AndreaBozzo/ceres-open-data-index huggingface dataset:
IOM Bangladesh - Needs and Population Monitoring (NPM) Round 9 Site Assessment

Following an outbreak of violence on 25 August 2017 in Rakhine State, Myanmar, a new massive influx of Rohingya refugees to Cox’s Bazar, Bangladesh started in late August 2017. Most of the Rohingya refugees settled in Ukhia and Teknaf Upazilas of Cox’s Bazar, a district bordering Myanmar identified as the main entry area for border crossings.

This assessment presents data from NPM Round 9 baseline survey and site assessment: the baseline was conducted between 5 and 20 February 2018; the site assessment between 7 and 25 March.


Related majhee blooks shapefiles are available on [HDX](https://data.humdata.org/dataset/iom-bangladesh-needs-and-population-monitoring-npm-majhee-blocks-mapping).
For further information, the full report and related information package are available on [humanitarianresponse.info](https://www.humanitarianresponse.info/en/operations/bangladesh/assessment/iom-bangladesh-needs-and-population-monitoring-npm-site-0).




Tags: displacement, needs assessment, refugee crisis, refugees
Organization: International Organization for Migration (IOM)
Nigeria - IOM DTM R3-R7 datasets - Baseline and Site assessment data

Since May 2013, Nigeria has seen an intensification of conflict due to Boko Haram attacks in its north east states of Adamawa, Bauchi, Borno, Gombe, Taraba and Yobe. Insurgency and counter-insurgency have inevitably resulted in the displacement of people across the troubled states. In response to the need for accurate information on internally displaced persons (IDPs), the International Organization for Migration (IOM) began implementing the Displacement Tracking Matrix (DTM) project in July 2014. The objective of the project is to support the Government of Nigeria in establishing a comprehensive system to collect and disseminate data on IDPs by strengthening the capacity of the National Emergency Management Agency (NEMA), the State Emergency Management Agencies (SEMAs) and other humanitarian actors to conduct assessments on IDPs in a unified and systematized manner. The activities of the DTM project, which consist of conducting baseline assessments and registration for IDPs living in camps and host communities, are currently being carried out in Adamawa, Bauchi, Borno, Gombe, Taraba and Yobe. The information collected will contribute to the provision of a comprehensive profile of the IDP population in Nigeria which will be shared with all relevant stakeholders and will contribute towards enabling the government of Nigeria and humanitarian partners identify the needs of Nigeria’s displaced population and develop interventions for providing IDPs necessary assistance. The project is funded by the Office of Disaster Assistance of the United States Agency for International Development and the European Commission's Humanitarian Aid and Civil Protection Department.

Tags: baseline population, complex emergency-conflict-security, displacement, internally displaced persons-idp
Organization: International Organization for Migration (IOM)
Egypt - Administrative Boundaries, Populated Places

**Summary:** Admin boundaries (1,2,3)



**Abstract:** Admin boundaries level 1 (Province), level 2 (District), level 3 (Sub-district) (Geodatabase format)

Source: Central Agency for Public Mobilization and Statistics (CAPMAS)





Tags: administrative boundaries-divisions, geodata, populated places-settlements
Organization: OCHA Middle East and North Africa (ROMENA)
Libya - Admin Level 4

Updated by IOM’s Displacement Tracking Matrix in coordination with OCHA and Libya’s Ministry of Planning Bureau of Statistics (BSc) this dataset provides the latest GPS coordinates and both the English and Arabic spellings of the Administrative level 4 (Location) of Libya. Developed by the Bureau of Statistics and Census the CODs for the Administrative level 4 (locations) provides data on 667 Muhallas). A Muhalla can be one village or a small collection of villages in rural settings, whilst in urban settings it equates to a neighbourhood. As with the Baladiyas, there are some diversions about the total number of Muhallas and how they are administratively linked to the Baladiyas. Although it is acknowledged that clarification of administrative division in Libya are still ongoing and the number of administrative areas is subject to change the current CODs provide all partners with a common operational geographic understanding of the country. The names and boundaries of the CODs do not imply official endorsement. 

Tags: geodata, populated places-settlements
Organization: OCHA Middle East and North Africa (ROMENA)
occupied Palestinian territory (oPt) - 2022 Multi-Sectoral Needs Assessment

The 2022 Multi-Sector Needs Assessment (MSNA), conducted by the REACH Initiative in close collaboration with the United Nations Office for the Coordination of Humanitarian Affairs (OCHA), aims to identify and assess multi-sectoral and sector-specific needs, circumstances, and vulnerabilities of households across the entirety of the Occupied Palestinian Territories (oPt). The assessment is designed to directly inform joint humanitarian-development planning across the oPt through the provision of comprehensive, multi-sectoral data and inter-sectoral analysis. In particular, the MSNA serves as a critical information resource for Humanitarian Program Cycle (HPC) activities, principally the development of the Humanitarian Needs Overview (HNO) and the Humanitarian Response Plan.

All data collection activities for the MSNA were conducted by the Palestinian Central Bureau for Statistics (PCBS) and took place from May 30 to July 6. A total of 8,331 households were assessed, of which 4,152 were in the Gaza Strip and 4,179 in the West Bank, including 244 in East Jerusalem.

Analysis findings at the strata level, meaning the localities of the Gaza Strip, the governorates of the West Bank according to “Oslo Area”, East Jerusalem, and H2, are statistically representative with a 95% level of confidence and a +/- 9% margin of error. Findings at the governorate level, for both the West Bank and Gaza, are representative at a higher degree of accuracy.

Tags: affected population, needs assessment
Organization: REACH Initiative
2019 Somalia Joint Multi Cluster Needs Assessment

Primary data will be collected by means of a household-level survey designed with the participation of the humanitarian clusters in Somalia. Cluster leads are asked to outline information gaps and the type of data required to inform their strategic plans. Key indicators are developed by REACH with the substantive input of participating partners, and subsequently validated by the clusters. REACH will draft the household survey tool through an iterative consultation process with cluster partners and OCHA and is aligned, as much as possible, with the Joint Inter-Sectoral Analysis Framework (JIAF) which will serve as a common and structured method for assessing the severity of needs across different clusters.

The assessment will use stratified cluster sampling at the district level using settlements as the clusters and households as the unit of measurement. For some districts, 2-stage stratified random sampling will be used instead of stratified cluster sampling for large urban centres, if it proves to be more efficient and logistically feasible for data collection. The sample will be stratified by population group, disaggregated by non-displaced communities, and IDP settlements; the sample will be further stratified by district to ensure coverage and comparison across the entire country (with the exception of inaccessible areas). In the case of cluster sampling, the minimum cluster size will be set to 6 households. The sample size will be adjusted for the design effect and will enable generalisation of the results to each of the two population strata in each district, with a 90% confidence level and a 10% margin of error.

Tags: demographics, education, health, needs assessment, population, shelter
Organization: REACH Initiative
REACH_Afghanistan_Humanitarian_Situation_Monitoring_Pilot_KII_Dataset

The HSM Pilot aims to monitor how the needs in the districts that were assessed under the Whole of Afghanistan Assessment (WoAA 2021) are evolving under the rapidly changing context in Afghanistan, whilst providing sufficient longitudinal data to allow for the development of an analytical framework that would allow a regular monitoring of humanitarian needs. This pilot is a one-time assessment designed to demonstrate the utility of a broader HSM designed and led by the humanitarian response in Afghanistan. HSM would aim to inform both the geographical and sectoral prioritization to inform a tailored and evidence-based response. To determine the assessment framework, an adapted Hard to Reach (HtR) methodology was used. At least three key informant interviews (KIIs), in a number proportionate to the number of settlements, were conducted in each Basic Service Unit (BSU). To determine a BSU, participatory mapping of settlements and services available were conducted prior to data collection. Key informant networks were developed to cover the entirety of selected districts, stratifying each district’s network by BSU to ensure a minimum of 3 KIs per BSU, each with information covering a different settlement than the others. Any analysis conducted using the data in this file would generate statistically non-representative results, as the key informant methodology is indicative. 

Tags: displacement, education, food security, health, internally displaced persons-idp, water sanitation and hygiene-wash
Organization: REACH Initiative
REACH_Afghanistan_Humanitarian_Situation_Monitoring_Round1_KII_Dataset

Humanitarian Situation Monitoring aims to collect and then triangulate information regarding service provision, sectoral needs, and vulnerabilities in Afghan communities, in order to then support geographical and sectoral prioritizations within the 2022 humanitarian response in Afghanistan, particularly in light of the rapidly evolving context in Afghanistan. The key-informant-based HSM methodology uses settlements as the unit of analysis, using a structured survey tool to interview key informants about the situation in their settlement. A sampling frame covering all 419 districts in Afghanistan was used, covering a minimum of 10% of total settlements in each district. To achieve geographical spread across each district, at least three key informant interviews (KIIs), in a number proportionate to the number of settlements, were conducted in each Basic Service Unit (BSU). To determine a BSU (defined as an economic/geographic service unit which relies on the same services, i.e. healthcare clinics and schools, and common public spaces, i.e. markets and roads), participatory mapping of settlements and services available was conducted prior to data collection. Any analysis conducted using the data in this file will generate statistically non-representative results, as the key informant methodology is indicative

Tags: displacement, education, food security, health, internally displaced persons-idp, water sanitation and hygiene-wash
Organization: REACH Initiative
REACH_Afghanistan_Humanitarian_Situation_Monitoring_Round2_KII_Dataset

Humanitarian Situation Monitoring aims to collect and then triangulate information regarding service provision, sectoral needs, and vulnerabilities in Afghan communities, in order to then support geographical and sectoral prioritizations within the 2022 humanitarian response in Afghanistan, particularly in light of the rapidly evolving context in Afghanistan. The key-informant-based HSM methodology uses settlements as the unit of analysis, using a structured survey tool to interview key informants about the situation in their settlement. A sampling frame covering all 401 districts in Afghanistan was used, covering a minimum of 10% of total settlements in each district. To achieve geographical spread across each district, at least three key informant interviews (KIIs), in a number proportionate to the number of settlements, were conducted in each Basic Service Unit (BSU). To determine a BSU (defined as an economic/geographic service unit which relies on the same services, i.e. healthcare clinics and schools, and common public spaces, i.e. markets and roads), participatory mapping of settlements and services available was conducted prior to data collection. Any analysis conducted using the data in this file will generate statistically non-representative results, as the key informant methodology is indicative.

Tags: displacement, education, food security, health, internally displaced persons-idp, water sanitation and hygiene-wash
Organization: REACH Initiative
Japan: Building Footprints in the Noto Earthquake Area

A dataset of building footprints in Japan, in the area of the January 2024 Noto earthquake. Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. For more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq 

Tags: earthquake-tsunami
Organization: Google Research
Reunion Island: Building Footprints in Areas Impacted by Cyclone Belal

A dataset of building footprints of the Reunion Island, in the area of the January 2023 Belal Cyclone. Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. For more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq



Tags: cyclones-hurricanes-typhoons
Organization: Google Research
Bangladesh Floods - August 2017 - Flooding levels & Vulnerability

In this analysis we have combined several data sources around the floods in Bangladesh in August 2017. 

## Visualization

* See attached map for a map visualization of this analysis.
* See http://bit.ly/2uFezkY for a more interactive visualization in Carto.

## Situation

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).

Tags: geodata, population
Organization: Netherlands Red Cross - 510
Typhoon Nina - windspeed, rainfall and priority index

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. 

Tags: cyclones-hurricanes-typhoons, damage assessment, geodata, logistics, severity
Organization: Netherlands Red Cross - 510
Floods - Southern Malawi - March 2019

For the floods in Southern Malawi of March 2019, we have combined flood extent maps (Sentinel) with HRSL settlement/population grid. This results in a calculation of # of affected buildings/people per district. The results is shared through maps and in a shapefile.

### 1. Data sources
## Sentinel 1 
Imagery from 7th of March 2017
## Sentinel 2 
Imagery from 10th/12th/14th of March 2017
## HRSL population data
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 9 March 2019.

### 2.	Good to know
The flood extent for Nsanje district was separately added on March 14th, to the existing flood extent for the main area from March 12th.

### 3.	Methodology

## A.	Flood Extent Based on SAR
The following steps were used to detect flood extent(water/no water).
In SNAP tool the raw data downloaded from sci-hub Copernicus was processed to calibrate image for atmospheric correction, spike filter and terrain correction(This is mainly for Sentinel 1 data). Finally defining water no water based on a threshold applied on the corrected image. Defining a threshold is always a challenge in SAR image analysis for flood detection, we collected data from the field to define this threshold. 
For Sentinel 2 as a first step cloud filter was calculated by applying a combined threshold on Band 2 and Band 10. The cloud mask shown in the figure below didn’t capture shadows of clouds, these were miss interpreted by the flood algorithm as water/flood. To correct this areas with more cloud cover were clipped out with a polygon. 
To define water no water based on sentinel data we used NDWI index, the treshold is adjusted based on data collected from the field 
Validation points were collected by Field team tested different values and check if the threshold identified fits with observation. The complete methodology how to detect flooding based on Sentinel 1 data and SNAP toolbox is documented in ESA website.

## B.	Affected People
To calculate number of affected people per each admin level, flood extent map is combined with HRSL population data. This is done in two steps: First, in step 1, we calculate a raster, which multiplies the population grid with the flood grid, such that we are left with only "population in flooded area". This is done using raster calculator where population density raster was multiplied by flood extent raster, which has a value of 0 for no flood and 1 for flood areas. Note that the flood extent grid was first resampled to match it to the population grid. This whole exercise is repeated for settlement/buildings instead of population. 
Step 2: We apply zonal statistics per TA to calculate total number of buildings/people affected in each admin level. For each Admin level2 estimated number of affected people and affected houses are plotted in the map. The zonal statistics data used for plotting can be found in the shape file.

Tags: geodata
Organization: Netherlands Red Cross - 510
Lake Chad Basin Baseline Population

The data contains the latest estimated population of each administrative level 1 unit in the Lake Chad Basin.  Estimation is based on input from UNFPA and the most recently available census for each country. Data is encoded as utf-8.  The second row of the CSV contains [HXL](http://hxlstandard.org) tags.

Tags: baseline population, complex emergency-conflict-security, hxl
Organization: OCHA West and Central Africa (ROWCA)
Lake Chad Basin Crisis Displaced Persons

Displacement figures for the Lake Chad Basin Crisis.  Derived from http://ors.ocharowca.info/KeyFigures/KeyFiguresListingPublic.aspx.   Data is encoded as utf-8.  The second row of the CSV contains [HXL](http://hxlstandard.org) tags.



Tags: baseline population, complex emergency-conflict-security, displacement, hxl
Organization: OCHA West and Central Africa (ROWCA)
Senegal - geodatabase on admin boundaries, settlements, hydrology, transporation, land use and infrastructure

Admin boundaries (admin1, admin 2 and admin3 polygons and polylines), settlements with pcode iso2 and iso 3, transporation (roads, railways, airports, ports), Hydrology (river line), Infrastructures (Education, health, trade)

Tags: administrative boundaries-divisions, education, education facilities-schools, geodata, hydrology, populated places-settlements, roads, transportation
Organization: OCHA West and Central Africa (ROWCA)
Indonesia: Earthquake - 6.0M - Nov 2023

**ADAM ID: eq\_us6000lpyx**  Magnitude 6.0 earthquake at 115.83 depth occurred on Nov 22 2023 in 72km W of Danau. It impacted 8055 people. The epicentre was at latitude 1.7425 longitude 127.1526.

Tags: affected population, earthquake-tsunami
Organization: WFP Automated Disaster Analysis & Mapping