Refine your search: Clear all
Featured:
Data series [?]:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
  • 3200+ Downloads
    Time Period of the Dataset [?]: January 01, 2017-December 31, 2024 ... More
    Modified [?]: 24 March 2024
    Dataset Added on HDX [?]: 9 March 2017
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.
  • 5800+ Downloads
    Time Period of the Dataset [?]: January 01, 2019-February 01, 2024 ... More
    Modified [?]: 24 March 2024
    Dataset Added on HDX [?]: 10 July 2019
    This dataset updates: Every month
    The INFORM Severity Index is a regularly updated, and easily interpreted model for measuring the severity of humanitarian crisis globally. It is a composite index, which brings together 31 core indicators, organised in three dimensions: impact, conditions of affected people, and complexity. All the indicators are scored on a scale of 1 to 5. These scores are then aggregated into components, the three dimensions (Impact, Conditions, Complexity), and the overall severity category based on the analytical framework. The three dimensions have been weighted according to their contribution to severity: impact of the crisis (20%); conditions of affected people (50%); complexity (30%). The weightings are currently a best estimate and will be refined using expert analysis and statistical methods. Each crisis will fall into 1 of 5 categories based on their score ranging from very low to high. ACAPS – an INFORM technical partner – is responsible for collection, cleaning, analysis and input of data into the model and the production of the final results. Read more about the INFORM Severity Index: https://www.acaps.org/en/thematics/all-topics/inform-severity-index
  • 1900+ Downloads
    Time Period of the Dataset [?]: January 01, 2018-December 31, 2024 ... More
    Modified [?]: 4 March 2024
    Dataset Added on HDX [?]: 21 January 2019
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    Data provides the Humanitarian Country team's shared understanding of the crisis, including the most pressing humanitarian needs, and reflects its joint humanitarian response planning.
  • Time Period of the Dataset [?]: January 01, 2023-December 31, 2023 ... More
    Modified [?]: 15 February 2024
    Dataset Added on HDX [?]: 15 February 2024
    This dataset updates: Every year
    Severity level by location in 2023
  • COD 5900+ Downloads
    Time Period of the Dataset [?]: January 01, 2018-December 31, 2024 ... More
    Modified [?]: 6 February 2024
    Dataset Added on HDX [?]: 12 December 2017
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning. People in Need 2024: The Excel files contains people in Need and people targeted for 2024 disaggregated by sex and age group per cluster and Admin 2 level. The data was approved for use by the Humanitarian Country Team, and it’s based on the Humanitarian Program Cycle 2024 in Yemen. The tables are suitable for database or GIS linkages to the Yemen – Administrative Boundaries. Intersectoral People in Need 2022: For the 2022 Humanitarian Needs Overview, Yemen applied the enhanced HPC approach and the corresponding IASC Joint Inter-sector Analysis Framework (JIAF) global guidance. More details in the methodology of the HNO 2022 in the below link. https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2022%20-%20Final%20Version%20%281%29.pdf Cluster People in Need 2022: The data file contains people in need for 2022 per cluster. The data approved for use by the Humanitarian Country Team and it’s based on the Humanitarian Needs Overview for 2022 in Yemen. The tables are suitable for database or GIS linkages to the Yemen – Administrative Boundaries. The full Humanitarian Needs Overview available in the below link: https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2022%20-%20Final%20Version%20%281%29.pdf Yemen Population estimates for 2022: The data contains population estimates for 2022. The projections are based on 2004 Census data. The population figures are dis-aggregated by governorate and district levels, both containing p-codes. The data is further dis-aggregated by sex and age groups. The data approved for use by the Humanitarian Country Team and used in Humanitarian Needs Overview (HNO) for Yemen in 2022. These tables are suitable for database or GIS linkage to the Yemen - Administrative Boundaries boundaries. Intersectoral People in Need 2021: For the 2021 Humanitarian Needs Overview, Yemen applied the enhanced HPC approach and the corresponding IASC Joint Inter-sector Analysis Framework (JIAF) global guidance. More details in the methodology of the HNO 2021 in the below link. https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2021_Final.pdf Cluster People in Need 2021: The data file contains people in need for 2021 per cluster. The data approved for use by the Humanitarian Country Team and it’s based on the Humanitarian Needs Overview for 2021 in Yemen. The tables are suitable for database or GIS linkages to the Yemen – Administrative Boundaries. The full Humanitarian Needs Overview available in the below link: https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2021_Final.pdf Yemen Population estimates for 2021: The data contains population estimates for 2021. The projections are based on 2004 Census data. The population figures are dis-aggregated by governorate and district levels, both containing p-codes. The data is further dis-aggregated by sex and age groups. The data approved for use by the Humanitarian Country Team and used in Humanitarian Needs Overview (HNO) for Yemen in 2021. These tables are suitable for database or GIS linkage to the Yemen - Administrative Boundaries boundaries.
  • Time Period of the Dataset [?]: January 01, 2023-October 31, 2023 ... More
    Modified [?]: 25 January 2024
    Dataset Added on HDX [?]: 9 March 2023
    This data is by request only
    The CoRA employs a multi-sectoral location-level assessment (MSLA) methodology conducted with local authority key informants at the settlement or city-raion level. The assessment is based on 20 indicators divided into five ‘drivers’: (1) livelihoods, (2) utilities and services (3) residential destruction, (4) safety and security, and (5) public life. The indicators describe the critical or minimum conditions required for sustainable return and reintegration in Ukraine. The scores of each indicator and driver are grouped into three categories: low, medium, and high severity of living conditions. For more information on the methodology, please refer to the full report. When requesting the dataset, please specify if you would like to receive the report as well.
  • COD 2000+ Downloads
    Time Period of the Dataset [?]: January 01, 2020-December 31, 2024 ... More
    Modified [?]: 19 January 2024
    Dataset Added on HDX [?]: 20 April 2021
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    1) The dataset is produced by the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners. It presents the number of people in need (PiN), the severity of those needs, the activities of the planned response, and the number of people to be targeted for humanitarian assistance in Ukraine as explained the 2024 Humanitarian Needs and Response Plan (HNRP). 2) The interpretation and use of this data should be guided by the 2024 Ukraine Humanitarian Needs and Response Plan. 3) The data sources and methodology for the derivation of the PIN and severity is explained in Annex 4.2 of the Ukraine 2024 HNRP, "Analysis Methodology & Data Sources", which also includes the description and sources of the baseline population estimates.
  • 800+ Downloads
    Time Period of the Dataset [?]: January 01, 2018-December 31, 2023 ... More
    Modified [?]: 28 November 2023
    Dataset Added on HDX [?]: 19 December 2018
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    This dataset contains the people in need by sector and region. The dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners.
  • 1300+ Downloads
    Time Period of the Dataset [?]: January 01, 2015-December 31, 2024 ... More
    Modified [?]: 27 November 2023
    Dataset Added on HDX [?]: 25 March 2019
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    This data has been produced by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. The data provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance. It represents a consolidated evidence base and helps inform joint strategic response planning.
  • 40+ Downloads
    Time Period of the Dataset [?]: April 30, 2023-April 30, 2023 ... More
    Modified [?]: 9 June 2023
    Dataset Added on HDX [?]: 9 June 2023
    This dataset updates: Every year
    Haiti - Cartographie de la sévérité des contraintes d'accès
  • 100+ Downloads
    Time Period of the Dataset [?]: March 01, 2022-March 28, 2024 ... More
    Modified [?]: 10 February 2023
    Dataset Added on HDX [?]: 10 February 2023
    This dataset updates: Every week
    This is a collection of all the available data on Turkiye and Syria in the ACAPS database as of 10.02.2023. The collection puts together the extraction of data from the following datasets and products: INFORM Severity Index (TUR005, SYR002, REG015 crises) ACAPS Humanitarian Access (TUR005, SYR002, REG015) Humanitarian Access Events Dataset Events Timeline Seasonal Calendar ACAPS Risk List Protection Monitoring Dataset* this product is not yet publicly available, but they will be published as standalone global products in the next weeks. ACAPS supports the earthquake response with a comprehensive data repository, providing both baseline pre-earthquake data, and in-crisis data. All these datasets are available with more frequent releases on the ACAPS API Please refer to http://www.acaps.org for the detailed codebooks of each dataset. For any inquiry please reach out to info@acaps.org
  • 300+ Downloads
    Time Period of the Dataset [?]: June 21, 2022-November 21, 2022 ... More
    Modified [?]: 20 July 2022
    Dataset Added on HDX [?]: 14 April 2021
    This dataset updates: Every six months
    Priorisation du cluster nutrition qui se base sur plusieurs facteurs aggravants pour permettre de mettre en avant les zones de santé les plus sensibles à la malnutrition alors que les données provenant des enquêtes nutritionnelles sont vieillissantes pour certaine Zone de santé.
  • 80+ Downloads
    Time Period of the Dataset [?]: March 24, 2021-March 28, 2024 ... More
    Modified [?]: 31 December 2021
    Dataset Added on HDX [?]: 7 June 2021
    This dataset updates: Every year
    Iraq severity of humanitarian conditions by districts. The severity score provides a rating from 1 (low severity of humanitarian need) to 5 (high severity of humanitarian need).
  • 40+ Downloads
    Time Period of the Dataset [?]: January 01, 2019-December 31, 2022 ... More
    Modified [?]: 28 December 2021
    Confirmed [?]: 28 December 2021
    Dataset Added on HDX [?]: 28 December 2021
    This dataset updates: Every year
    Ce jeu de données porte sur la sévérité des besoins dans plusieurs secteurs dont la santé, l'éducation, la nutrition, la sécurité alimentaire et la protection.
  • 600+ Downloads
    Time Period of the Dataset [?]: January 01, 2018-December 31, 2022 ... More
    Modified [?]: 21 December 2021
    Dataset Added on HDX [?]: 20 March 2018
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.
  • 300+ Downloads
    Time Period of the Dataset [?]: January 01, 2020-December 31, 2021 ... More
    Modified [?]: 24 July 2021
    Confirmed [?]: 16 December 2021
    Dataset Added on HDX [?]: 14 February 2020
    This dataset updates: Every year
    This file contains the severity of needs analysis prepared for the humanitarian needs overview. The severity score indicates how compounded the humanitarian needs in a woreda are. The higher the severity score of a woreda, the more severe, time-critical and compounded the needs are.
  • 90+ Downloads
    Time Period of the Dataset [?]: January 01, 2021-March 28, 2024 ... More
    Modified [?]: 15 July 2021
    Confirmed [?]: 31 December 2021
    Dataset Added on HDX [?]: 15 July 2021
    This dataset updates: Every year
    The dataset contains severity score of needs per "zone de santé" (admin level 3).
  • 50+ Downloads
    Time Period of the Dataset [?]: January 01, 2021-December 31, 2021 ... More
    Modified [?]: 15 June 2021
    Dataset Added on HDX [?]: 15 June 2021
    This dataset updates: Every year
    Zimbabwe Joint severity analysis.
  • 100+ Downloads
    Time Period of the Dataset [?]: January 01, 2021-December 31, 2021 ... More
    Modified [?]: 2 June 2021
    Confirmed [?]: 23 December 2021
    Dataset Added on HDX [?]: 2 June 2021
    This dataset updates: Every year
    This dataset is part of the data series [?]: Humanitarian Needs Overview
    This data is a snapshot of the humanitarian situation in Burkina Faso.
  • 90+ Downloads
    Time Period of the Dataset [?]: January 01, 2019-January 01, 2019 ... More
    Modified [?]: 8 November 2019
    Confirmed [?]: 8 November 2019
    Dataset Added on HDX [?]: 8 November 2019
    This dataset updates: Every year
    The file contains overall severity of needs analysis by woreda (admin level3) for 2019 HNO. It has severity of needs as of January 2019 and revised severity as of July 2019.
  • 600+ Downloads
    Time Period of the Dataset [?]: March 24, 2019-March 29, 2019 ... More
    Modified [?]: 2 April 2019
    Dataset Added on HDX [?]: 2 April 2019
    This dataset updates: Never
    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.
  • 300+ Downloads
    Time Period of the Dataset [?]: December 26, 2016-December 26, 2016 ... More
    Modified [?]: 9 August 2018
    Dataset Added on HDX [?]: 26 December 2016
    This dataset updates: Never
    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.
  • 100+ Downloads
    Time Period of the Dataset [?]: July 21, 2018-July 21, 2018 ... More
    Modified [?]: 21 July 2018
    Dataset Added on HDX [?]: 21 July 2018
    This dataset updates: Every month
    Dataset covers Shelter and Non-food Items needs severity mapping by Local Government Area (LGA) as of June 2018. Dataset covers Borno, Yobe and Adamawa, the three crisis-affected states; Shelter needs severity mapping by Local Government Area (LGA) as of June 2018. Dataset covers Borno, Yobe and Adamawa, the three crisis-affected states; Non-food Items needs severity mapping by Local Government Area (LGA) as of June 2018. The zipped shapefile covers Borno, Yobe and Adamawa, the three crisis-affected states; and a CSV dataset containing Shelter and Non-food Items (NFI) needs severity mapping combined, by Local Government Area (LGA) as of June 2018, covering the three crisis-affected states of Borno, Yobe and Adamawa.
  • 400+ Downloads
    Time Period of the Dataset [?]: June 01, 2017-June 01, 2017 ... More
    Modified [?]: 8 June 2017
    Dataset Added on HDX [?]: 2 June 2017
    This dataset updates: Never
    Product This priority index was derived by combining a detailed flood extent mapping with detailed human settlement geo-data. Both sources were combined to produce the location and magnitude of population living in flooded areas. This was subsequently aggregated to admin-4 areas (GND) as well as admin-3 areas (DS divisional). The flood extent mapping was derived in turn by combining two sources: Flood extent maps could be produced rather faster using satellite imageries captured by either optical sensors or Synthetic Aperture Radar (SAR) sensors. In most places flood is cause by heavy rainfall which means in most cases cloud is present, this is a limitation for optical sensors as they can’t penetrate clouds. Radar sensors are not affected by cloud, which make them more useful in presence of cloud. In This analysis we analyzed sentinel2 optical image from May 28th and Sentinel 1 SAR image from May 30th. Then we combine the two results adding up the flood extents. Main cloud covered areas and permanent water bodies are removed from the flood extent map using the Sentinel 2 cloud mask. The scale/resolution of the flood extent map is 30mts where as the permanent water body map has 250m scale resolution. This will introduce some discrepancy: part of flood extent map could be permanent water body. Scope Analysis focused on 4 districts in South-West Sri Lanka based on news reports (https://www.dropbox.com/s/n0qdqe7qfgq6fyv/special_situation.pdf?dl=0). Based on the admin-3-level analysis, highest percentages of population living in flooded areas were seen in Matara district. Admin-4 level analysis concentrated only on Matara district for that reason. Caveats The dataset is showing percentage flooded. The data has not yet been corrected for small populations. We believe the product is currently pointing to the high priority areas. In the shp or csv files the user of this data could easily correct for small populations, if there is a wish to target on the amount of people affected. Data used from partners The human settlement data was retrieved from http://ciesin.columbia.edu/data/hrsl/. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-06-2017. The Radar imagery analysis was done by NASA JPL, whose input in this product has been crucial. Visualization An example map is available here: http://bit.ly/SriLankaFloodMap Linked data Admin boundaries 3 and 4 can be found here (link on OBJECT_ID): https://data.humdata.org/group/lka?q=&ext_page_size=25&sort=score+desc%2C+metadata_modified+desc&tags=administrative+boundaries#dataset-filter-start How to use The ratio column in the SHPs or CSVs can be multiplied by 100 to get the percentage of flooding in the area.
  • 500+ Downloads
    Time Period of the Dataset [?]: November 22, 2016-November 22, 2016 ... More
    Modified [?]: 7 December 2016
    Dataset Added on HDX [?]: 21 October 2016
    This dataset updates: Never
    Blog post about this prediction can be found here: http://bit.ly/2fWF2jq The predicted priority index of Typhoon Haima is produced by a machine learning algorithm that was trained on four past typhoons: Haiyan, Melor, Hagupit and Rammasun. 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 people affected and houses damaged. First run The Priority Index is a 1-5 classification that can be used to identify the worst hit areas: those that need to be visited for further assessments or support first. Second run The model now predicts two things: 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 precentage of total damage (damaged houses versus all houses) 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. 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 (km/h) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) For the second run of the algorithm we also included: Roof and wall materials New geographical features The result of different models can be found in the file 'Typhoon Haima - performance of different models - second run.csv' A note on how to interpret this. date running date alg_date same alg_model name of the algorithm used alg_predict_on name of the learning variable alg_use_log i s the learning variable transformed in log code_version version of the learn.py code 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.