Updated November 18, 2019
| Dataset date: Nov 18, 2019
This dataset updates: Every week
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:
blockage
width
layer
waterway
depth
covered
water
natural
name
tunnel
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 18, 2019
| Dataset date: Nov 18, 2019
This dataset updates: Every week
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:
opening_hours
name
tourism
shop
amenity
addr:housenumber
addr:street
beds
man_made
addr:city
rooms
addr:full
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 18, 2019
| Dataset date: Nov 18, 2019
This dataset updates: Every week
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:
surface
smoothness
width
layer
lanes
highway
oneway
bridge
name
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 18, 2019
| Dataset date: Nov 18, 2019
This dataset updates: Every week
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:
building:materials
office
building
building:levels
addr:housenumber
addr:street
name
addr:city
addr:full
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 16, 2019
| Dataset date: Oct 17, 2019
This dataset updates: Never
UNOSAT code: FR20191016LBN This map illustrates satellite-detected fire hotspots based on the analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) accessed via NASA FIRMS, between October 15-16, 2019. 30 hotspots were detected in Lebanon and 121 hotspots were detected in three analysed Governorates (i.e. Homs, Lattakia and Tartous) in Syria. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Updated November 15, 2019
| Dataset date: Sep 13, 2018
This dataset updates: Never
Update 15/09 (POST-EVENT)
Now that the typhoon has passed the country, the model is not run with forecasted wind speeds and typhoon track any more, but with actual estimated wind speeds and typhoon track. They come from the same source (Tropical Storm Risk - UCL), and are of the exact same format. All output (map in PDF, data in Excel and in Shapefile) is of the exact same format and interpretation.
Full methodology
1. Based on existing Priority Index model: 510 has previously developed the Priority Index model for typhoons in the Philippines
One day after a typhoon has passed the Philippines ..
.. the model predicts ‘% of completely damaged houses’ per municipality
Based on 12 large typhoons in the last 5 years in the Philippines, for which detailed damage reports were available through NDRRMC (https://www.ndrrmc.gov.ph/)
For these same events, we also collected possible explanatory indicators, such as wind speed (event-specific) and building materials of houses (PH national census).
We built a statistical model, which could explain differences in damage on the basis of differences in wind speed and building materials (etc.)
When dividing all municipalities in 5 equal damage classes (class 1 being the 20% municipalities with lowest damage; class 5 the 20% with highest damage) ..
.. we found that in 73% of the cases we are at most 1 class off.
2. Mangkhut methodology: In the case of typhoon Mangkhut, we are dealing with an upcoming typhoon, which is still awaiting landfall on Saturday 15/09. This is a new situation, which requires the following noteworthy changes in methodology.
Our wind speed source (UCL Tropical Storm Risk) has – in addition to post-event wind speed data as used above – also forecast wind speed data for 5 days ahead.
This forecasted wind speed (and typhoon track) are plugged as input into the above-mentioned prediction model, which - still in combination with building materials - lead to the predicted damage class per municipality that can be seen in the map.
Note that the results are strongly dependent on the input of windspeed, which is itself still an unknown. (see accuracy below).
3. How to use this product:
The map contains damage classes (1-5) per municipality. As such, we advise to put priority on municipalities in damage class 5, and depending on available resources continue with class 4, etc.
This damage class is based on ‘% of houses that are completely damaged’. As priority might also be based on exposure and vulnerability, we have added to the Excel a couple of relevant indicators, from the Community Risk Assessment dashboard.
PRC can decide if and how to combine these various features. If needed, 510 can be asked for assistance of course.
4. Important notes:
ACCURACY: it should be realized that during the course of the coming 3 days, the typhoon might change course, or increase/decrease in terms of strength. This will affect the quality of these predictions. The accuracy figure of 73% that is mentioned in the post-event case should be seen as an upper bound. Given the added inaccuracy of wind speed, the overall accuracy will be lower.
This damage prediction is only about completely damaged houses, not about partially damaged houses.
We only included municipalities that are within 100km of the forecasted typhoon track, as we have seen from previous typhoons (with comparable wind speeds) that damage figures outside of this area are low.
5. Sources
The wind speed is provided by Tropical Storm Risk (University College London). It is the ‘maximum 1-minute sustained wind speed’. An average of this is calculated per municipality. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time)
Typhoon track (from which ‘distance to typhoon track’ per municipality is calculated), is provided by UCL as well. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time)
Additionally, various wall and roof type categories from the Philippines national census. The model uses 2010 census data, as it was developed using this data (2015 census data on municipality level only became available in 2018). The 2015 census data could not be easily plugged in, because of some differences in roof/wall categories. We believe that this would not change the result much though, as even if there are large differences from 2010 to 2015, these would still be dominated by wind speed effects in the model.
All additional indicators, that are added to the Excel table (population, poverty) are derived from the Community Risk Assessment dashboard (Go to this link and click ‘Export to CSV’ on top-right.) The sources for these indicators can be found in the dashboard itself.
Updated November 14, 2019
| Dataset date: Nov 13, 2019
This dataset updates: Never
UNOSAT code: FL20191030SOM This map illustrates the extent of surface waters detected over Hiraan, Middle Shabelle and Lower Shabelle Region in Somalia as detect by VIIRS-NOAA satellite between 2 & 6 November 2019. In the analysed area, a total of about 830 km2 are likely flooded and about 74,000 people might be exposed by taking into account WorldPop population estimates. About 10 km of the roads seem to be affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Updated November 14, 2019
| Dataset date: Nov 13, 2019
This dataset updates: Never
UNOSAT code: FL20191028CAF This map illustrates satellite-detected surface water in Ouaka and Basse-Kotto Prefectures of the Central African Republic, as observed from Sentinel-1 imagery acquired on 5 November 2019. Within the analysed extent of about 970 km2, a total about 9 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT.
Important note: Flood analysis with Sentinel-1 imagery may notably underestimate the presence of standing water in built up areas due to backscattering of the radar signal.
Updated November 14, 2019
| Dataset date: Nov 13, 2019
This dataset updates: Never
UNOSAT code: FL20191028CAF This map illustrates satellite-detected surface waters in Basse-Kotto Prefecture of the Central African Republic, as observed from Sentinel-1 imagery acquired on 5 November 2019. Within the analysed extent of about 390 km2, a total of about 7 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT.
Important note: Flood analysis with Sentinel-1 imagery may notably underestimate the presence of standing water in built up areas due to backscattering of the radar signal.
Updated November 14, 2019
| Dataset date: Nov 13, 2019
This dataset updates: Never
UNOSAT code: FL20191028CAF This map illustrates satellite-detected surface water in Ouaka Prefecture of Central African Republic, as observed from Sentinel-1 imagery acquired on 5 November 2019. Within the analysed extent of about 2,000 km2, a total about 10 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR -UNOSAT.
Important note: Flood analysis with Sentinel-1 imagery may notably underestimate the presence of standing water in built up areas due to backscattering of the radar signal.
Updated November 14, 2019
| Dataset date: Nov 5, 2019
This dataset updates: Never
UNOSAT code: FL20191030SOM This map illustrates the satellite-detected flood water extent and IDP distribution within the town of Belet Weyne in Belet Weyne District, Hiiran Region, Somalia. The analysis was conducted by analyzing WorldView-1 images acquired on the 1 November 2019. As observed from the satellite image, the town of Belet Weyne is heavily affected by floods. Around 60% of the vicinity of the town is completely inundated; the districts of Hawa tako, Kutimbo, and the Lamagalay Regional Millitary Base completely submerged in water. More than 110 IDP sites are located inside of the town and 40% of them are located within completely flooded areas. This is a preliminary analysis and has not been validated in the field yet. Please send ground feedback to UNITAR-UNOSAT.
Updated November 14, 2019
| Dataset date: Nov 13, 2019
This dataset updates: Never
UNOSAT code: FL20191023SSD This map illustrates the 5-day cumulative, day-time surface water extent detected over Luakpiny/Nasir, Pibor and neighbouring counties in South Sudan. The extent was derived from VIIRS-NOAA satellite imagery between 2 and 6 November 2019 and includes all pixels with 0-100% open water.
In the two counties of interest, about 9% of the population in Luakpiny/Nasir and 7% in Pibor may be affected by taking into account WorldPop population estimates. This is a preliminary analysis that has not yet been validated in the field. Please send any fieldbased comments to UNITAR-UNOSAT.
Updated November 13, 2019
| Dataset date: Nov 1, 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.
Updated November 13, 2019
| Dataset date: Nov 4, 2019
This dataset updates: Never
UNOSAT code: FL20191031KEN This map illustrates satellite-detected surface water in Wajir East Sub County, Wajir County of Kenya as observed from Sentinel-2 imagery acquired on 2 November 2019. Within the analysed extent of about 200 km2, a total of about 7 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Updated November 13, 2019
| Dataset date: Nov 2, 2019
This dataset updates: Never
UNOSAT code: FL20191031KEN This map illustrates satellite-detected surface water in Wajir East Sub County, Wajir County of Kenya as observed from Sentinel-2 imagery acquired on 28 October 2019. Within the analysed extent of about 450 km2, a total of about 25 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Updated November 13, 2019
| Dataset date: Nov 5, 2019
This dataset updates: Never
UNOSAT code: FL20191030SOM This map illustrates the extent of surface waters detected over Jowhar District, Middle Shabelle Region located in Somalia, as detect by VIIRS-NOAA satellite between 30 October & 3 November 2019. In the analysed area, a total of about 700 km2 are likely flooded and about 70,000 people may be affected, based on WorldPop population estimates. The 10 km of roads seem to be affected. This is a preliminary analysis and has not been validated in the field yet. Please send ground feedback to UNITAR-UNOSAT.
Updated November 13, 2019
| Dataset date: Nov 4, 2019
This dataset updates: Never
UNOSAT code: FL20191031KEN This map illustrates satellite-detected surface water in Garsen Sub County, Tana River County of Kenya as observed from Sentinel-2 imagery acquired on 28 October 2019. Within the analysed extent of about 150 km2, a total about 12 km2 of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Updated November 13, 2019
| Dataset date: Nov 12, 2019
This dataset updates: As needed
Draft Subdivisions for Grand Bahama, showing draft subdivisions by jurisdiction.
The boundaries, designations and names used on this map do not imply official endorsement or acceptance by the Shelter Cluster.
This is a draft produced for programmatic and planning purposes, it will be updated as soon as possible.
The boundaries for Grand Bahama Port Authority (GBPA) on this shapefile are approved. Other boundaries are draft boundaries.
Updated November 12, 2019
| Dataset date: Nov 12, 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
layer
smoothness
lanes
oneway
surface
width
highway
bridge
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 12, 2019
| Dataset date: Nov 12, 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
shop
amenity
opening_hours
addr:street
man_made
beds
addr:housenumber
addr:full
rooms
tourism
addr:city
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 12, 2019
| Dataset date: Nov 12, 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
depth
layer
natural
tunnel
covered
width
water
waterway
blockage
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
Updated November 10, 2019
| Dataset date: May 1, 2018
This dataset updates: Every year
The zip file contains Health region (adm1) and districts boundaries (adm2) for DRC in shapefile and GeoJSON format. These datasets can be found in the WHO Ebola open data platform: https://ebolaoutbreak2018-who.opendata.arcgis.com
