
    Updated April 21, 2019 | Dataset date: Jan 1, 2010-Dec 31, 2019
    This dataset updates: Live
    The ACLED project codes reported information on the type, agents, exact location, date, and other characteristics of political violence events, demonstrations and select politically relevant non-violent events. ACLED focuses on tracking a range of violent and non-violent actions by political agents, including governments, rebels, militias, communal groups, political parties, external actors, rioters, protesters and civilians. Data contain specific information on the date, location, group names, interaction type, event type, reported fatalities and contextual notes.
  • 100+ Downloads
    Updated April 12, 2019 | Dataset date: Dec 19, 2018
    This dataset updates: Every year
    This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.
  • 600+ Downloads
    Updated April 9, 2019 | Dataset date: Sep 1, 2016-Apr 1, 2018
    This dataset updates: Every six months
    The Future of Business survey is a collaboration between Facebook, the OECD and the World Bank to provide timely insights on the perceptions, challenges, and outlook of online Small and Medium Enterprises (SMEs). The Future of Business survey was first launched as a monthly survey in 17 countries in February 2016 and expanded to 42 countries in 2018. In 2019, the Future of Business survey increased coverage to 97 countries and moved to a bi-annual cadence. The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. To date, more than 90 million SMEs have created a Facebook Page, and more than 700,000 of these Facebook Page owners have taken the survey. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest. The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills. The full questionnaire is available for download. This dataset contains survey response data aggregated by country and wave. Future of Business Survey website: futureofbusinesssurvey.org
  • Updated April 5, 2019 | Dataset date: Jan 1, 2019-Jan 1, 2020
    This dataset updates: Live
    Live list of active aid activities for Myanmar shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org
  • 400+ Downloads
    Updated March 14, 2019 | Dataset date: Dec 31, 2018
    This dataset updates: Every year
    This shape file consists of consolidated history of tropical storm paths over the past 50 years in the West Pacific, South Pacific, South Indian and North Indian basin. Attributes provides details such as storm Name, Date, Time, wind speed and GPS points for each advisory point. Wind speeds are in knots for more details on speeds conversion and storm categories please visit the original source of data: UNISYS (http://weather.unisys.com/hurricane/index.php), NOAA (http://rammb.cira.colostate.edu/products/tc_realtime/index.asp)
  • 100+ Downloads
    Updated March 1, 2019 | Dataset date: Feb 27, 2019
    This dataset updates: Every year
    A compilation of time-series data from a variety of sources reported at the national, state, and township level. Additional information about the sources is available in the file.
  • Updated February 7, 2019 | Dataset date: Jan 1, 2000-Dec 31, 2025
    This dataset updates: Live
    Information about asylum applications in a given year and the progress of asylum-seekers through the refugee status determination process.