Lebanon Crisis

FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.

OpenStreetMap contains roughly 741.0 thousand buildings in this region. Based on AI-mapped estimates, this is approximately 66% of the total buildings.The average age of data for this region is 1 year, 11 months ( Last edited a month ago ) and 22% buildings were added or updated in the last 6 months. Read about what this summary means : indicators , metrics This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['building'] IS NOT NULL Features may have these attributes: name name:en building building:levels building:materials addr:full addr:housenumber addr:street addr:city office source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap contains roughly 47.7 million km of roads in this region. Based on AI-mapped estimates, this is approximately 86 % of the total road length in the dataset region. The average age of data for the region is 1 year, 11 months ( Last edited a month ago ) and 14% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['highway'] IS NOT NULL Features may have these attributes: name name:en highway surface smoothness width lanes oneway bridge layer source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: name name:en railway ele operator:type layer addr:full addr:city source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city') Features may have these attributes: name name:en place population is_in source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: name name:en amenity building port operator:type addr:full addr:city source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['aeroway'] IS NOT NULL OR tags['building'] = 'aerodrome' OR tags['emergency:helipad'] IS NOT NULL OR tags['emergency'] = 'landing_site' Features may have these attributes: name name:en aeroway building emergency emergency:helipad operator:type capacity:persons addr:full addr:city source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] IN ('kindergarten', 'school', 'college', 'university') OR tags['building'] IN ('kindergarten', 'school', 'college', 'university') Features may have these attributes: name name:en amenity building operator:type capacity:persons addr:full addr:city source name:ar This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This dataset contains data obtained from the HDX Humanitarian API (HDX HAPI), which provides standardized humanitarian indicators designed for seamless interoperability from multiple sources. The data facilitates automated workflows and visualizations to support humanitarian decision making. For more information, please see the HDX HAPI landing page and documentation.

Conflict and disaster population movement (flows) data for Lebanon. The data is the most recent available and covers a 180 day time period. Internally displaced persons are defined according to the 1998 Guiding Principles as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. The IDMC's Event data, sourced from the Internal Displacement Updates (IDU), offers initial assessments of internal displacements reported within the last 180 days. This dataset provides provisional information that is continually updated on a daily basis, reflecting the availability of data on new displacements arising from conflicts and disasters. The finalized, carefully curated, and validated estimates are then made accessible through the Global Internal Displacement Database (GIDD). The IDU dataset comprises preliminary estimates aggregated from various publishers or sources.

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, health care services and refugee and IDP camps in Lebanon. They also provide information on incidents of explosive weapons use affecting aid access, education and health care services. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

This dataset contains Food Prices data for Lebanon, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

This dataset comes from the International Organization for Migration (IOM)'s displacement tracking matrix (DTM) publicly accessible API. This API allows the humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by DTM. The DTM API only provides non-sensitive IDP figures, aggregated at the country, Admin 1 (states, provinces, or equivalent), and Admin 2 (smaller subnational administrative areas) levels. For more detailed information, please see the country-specific DTM datasets on HDX.

This dataset contains data from WHO's data portal covering the following categories: Adolescent, Ageing, Air pollution, Assistive technology, Child, Child mortality, Cross-cutting, Dementia diagnosis, treatment and care, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, Global strategy, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and newborn, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics. For links to individual indicator metadata, see resource descriptions.

A weekly dataset providing the total number of reported political violence, civilian-targeting, and demonstration events in Lebanon. Note: These are aggregated data files organized by country-year and country-month. To access full event data, please register to use the Data Export Tool and API on the ACLED website.

This dataset contains agency- and open source events in which aid access, education and healthcare services were impacted by explosive weapons. Categorized by country. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org This data collection is funded and supported by the European Union. The opinions expressed in it do not reflect in any way the position of the EU who are not responsible for the content expressed in this document. Insecurity Insight collaborates with the International Network on Explosive Weapons (INEW) in producing research and analysis on the harm and use of explosive weapons for the Explosive Weapons Monitor by documenting their effects on health care, education, and aid access.

The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports. There is also a global dataset.

Exported data from the Lebanon Assessment, Survey and Evaluation Registry(LASER). It contains planned, on-going and completed assessments, surveys and evaluations in Lebanon.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between **paved** and **unpaved** surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the [paper](http://arxiv.org/abs/2410.19874) Roughly 0.0517 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0099 and 0.002 (in million kms), corressponding to 19.1793% and 3.8679% respectively of the total road length in the dataset region. 0.0398 million km or 76.9528% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0001 million km of information (corressponding to 0.2769% of total missing information on road surface) It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications. This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications. AI features: pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved." pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved). osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved." combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved." combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved." n_of_predictions_used: Number of predictions used for the feature length estimation. predicted_length: Predicted length based on the DL model’s estimations, in meters. DL_mean_timestamp: Mean timestamp of the predictions used, for comparison. OSM features may have these attributes(Learn what tags mean here): name: Name of the feature, if available in OSM. name:en: Name of the feature in English, if available in OSM. name:* (in local language): Name of the feature in the local official language, where available. highway: Road classification based on OSM tags (e.g., residential, motorway, footway). surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt). smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad). width: Width of the road, where available. lanes: Number of lanes on the road. oneway: Indicates if the road is one-way (yes or no). bridge: Specifies if the feature is a bridge (yes or no). layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels). source: Source of the data, indicating the origin or authority of specific attributes. Urban classification features may have these attributes: continent: The continent where the data point is located (e.g., Europe, Asia). country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States). urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban) urban_area: Name of the urban area or city where the data point is located. osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature. osm_type: Type of OSM element (e.g., node, way, relation). The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer. This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information. We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Lebanon administrative level 4 (Beirut neighborhoods) boundary shapefile and geodatabase

Lebanon administrative level 0-3 boundaries (COD-AB) dataset. These administrative boundaries were established in: 2014 NOTE: Please see caveats pertaining to the P-coding system. This COD-AB was most recently reviewed for accuracy and necessary changes in November 2024. The COD-AB requires improvements. Sourced from Council for Development and Reconstruction (CDR) Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID. There is no suitable population statistics dataset (COD-PS) for linkage to this COD-AB.. An edge-matched (COD-EM) version of this COD-AB is available on HDX here. Please see the COD Portal. Administrative level 1 contains 8 feature(s). The normal administrative level 1 feature type is ""Governorate (Mouhafaza)"". Administrative level 2 contains 26 feature(s). The normal administrative level 2 feature type is ""District (Kaza)"". Administrative level 3 contains 1,627 feature(s). The normal administrative level 3 feature type is ""Cadaster (Manatek Ikaria)"". Recommended cartographic projection: Asia South Albers Equal Area Conic This metadata was last updated on January 9, 2025.

Since October 8 there has been an increase in cross-border incidents between Israel and Lebanon, resulting in the displacement of people both within the South and elsewhere within the country. Since October 10, the Displacement Tracking Matrix (DTM) has been conducting the daily monitoring of population movements. The objective of the exercise is to inform preparedness and response planning.

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working and what they are doing in order to identify gaps and plan for future humanitarian response. This dataset includes a list of humanitarian organizations operating in Lebanon at administrative level 2.

Lebanon population density for 400m H3 hexagons. Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution. Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

Lebanon administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata. Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population