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  • Master list of IDPs currently residing in Mozambique as of July 2015
    • XLSX
    • 100+ Downloads
    • This dataset updates: Never
  • WFP - World Food Programme
    Updated July 17, 2016 | Dataset date: May 14, 2015
    The Coping Strategy Index dataset measures the severity and frequency of the strategies that households use to cope with acute food insecurity. The strategies vary from borrowing food or money from neighbors to selling household assets. This data is available for 31 countries at a sub-national level.
    • CSV
    • 500+ Downloads
    • This dataset updates: Every month
  • United Nations Economic Commission for Africa
    Updated May 23, 2016 | Dataset date: Jan 1, 2006-Dec 31, 2015
    Total migrants in Africa.
    • XLSX
    • 200+ Downloads
    • This dataset updates: Never
  • HDX
    Updated March 21, 2016 | Dataset date: Jun 1, 2013
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on June 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for April-June 2013 southernafrica201304_ML2 Most likely food security outcome for July-September 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • HDX
    Updated March 21, 2016 | Dataset date: Oct 1, 2015
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on February 05, 2016. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: SA201304_ML1 Most likely food security outcome for October-December 2015 SA201304_ML2 Most likely food security outcome for January-March 2016 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • HDX
    Updated March 21, 2016 | Dataset date: Jan 1, 2014
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201401_ML1 Most likely food security outcome for January-March 2014 southernafrica201401_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • HDX
    Updated March 21, 2016 | Dataset date: Apr 1, 2014
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on July 17, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for April-June 2014 southernafrica201304_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • HDX
    Updated March 21, 2016 | Dataset date: Jul 1, 2013
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201307_ML1 Most likely food security outcome for July-September 2013 southernafrica201307_ML2 Most likely food security outcome for October-December 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • HDX
    Updated March 21, 2016 | Dataset date: Oct 1, 2014
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for October-December 2014 southernafrica201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • HDX
    Updated March 21, 2016 | Dataset date: Jul 1, 2014
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on September 26, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201307_ML1 Most likely food security outcome for July-September 2014 southernafrica201407_ML2 Most likely food security outcome for October-December 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • HDX
    Updated March 21, 2016 | Dataset date: Jul 1, 2015
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on August 19, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: SA201304_ML1 Most likely food security outcome for July-September 2015 SA201304_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • HDX
    Updated March 21, 2016 | Dataset date: Jan 1, 2013
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on January 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for January-March 2013 southernafrica201304_ML2 Most likely food security outcome for April-June 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • HDX
    Updated March 21, 2016 | Dataset date: Feb 10, 2015
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on February 10, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for January-March 2015 southernafrica201304_ML2 Most likely food security outcome for April-June 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • This map illustrates satellite-detected shelters and other structures at the Kapise refugee camp in Mwanza District, Malawi. The camp is for refugees fleeing reported violence in neighboring Mozambique. An initial examination of WorldView-2 satellite imagery acquired 16 February 2016 revealed a total of 1,497 structures within the camp. Approximately 24 of these were administrative buildings, 1,416 were tent or improvised shelters, and 57 were semi-permanent structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
  • HDX
    Updated November 25, 2015 | Dataset date: Jun 1, 2015
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on June 01, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for April-June 2015 southernafrica201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • This map illustrates satellite-detected flood waters in the Chikwawa and Nsanje Districts of Southern Region of Malawi and in the Morrumbala District of Zambezia Province of Mozambique along the Shire River as detected by Landsat-7 imagery acquired 07 February 2015. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.
  • This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Landsat-7 imagery acquired 07 February 2015. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.
  • This map illustrates satellite-detected flood waters in Maganja Da Costa and Namacura District of Zambezia Province, Mozambique, as detected by Radarsat-2 imagery acquired 03 February 2015. Between 18 January and 03 February, flood waters slightly decreased and affected roughly 52,700 hectars of land. A total of 69 potentially affected towns were detected within the complete analyzed area.This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Radarsat-2 imagery acquired 04 February 2015. Between 30 January 2015 and 04 February 2015 waters receded from approximatively 11% of the surface of lands detected as flooded the 30 January 2015. 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 flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Radarsat-2 imagery acquired 30 January 2015. Between 21 January 2015 and 30 January 2015 waters receded from about 30,000 ha of lands but many areas along the Shire River remain affected. About 22 villages are located within the flooded zone as of 30 January 2015 and according to the World Population data base around 25,000 people are located within these potentially affected a rea s. 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 flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and southern Malawi along the Chire River as detected by Radarsat-2 imagery acquired 21 January 2015. Between 11 December 2014 and 21 January 2015 flood waters affected roughly 55,000 hectares of lands in the five listed districts. About 31 villages are located within the flooded zone and according to the World Population database around 33,500 people are located within these potentially affected areas. 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 flood waters in the Maganka Da Costa, Namacurra and Mocuba Districts of Zambezia Province, Mozambique, as detected by Radarsat-2 imagery acquired 18 January 2015. Between 11 and 18 January 2015 flood waters affected roughly 85,000 hectares of land, with inundated areas increasing approximately 800% from pre-flood areas, particularly in the coastal part of Mangaja Da Costa District. About 41 villages are located within the flooded zone, and according to the World Population database around 73,000 people are located within these potentially affected areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • WFP - World Food Programme
    Updated November 24, 2015 | Dataset date: May 14, 2015
    The Income Activities dataset includes data on income generation at the household level. Sources of income listed include labor, agriculture, asset sales, and remittances, among others. It is available for 32 countries.
    • CSV
    • 400+ Downloads
    • This dataset updates: Every month