12 October 2017
| Dataset date: October 29, 2017-October 29, 2017
On 22 September 2017 at 20.30 hrs. Indonesia’s Centre of Volcanology and Geological Hazard Mitigation (PVMBG) increased the status of Mt. Agung in Karangasem District, Bali Province from Level 3 (High Alert) to Level 4 (Danger), the highest level for a volcano. And on 29 October 2017, at 16.00 hrs, the status of Mt. Agung has been lowered from Level IV (dangerous) to Level III (alert).
The no activity zone has also been reduced from 9 Km radius with additional sectoral expansion of 12km north-northwest and south-southwest become 6 km radius from the volcano with additional sectoral expansion of 7.5 km north-northwest and south-southwest. The displaced people who lived outside of the no activity zone start to return back home but advised to remain cautious.
9 October 2017
| Dataset date: January 01, 1950-September 30, 2017
The Oceanic Niño Index (ONI) has become the de facto standard that the National
Oceanic and Atmospheric Administration (NOAA) uses to identify El Niño (warm) and
La Niña (cool) events in the tropical Pacific. It is the three month mean SST
anomaly for the El Niño 3.4 region (i.e., 5°N-5°S, 120°-170°W). Events are defined as five
consecutive overlapping three month periods at or above the +0.5°C anomaly for warm (El
Niño), events and at or below the -0.5 anomaly for cold (La Niña) events. The threshold
is further broken down into Weak (with a 0.5 to 0.9 SST anomaly), Moderate (1.0 to 1.4)
and Strong (≥ 1.5) events. For an event to be categorized as weak, moderate or strong. it
must have equalled or exceeded the threshold for at least three consecutive overlapping
three month periods.
4 October 2017
| Dataset date: September 01, 2015-September 01, 2016
This is the list of feedback received by the Internews Humanitarian Information System In the Protection of Civilians Camps in South Sudan, and namely in: Juba POC 1 and 3; Malakal PoC; Bentiu PoC; Bot PoC. The data was collected from September 2015 to September 2016.
10 September 2017
| Dataset date: September 08, 2017-September 08, 2017
This dataset illustrates satellite-detected potential damaged buildings in Anguilla Island, following the landfall of Tropical Cyclone IRMA-17 on September 6, 2017. The UNITAR-UNOSAT analysis used a Kompsat-3 satellite image acquired on 9 February 2017 and WorldView-2 image acquired on 5 July 2017 as pre-imagery and Pleiades satellite imagery acquired on the 7 & 8 September 2017 as a post-imagery. The UNITAR-UNOSAT analysis identified 2,147 potentially damaged structures within the analyzed area that was not covered by clouds. According to the pre-building footprints, inside the area free of clouds, provided by Humanitarian Open Street Map, this represents 42% of potentially affected structures in Anguilla.
8 September 2017
| Dataset date: September 07, 2017-September 07, 2017
This dataset illustrates the tropical cyclone IRMA-17 path with low, medium and strong wind impact zones observed and predicted at 7 September 2017. The tropical cyclone path and wind speed zones were derived from Joint Research Centre data (Warning 33 issued the 07 th September 2017 at 09:00 UTC). This is a preliminary analysis and has not yet been validated in the field.
29 August 2017
| Dataset date: January 29, 2016-August 04, 2016
Within 24 hours of the World Health Organization declaring the Zika virus a global health emergency, RIWI began a study in 9 countries across the Americas capturing over 30,000 respondents. Data collection targeted respondents' knowledge of Zika virus transmission mechanisms and confidence in government health agencies to treat and contain the epidemic. The data was collected using RIWI's patented Random Domain Intercept Technology™ (RDIT).
17 August 2017
| Dataset date: August 15, 2017-August 15, 2017
In this analysis we have combined several data sources around the floods in Bangladesh in August 2017.
See attached map for a map visualization of this analysis.
See http://bit.ly/2uFezkY for a more interactive visualization in Carto.
Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action.
Indicators and sources
In the attached map, we combined several sources:
Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20)
Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.)
Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis.
Detailed methodology Vulnerability
The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop).
The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas.
Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).
19 July 2017
| Dataset date: April 05, 2017-April 05, 2017
Madagascar, Cyclone Enawo
Final Needs Assessment data with disaggregated data related to key vulnerable populations and SADD available.
Data collected and put together by the Malagasy Red Cross Society (MRCS)
4 July 2017
| Dataset date: July 04, 2017-July 04, 2017
The Formal Sites Monitoring Tool (FSMT) is a camp management monitoring tool, designed to provide a synopsis of the main demographic information at site level as well key humanitarian indicators for all formal sites across Iraq.