Syrian Arab Republic GIS data

geoBoundaries ADM0 boundaries for Syrian Arab Republic: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

Date of Images: Post-Event: 2/7/2023, 2/9/2023, 2/11/2023, 2/12/2023, 2/14/2023, 2/16/2023, 2/17/2023, 2/19/2023, 2/21/2023, 2/22/2023, 2/24/2023 Pre-Event: 12/28/2022, 1/10/2023, 1/22/2023, 1/25/2023, 1/28/2023 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The Natural Color RGB provides a false composite look at the surface. This RGB uses a shortwave, the near-infrared, and red channels from the instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. For the Natural Color RGB, areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow covered areas in cyan. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color: 10 meters Natural Color: 20 meters Credits: NASA/MSFC, USGS, ESA Copernicus Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=5bd8e58267b5457e8f9f0299e30055ce WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202302_EARTHQUAKE_TURKIYE/2302_sentinel2/ImageServer/WMSServer

Date of Image: 2/8/2023, 2/9/2023, 2/10/2023 Date of Next Image: Unknown Summary: 2/8/2023 (EOS-RS, JAXA ALOS-2) The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Turkiye (Turkey) and Syria due to the M7.8 and M7.5 earthquakes that occurred on 6 Feb 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the ALOS-2 satellite operated by the Japan Aerospace Exploration Agency (JAXA) before (7 Apr 2021 and 6 Apr 2022) and after (8 Feb 2023) the event. 2/9/2023 (EOS-RS, Copernicus Sentinel-1) The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Turkiye (Turkey) and Syria due to the M7.8 and M7.5 earthquakes that occurred on 6 Feb 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (12 Oct 2022 to 28 Jan 2023) and after (9 Feb 2023) the event. 2/10/2023 (EOS-RS, Copernicus Sentinel-1) The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Turkiye (Turkey) and Syria due to the M7.8 and M7.5 earthquakes that occurred on 6 Feb 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (13 Oct 2022 to 29 Jan 2023) and after (10 Feb 2023) the event. Suggested Use: Standard Version: The color variation from pale yellow to red indicates increasingly more significant surface change (drop in radar reflection coherence). Preliminary validation was done by comparing with high-resolution optical imagery and media reports. Color Vision Deficiency (CVD) Version: The color variation from light blue to dark blue indicates increasingly more significant surface change. Preliminary validation was done by comparing with high-resolution optical imagery and media reports. NOTE: This damage proxy map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated areas. The time intervals between the ALOS-2 acquisitions are up to a year apart, so the accuracy of the DPM may be lower in areas of vegetation, such as in the mountains. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage. Satellite/Sensor: Japan Aerospace Exploration Agency (JAXA) ALOS-2 PALSAR-2 Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) Resolution: 30 meters Credits: Original data ALOS-2 PALSAR-2 Product - JAXA (2021-2023). Data were provided by Sentinel Asia and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) in collaboration with NASA-JPL and Caltech. The product contains modified Copernicus Sentinel data (2022-2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) in collaboration with NASA-JPL and Caltech, using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=9d7145c545c6407680b368601b9a611e WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202302_EARTHQUAKE_TURKIYE/2302_opera_rs_dpm/ImageServer/WMSServer Data Download: http://eos-rs-products.earthobservatory.sg/EOS-RS_202302_Turkiye_Syria_Earthquake/

geoBoundaries ADM3 boundaries for Syrian Arab Republic: 272 units, representing 2017. Source: United Nations Cartographic Section (UNCS) and United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), OCHA ROSC.

geoBoundaries ADM2 boundaries for Syrian Arab Republic: 61 units, representing 2017. Source: United Nations Cartographic Section (UNCS) and United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), OCHA ROSC.

Date of Image: 2/10/2023 Date of Next Image: Unknown Summary: 2/10/2023 (ARIA, Copernicus Sentinel-1) The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology in Southern California created this Damage Proxy Map (DPM) depicting areas that are likely damaged by the large earthquakes (Feb. 06, 2023) in Turkiye (Turkey) and Syria. The map was derived from synthetic aperture radar (SAR) images on Feb. 10, 2023 by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA). The pre-event images were taken from Sep. 19, 2022 to Jan. 29, 2022). Suggested Use: Standard Version: The color variation from pale yellow to red indicates increasingly more significant surface change (drop in radar reflection coherence). Preliminary validation was done by comparing with high-resolution optical imagery and media reports. NOTE: This damage proxy map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated and mountainous areas. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage. Satellite/Sensor: Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) Resolution: 30 meters Credits: Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2022-2023), processed by ESA and analyzed by the NASA-JPL/Caltech ARIA team in collaboration with Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). Part of the funding was provided by NASA's Earth Applied Sciences Disasters Program. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=4e684f9ad7e74e5d991cb011ca0d07ad WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202302_EARTHQUAKE_TURKIYE/2302_opera_dpm/ImageServer/WMSServer Data Download: https://aria-share.jpl.nasa.gov/20230206_Turkey_EQ/DPM/

geoBoundaries ADM1 boundaries for Syrian Arab Republic: 14 units, representing 2017. Source: United Nations Cartographic Section (UNCS) and United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), OCHA ROSC.

Date of Images: 2/4/2023, 2/5/2023, 2/9/2023, 2/10/2023, 2/16/2023, 2/17/2023 Date of Next Image: None Expected Summary: The Alaska Satellite Facility has developed water extent images using the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument. These images can be used to see where water is located at the time of the satellite overpass. This product shows all water detected and does not differentiate between normal water and flood water. To determine where flooding may have occurred, combine this layer with a reference water layer. Additional SAR Information Suggested Use: This product shows all water detected and does not differentiate between normal water and flood water. To determine where flooding may have occurred, combine this layer with a reference water layer. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satellite Resolution: 30 meters Credits: Sentinel data used in this derived product, contains modified Copernicus Sentinel data (2023), processed by ESA, Alaska Satellite Facility, NASA MSFC Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=c9225c80d734497a8a8509383feb65b8 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202302_EARTHQUAKE_TURKIYE/2302_sentinel1_water/ImageServer/WMSServer

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Syrian Arab Republic **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.1947 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0052 million km (2.6901%) - Unpaved roads: 0.009 million km (4.614%) About 0.1805 million km (92.6958%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.0009%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Syrian Arab Republic administrative level 0-3 boundaries (COD-AB) dataset version 02. This dataset is structured into 3 levels: - Admin 1: 14 Governorate - Admin 2: 62 District - Admin 3: 272 Sub-district Dates associated with this dataset: - 01 April 2024: dataset reviewed for accuracy and completeness - 17 December 2020: valid for use by the humanitarian community - 01 January 1960: last edit to the dataset before publishing - 01 January 1960: boundaries created by the source Contributed by OCHA Syria. Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

Forward-looking analysis representing the most likely food security outcomes for the near term (August 2026 - September 2026) and medium term (October 2026 - January 2027) periods.

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Eight Geojson spatial layer of Syria power line and generator and power plant and substation Two Geojson spatial layer of Syria Natural Water and waterway dam Four Geojson spatial layer of Syria highway /motorway / link /primary / trunk

Using 10m resolution multispectral imagery from the Sentinel-2A satellite, a relativized burn ratio (RBR) was calculated and used for burned land classification to assess and quantify areas which have been burned between 4 May and 30 June 2019 in the governorates of Al-Hasakeh, Deir-ez-Zor, Ar-Raqqa, Aleppo, Idleb and Northern Hama. The before and after images are composites ranging from 6 April to 3 May 2019 and 27 to 30 June 2019.

**UNOSAT code EQ20230206SYR, GDACS Id: 1357372** This map illustrates the potentially damaged structures/buildings affected by the 10h of February 2023 earthquake in Jindires and in the surrounding area in Afrin district (Aleppo governorate), Syria as detected from a Pleiades very highresolution satellite image acquired on 10 February 2023. Within the 17 locations (including the city of Jindires), UNOSAT identified 310 damaged structures and 470 potentially damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code EQ20230206SYR, GDACS Id: 1357372** This map illustrates the potentially damaged structures/buildings affected by the 6th of February 2023 earthquake in Al Bab and in Al Bab district (Aleppo governorate), Syria as detected from a Pleiades very high resolution satellite image acquired on 11 February 2023. UNOSAT identified 16 damaged structures and 85 potentially damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code EQ20230206SYR, GDACS Id: 1357372** This report provides satellite-based comprehensive damage assessment results over Syria following the 06th of February 2023 Marash/Antep earthquakes sequence which struck southern Türkiye and north-western parts of Syria. The mainshock measured a magnitude of 7.8 and occurred at a crucial moment, 4:15 am local time, when most people were at home sleeping. It was a shallow depth earthquake with an epicentre located about 35 km north-west of the Turkish city of Gaziantep. These earthquakes caused more than 46,000 deaths in Türkyie and over 4,500 in northwest Syria. The humanitarian community estimated that 8.8 million people live in the most affected areas in Syria. Overall, 170 sub-districts in 43 districts in 10 of Syria’s governorates have been impacted by the earthquake. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.