This dataset has been consolidated from NDRMC / DROMIC reports from 2014 - 2020 to summarize the number of people affected and houses damaged in the Philippines as a result of typhoons.
War-Damaged Shelter Rehabilitation data as submitted by humanitarian and development actors undertaking assessment and rehabilitation of war-damaged shelter for returnees across the country.
The Civilian Impact Monitoring Project (CIMP) is a monitoring mechanism for real-time collection, analysis and dissemination of open source data on the civilian impact from armed violence in Yemen, with the purpose of informing and complementing protection programming. CIMP is a service under the United Nations Protection Cluster for Yemen and, since going live on 01 August 2018, has reported in real-time on the impact of incidents of armed violence on civilians at the national level, divided into 5 hubs: Al-Hudaydah, Sa’ada, Sana’a, Aden and Ibb. The dataset shows the number of incidents to have impacted upon civilian houses, farms, vehicles, businesses and markets and the number of incidents to have impacted upon civilian infrastructure sites since December 2017.
1) Natural disaster events include avalanches, extreme winter conditions, flooding, heavy rainfall, landslides & mudflows, and extreme weather (sandstorms, hail, wind, etc) as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB).
2) A natural disaster incident is defined as an event that has affected (i.e. impacted) Afghans, who may or may not require humanitarian assistance.
3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
1) Natural disaster events include avalanches, extreme winter conditions, flooding, heavy rainfall, landslides & mudflows, and extreme weather (sandstorms, hail, wind, etc) as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB). 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) Afghans, who may or may not require humanitarian assistance. 3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
On the 26th of October 2015, a large scale earthquake caused shelter damage throughout much of northern and central Afghanistan. During August 2016, the REACH Initiative (supported by ACTED, AfghanAid and People in Need) conducted a shelter response evaluation in 3 districts of Afghanistan on behalf of the Shelter Cluster. The aim of the assessment was to evaluate shelter interventions and locate possible intervention gaps in order to inform the shelter cluster of Afghanistan of the current shelter context and needs of earthquake affected families. The assessment consisted of three specific areas of investigation:
1. To monitor change in sheltering conditions for families
2. To evaluate the value of various shelter interventions in allowing families to recover and to identify possible gaps
3. To determine recovery limitations and successes relating to vulnerable groups
1) Natural disaster events include avalanches,earthquake, flooding, heavy rainfall & snowfall, and landslides & mudflows as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB).
2) A natural disaster incident is defined as an event that has affected (i.e. impacted) people, who may or may not require humanitarian assistance.
3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
4) The number of affected people and houses damaged or destroyed are based on the reports received. These figures may change as updates are received.
1) Natural disaster events include avalanches, extreme winter conditions, flooding, heavy rainfall, landslides & mudflows, and extreme weather (sandstorms, hail, wind, etc) as recorded by OCHA field offices. 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) Afghans, who may or may not require humanitarian assistance. 3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
In 2012, REACH, in support of the Libyan Ministry of Education and UNICEF, conducted an assessment of all Libyan schools enabling the collection of comprehensive baseline data on the status of the education sector in Libya following the 2011 uprising. The assessment covered all Libyan schools (4,800 in total) through a comprehensive survey that consisted of 63 items covering 5 dimensions (school information, student and teacher information, WASH facilities, education facilities and materials, protection issues).
This dataset contains:
windspeeds of Typhoon Nina
rainfall of Typhoon Nina
Priority Index of Typhoon Nina
The predicted priority index of Typhoon Nina is produced by a machine learning algorithm that was trained on five past typhoons: Haiyan, Melor, Hagupit and Rammasun and Haima, It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on houses damaged and completely destroyed.
The output is a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy.
The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing.
Scoring
The model has an best r2 score of 0.794933727 and an accuracy of 0.699470899
Data sources:
Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX)
Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX)
Avg. wind speed (mph) - University College London
Typhoon path - University College London
Houses damaged - NDRRMC
Rainfall - GPM
Poverty - Pantawid pamilyang pilipino program (aggregated)
Roof and wall materials
New geographical features
All the columns with feat_ indicates the importance of that feature, if not present that feature was not used.
learn_matrix name of the learning matrix with the 5 typhoons
run_name unique run name (pickle files and csv files have this name for this model)
typhoon_to_predict name of a new typhoon to predict
val_accuracy accuracy based on 10 categories of damage 0% 10% 20% …
val_perc_down perc of underpredicted categories
val_perc_up perc of overpredicted categories
Val_best_score best r2 score
Val_stdev_best_score error on best score based on the CV
Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit
Val_mean_error_num_houses average error on the number of houses
val_median_error_num_houses median
val_std_error_num_houses std deviation of the errors (lower is better)
Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.
This map illustrates satellite-detected damage to infrastructure and roads in a portion of the city of Deir Ez Zor, Syrian Arab Republic and is derived from a full UNOSAT analysis of damage to Deir Ez Zor in 2017. Using satellite imagery acquired 9 November 2017, UNITAR - UNOSAT identified 6 destroyed bridges, 54 damaged road segments and 93 visible impact craters affecting roads. Approximately 169 structures corresponding to educational facilities, and 21 structures likely related to health facilities are also affected. UNOSAT analysis shows that there are 3 destroyed electricity towers south of the city. Note that sources of infrastructure data include various open source datasets which are likely incomplete, as well as visual review of satellite imagery by UNOSAT for certain features like towers. 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 damage to infrastructure and roads in the city of Ar Raqqa, Syrian Arab Republic and is derived from a full UNOSAT analysis of damage to Ar Raqqa in 2017. Using satellite imagery acquired 21 October 2017, UNITAR - UNOSAT identified 5 destroyed bridges, 74 damaged road segments and 94 visible impact craters affecting roads. Approximately 159 structures corresponding to educational facilities, and 26 structures likely related to health facilities are also affected. UNOSAT analysis shows that 8 water towers are completely destroyed and there is damage to one electric substation and a sewage treatment facility. Note that sources of infrastructure data include various open source datasets which are likely incomplete, as well as visual review of satellite imagery by UNOSAT for certain features like water towers. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates the percentage of buildings damaged in the city of Aleppo, Syrian Arabic Republic, as determined by satellite imagery analysis. Using satellite imagery acquired 18 September 2016, 01 May 2015, 26 April 2015, 23 May 2014, 23 September 2013, and 21 November 2010, UNOSAT identified a total of 33,521 damaged structures within the extent of this map. These damaged structures are compared with total numbers of buildings found in a pre-conflict satellite image collected in 2009 to determine the percentage of damaged buildings across the city. Based on this analysis and in the map extent, in 19 neighborhoods the number of damaged buildings is more than 40%. The most damaged is Al Aqabeh with 65.61% of buildings damaged and the most significant change since UNOSAT’s 2015 analysis is Khalidiyeh, which increased in percentage damage from 4.20% to 55.80%. Note that this analysis considers only damage in residential areas and excludes industrial areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
