Cyclone Idai - 2019

Tropical Cyclone Idai made landfall in central Mozambique the night of 14 March 2019. On 27 March 2019, IOM in coordination with the Government of Mozambique carried out site assessments in 32 evacuation sites in the Beira district in the Sofala province of Mozambique one of the provinces affected by the storm.

The dataset contains number of IDPs, Returnees and host communities and their needs at sub national level.

This dataset is based on an extract from OpenStreetMap. In addition, it contains access constraints status in the column "status" and is edited regularly with information received by partners of the Logistics cluster in Mozambique. This dataset is the same as used on the Logistics Cluster Access Constraints map published here: https://logcluster.org/sector/cyclone-idai19

Tropical cyclone Idai, on March 15th 2019, brought torrential rains and winds affecting mostly the provinces of Manica, Sofala and Zambézia, In Mozambique, causing flash flooding and subsequent destruction. This dataset contains DTM assessments containing number of affected people, their needs, geographic locations etc.

DTM is tracking in/out movement of displaced people in 4 Provinces hit by IDAI cyclone.

Malawi administrative level 0 (country), 1 (region), 2 (district or city council), and 3 (traditional authority area) 2008 population statistics and gazetteer Some humanitarian actors are using an alternative 28-unit administrative level 2 (district) structure instead of the endorsed 32-unit structure. This dataset provides resources for both the 28-district and 32-district administrative level 2 (district) structures and the level 3 (traditional authority area) resources have P-codes for both. A lookup table is provided. These CSV tables are suitable for database or ArcGIS linkage to the Malawi administrative level 0-3 boundaries shapefiles.

Spreadsheet populated with HDX metadata of Mozambique datasets used for operational purposes during Cyclone Idai humanitarian response.

Beira and Dondo neighbourhood boundaries. Official Beira bairros from INGC.

The data contains number of IDPs dis-aggregated by gender, age at village level.

Malawi has experienced floods and sustained heavy rains caused by the tropical cyclone Idai weather system. IOM, in close coordination with the Government of Malawi through the Department of Disaster Management Affairs (DoDMA), conducted multi-sectoral location assessments in Chikwawa, Nsanje, Phalombe, Zomba districts. The dataset contains number of IDPs, households and their needs at sub-national level.

Mozambique 3W/4W (Response Tracking) for the the Cyclone Idai response.

This data illustrates the satellite detected surface waters in Manica, Sofala, and Tete Provinces in Mozambique, as observed from Sentinel-1 imagery acquired on 13, 14, 19 and 20 March 2019.

This dataset includes 700 completed interviews from Mozambique following Cyclone Idai, including respondents from Sofala province and Beira. All respondents were affected by the cyclone, and data includes information on home and community damages, food security, and access to resources including clean water and electricity. Data was collected via remote SMS survey March 21st, 2019 - March 22nd 2019 and is not nationally or regionally representative but is intended to show a snapshot of the cyclone's impact on the ground. Statistical disclosure control methods have been applied to the data in order to protect individual respondents information.

Rapid Assessments registry for Mozambique

This dataset contains the shapefile of health facility damages due to Tropical Cyclone Idai as at 8 April 2019

Layer provides distribution of landslide hazard, describing by an index of very low to very high hazard. Data produced under the ADRF Sub Saharan Africa Risk Profiles project. The project provides also risk data produced, exposure and hazard data used in the analyses.

Beira, Mozambique Water Points

Daily extract of Mozambique survey made by WFP to track access constraints

Data produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA).

Data produced under hydrological forecasting strategy where National Directorate of Water Resource Management (DNGRH) is one of the main actors of the Disaster Risk Reduction system, whose fundamental and prime task is the monitoring, forecasting and issuing early warnings about the risks ahead, resulting in the safeguarding of lives and property

Livelihood zone maps define geographic areas of a country where people generally share similar options for obtaining food and income and similar access to markets. An understanding of geographic livelihood systems is a key component of FEWS NET’s food security analysis. The maps are produced through multi-day workshops during which food security stakeholders and country experts identify zones. Factors considered include agro-climatology, elevation, land-cover, market accessibility, sources of food, and major economic activities. A livelihood zone map is typically accompanied by a livelihood description which outlines the key characteristics of each zone. In some cases, maps may also be accompanied by livelihood profiles, which are in-depth descriptions of the characteristics of wealth groups within each zone. The zone maps are available for download as PDFs, PNGs, and GIS shapefiles. The shapefiles and maps are all produced and maintained by FEWS NET. Please consult the logos in maps for the full list of organizations who participated in and/or funded the zoning workshop. For more information on FEWS NET’s livelihood approach and to view our other livelihood products, please click here.

Population data collected from Census 2017, projected to 2018 . The base map was produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA).

Data were produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA)

This dataset contains key figures and other data pertaining to the evolving humanitarian situation in during the Tropical Cyclone Idai response in Mozambique.

Data produced under the ADRF Sub Saharan Africa Risk Profiles project. The project provides also risk data produced, exposure and hazard data used in the analyses.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas. The data were developed using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Proportional allocation was used to distribute population data from subnational census data to the settlement extents. The data are distributed as GeoTIFF files. The Population GeoTIFF represents population estimates (in persons)

Data produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA).

Adjusted shape file with position and names of dams in Mozambique watersheds.

This map illustrates the satellite detected surface waters in Manicaland Province, Zimbabwe, as observed from the Sentinel-1 data imagery acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 164,130 ha of surface waters were observed the 12 March 2019. and about of 406,600 ha of surface waters were observed the 24 March 2019. It represents an increase of 40 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT

This map illustrates the satellite detected surface waters in Masvingo Province, Zimbabwe, as observed from the Sentinel-1 data imageries acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 84,500 ha of surface waters were observed the 12 March 2019. and about of 288,500 ha of surface waters were observed the 24 March 2019. It represents an icrease of 29 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT

This map illustrates the satellite detected surface waters in Mashonaland East Province, Zimbabwe, as observed from the Sentinel-1 data imageries acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 108,780 ha of surface waters were observed the 12 March 2019. and about of 469,680 ha of surface waters were observed the 24 March 2019. It represents an icrease of 23 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT

This map illustrates the cumulative satellite-detected surface waters in central provinces of Mozambique and particularly the Sofala province and its surroundings, as observed from the Sentinel-1 imagery acquired between 13 & 26 March 2019. Within the analysis extent. over Sofala and Manica provinces, a total of 170,000 km2 of surface imagery were analyzed and about 10,000 km2 of lands experienced floods within this period. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data : Sentinel-1 Imagery date: 13-14-19-20-26 March 2019 Resolution: 10 m Copyright: Copernicus 2019/ESA Source:ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production:UNITAR - UNOSAT

This map illustrates satellite-detected surface waters in Tete and Zambezia province, Mozambique as observed from Sentinel-1 imagery acquired on 7 April 2019. Within the analysed extent of about 7,800 sq km, a total about 100 sq km of lands appear to be flooded as of 7 April 2019. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite Data (Post): Sentinel-1 Imagery Date (1): 7 April 2019 Imagery Date (2): 20 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA

Mozambique Rapid Needs Assessment data taken from IFRC Kobo toolbox

IDP shapefile collected by MSF Médecins Sans Frontières (MSF) International - doctors without borders during the first week of April 2019 in Mozambique Beire

This data relates to an aerial survey conducted of Mozambique from the 24-29 March 2019 following tropical cyclone Idai. Data was collected by INGC (National Disaster Management Authority of Mozambique), IFRC, UNDAC, MSF, DFID, Save the Children, and MapAction. Enumerators flew in squirrel helicopters over priority areas (usually at 500 feet) and some fixed wing aircraft for confirmation on outlying areas. Data contains location, points of interest, severity and estimated population. The survey used Kobo for data collection. Processing was conducted in R and then imported into ArcGIS for final map products. Data is presented in .xlsx (with and without hxl tags) and .geojson formats.

This dataset is a compilation of various sources such as Copernicus, Sentinel-1 and Atmospheric and Environmental Research, A Verisk Business, & African Risk Capacity using several days to calculate the maximum flood extent for the whole event.

Mozambique health facilities