Key Figures
Data Completeness
3/26 Core Data 10 Datasets 9 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.
Legend:
Presence, freshness, and quality of dataset
  • Dataset fully matches criteria and is up-to-date
  • Dataset partially matches criteria and/or is not up-to-date
  • No dataset found matching the criteria
Expand
Affected People
1 Datasets
Coordination & Context
3 Datasets
Food Security & Nutrition
1 Datasets
Geography & Infrastructure
3 Datasets
Health & Education
0 Datasets
Population & Socio-economic Indicators
2 Datasets
Refine your search: Clear all
Featured:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
More
  • 3600+ Downloads
    Updated June 19, 2019 | Dataset date: Jun 19, 2019
    This dataset updates: Every day
    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.
  • 2000+ Downloads
    Updated June 18, 2019 | Dataset date: Jan 1, 2017-May 31, 2019
    This dataset updates: Every month
    This dataset contains agency- and publicly-reported data for events in which an aid worker was killed, kidnapped, or arrested. Categorized by country.
  • 40+ Downloads
    Updated June 16, 2019 | Dataset date: Jan 1, 1999-Dec 31, 2017
    This dataset updates: Every year
    FAO statistics collates and disseminates food and agricultural statistics globally. The division develops methodologies and standards for data collection, and holds regular meetings and workshops to support member countries develop statistical systems. We produce publications, working papers and statistical yearbooks that cover food security, prices, production and trade and agri-environmental statistics.
  • 3800+ Downloads
    Updated June 16, 2019 | Dataset date: Jan 1, 1992-May 15, 2019
    This dataset updates: Every week
    This dataset contains Global Food Prices data from the World Food Programme covering foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
  • 200+ Downloads
    Updated June 16, 2019 | Dataset date: Jan 15, 2008-Apr 15, 2019
    This dataset updates: Every week
    This dataset contains Food Prices data for Myanmar. Food prices data comes from the World Food Programme and covers foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
  • 10+ Downloads
    Updated June 15, 2019 | Dataset date: Nov 16, 2015-Dec 31, 2027
    This dataset updates: Live
    List of aid activities by InterAction members in Myanmar. Source: http://ngoaidmap.org/location/gn_1327865
  • 100+ Downloads
    Updated June 3, 2019 | Dataset date: Jun 3, 2019
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: building IS NOT NULL Features may have these attributes: name building building:levels building:materials addr:full addr:housenumber addr:street addr:city office This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 200+ Downloads
    Updated June 3, 2019 | Dataset date: Jun 3, 2019
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: highway IS NOT NULL Features may have these attributes: name highway surface smoothness width lanes oneway bridge layer This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 100+ Downloads
    Updated June 3, 2019 | Dataset date: Jun 3, 2019
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay') Features may have these attributes: name waterway covered width depth layer blockage tunnel natural water This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 80+ Downloads
    Updated June 3, 2019 | Dataset date: Jun 3, 2019
    This dataset updates: Every month
    OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL Features may have these attributes: name amenity man_made shop tourism opening_hours beds rooms addr:full addr:housenumber addr:street addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 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
  • 400+ Downloads
    Updated May 23, 2019 | Dataset date: Sep 1, 2018-Apr 30, 2019
    This dataset updates: Every month
    This dataset includes incidents affecting the affecting the protection of IDPs and refugees. The data contains incidents identified in open sources. Categorized by country.
  • 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.