This dataset shows humanitarian access constraints in the Borno, Adamawa and Yobe states.

Ce jeu de données porte sur l'accès physique dans les communes en République Centrafricaine.

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education, food security, health care services and refugee and IDP camps in Syria. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, and health care services in Pakistan. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, food security and health care services in Somalia. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, and health care services in Haiti. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, and health care services in Iraq. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, food security and health care services in Niger. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, health care services and food security in Mali. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education, food security, health care services and refugee and IDP camps in Yemen. They also provide information on incidents of explosive weapons use affecting aid access, education and health care services. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education, health care services and refugee and IDP camps in Nigeria. They also provide information on incidents of conflict related sexual violence (CRSV). Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education, food security, health care services and refugee and IDP camps in the DRC. They also provide information on incidents of conflict related sexual violence (CRSV). Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education, health care services and refugee and IDP camps in South Sudan. They also provide information on incidents of conflict related sexual violence (CRSV). Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education and health care services in Cameroon. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education and health care services in Burkina Faso. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education and health care services in Afghanistan. They also provide information on incidents of explosive weapons use affecting aid access, education and health care services. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

These datasets contain information on reported incidents of violence and threats affecting aid operations and workers, education, food security, health care services and refugee and IDP camps in Sudan.They also provide information on incidents of conflict related sexual violence (CRSV) and explosive weapons use affecting aid access, education and health care services. Also included are datasets cited in the Safeguarding Health in Conflict Coalition (SHCC)'s annual reports. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org

Perception of access constraint according to humanitarian partners in eight regions of Burkina Faso.

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 Users can specify time period and download from: https://www.acaps.org/en/data#dataset-3

After 13 years of relentless conflict, Syria face severe humanitarian challenges driven by deteriorating infrastructure, disrupted livelihoods, and inadequate essential services. With the recent fall of Assad’s regime and Syria’s liberation, the Rapid Needs Assessment (RNA) underscores pressing multisectoral needs in education, food security, WASH, protection, livelihoods, health, and shelter. Key priorities include restoring electricity, enhancing education, ensuring security, delivering food and water, and addressing critical health and shelter needs. These findings highlight the urgency of coordinated interventions to tackle the complex crises affecting communities in Syria.

The dataset provides an overview of the material entry process through Rafah and Kerem Shalom crossings. It gives insight into the progress of supplies and dispatched items as well as the vital manifest details associated with them.

This dataset contains analysis of humanitarian accessibility in Ethiopia at Woreda level. The analysis is produced in consultation with aid partners in the Access Working Group and OCHA Access field focal points/sub-national Access Working Groups. Note: The Access maps and accessibility data are prepared based on the information gathered from the humanitarian partners and other reliable sources on the ground. It depicts the general situation regarding access in Ethiopia down to the woreda level during the reporting period. By the time of the publication, the conditions on the ground might have changed and this could be reflected on the next iteration.

The number of humanitarian incidents between since January 2024, compiled by OCHA based on number of incidents reported by humanitarian access working group (HAG). The numbers are expected to change as new assessment figures become available.

L’analyse des contraintes d’accès à l’est de la RDC a été conduite au travers de consultations séparées avec les acteurs humanitaires - répartis par type d’organisation (ONGI, ONGN, NU). Elle donne une indication des difficultés rencontrées d’une part par les partenaires humanitaires pour atteindre les populations dans le besoin, et d’autre part pour ces populations d’accéder à l’assistance humanitaire et services fournis par ces organisations.

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 0.1691 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 0.0696 and 0.0099 (in million kms), corressponding to 41.182% and 5.8528% respectively of the total road length in the dataset region. 0.0896 million km or 52.9652% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0013 million km of information (corressponding to 1.451% 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.