# Global Adaptation Layer {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

Book a Demo

{% endcolumn %} {% endcolumns %} The Global Adaptation Layer is a set of globally consistent, hyperlocal geospatial data layers on the adaptation capacity of any coordinate on Earth, enabling "ground truth" analysis of climate resilience down to the neighborhood or asset level. ## Our approach: Engineering a hyper-local global adaptation dataset Climate volatility is accelerating — making adaptation all the more essential. Against this backdrop, organizations are looking to deepen their understanding of *adaptation capacity* in order to determine their assets' resilience to shocks. However, existing global climate adaptation datasets lack sufficient specificity and resolution, limiting the ability of investors, corporations, and governments to gain localized knowledge at the asset level. This includes data on hazard-specific adaptations such as drainage capacity for floods or fire stations for wildfire — factors crucial to measuring climate resilience. AlphaGeo’s Global Adaptation Layer solves this problem at global scale and granularity. Using proprietary AI techniques for data extraction and downscaling, we have engineered multiple layers of globally consistent, hyperlocal climate adaptation indices at up to 30-meter resolution. These datasets offer a true-to-the-ground portrayal of the resilience of any location in the world. The Global Adaptation Layer currently includes global scores and underlying feature datasets for: 1. **Heat Stress Adaptation** (e.g., Building Density, Urban Greenery) 2. **Inland Flooding Adaptation** (e.g., Porosity, Proximity to Drainage Systems) 3. **Coastal Flooding Adaptation** (e.g., Coastal Defenses, Proximity to Natural Buffers) 4. **Hurricane Wind Adaptation** (e.g., Building Strength, Proximity to Flood Storage) 5. **Drought Adaptation** (e.g., Proximity to Water Treatment, Proximity to Water Storage) 6. **Wildfire Adaptation** (e.g., Proximity to Fire Response, Proximity to Fire Prevention) 7. **Hail Adaptation** (e.g., Building Strength, Proximity to Nature-Based Flood Defense) 8. **Landslide Adaptation** (e.g., Vegetation Cover, Proximity to Manmade Barriers Against Landslides) 9. **Earthquake Adaptation** (e.g., Building Strength, Building Sparsity) 10. **Societal Resilience** (e.g., Local GNI, Vulnerable Population) ## Methodology The Global Adaptation Layer is a globally consistent, high-resolution climate adaptation dataset engineered from a diverse set of geospatial data sources that provide information on the built environment at up to 30-meter resolution and are calibrated to each type of climate hazard. The overall methodology can be broken down into four steps, as shown in the flowchart below. The process is designed to be iterative, allowing insights generated at each stage to continuously inform and refine decisions across the wider workflow.
The diagram below illustrates in greater technical detail how spatial attributes are collected from raw data sources and mapped to thematic features before being transformed into adaptation scores at each location on Earth.
### **Define adaptation themes from domain expertise** We have conducted extensive research on best practices in mitigation and adaptation for each hazard. We then define the thematic areas that can be quantified to explain the degree of adaptation. For example, water treatment facilities — including filtration systems, wastewater treatment plants, and water storage solutions — play a critical role in managing dry spells. During prolonged periods of drought, a location’s proximity to these facilities and its access to their services become key indicators of drought resilience. Thus, we define the thematic feature group **‘Availability & Capacity of Water Works’** to indicate whether a location falls within the service area of water treatment facilities. The table below shows example thematic feature groups that we define for drought, wildfire, inland flooding, and coastal flooding, along with relevant data sources.
HazardThematic feature groupsExample spatial featuresData sources
Droughtwater_storage, water_amenity, water_well, water_worksreservoir, water tower, water treatment plant, drinking water amenity, water well, etc.OpenStreetMap (OSM), Global Dam Watch Database (GDW)
Wildfirefire_detection, fire_prevention, fire_responsefire hydrant, fire station, fire lookout, emergency landing site, etc.OSM
Inland floodingstorage_and_control, direct_barrier, drainage, nature_based_solutiondam, reservoir, flood wall, drain, wetland, etc.OSM, GDW, GOODD
Coastal floodingstorage_and_control, coastal_defense, drainage, natural_bufferseawall, retention basin, drain, mangrove, etc.OSM
### **Collect required spatial attributes** After defining the features to be engineered, we identify suitable data sources to construct each proposed feature. The selected data sources must meet at least one of the three criteria below: * **Peer-reviewed:** the data is published in academic journals that underwent a peer-review process. For example, local climate zone data from [World Urban Database and Access Portal Tools](https://www.wudapt.org/) (WUDAPT). * **Well-documented:** the data is published with clear explanations of methodologies and/or code repositories for third parties to reproduce the results. For example, [OpenStreetMap](https://www.openstreetmap.org/#map=12/1.3649/103.8229). * **Well-established:** the data is published by reputable organizations and is widely used by the industry. For example, [10m Global Land Cover by Impact Observatory](https://www.impactobservatory.com/10m-land-cover/). In the case of the features defined above, OpenStreetMap provides a global map of geospatial features containing water works, wastewater treatment plants, water access points, and other related vector features. Additionally, data from the Global Dam Watch Database ([GDW](https://www.globaldamwatch.org/database)) and Global Georeferenced Database of Dams ([GOODD](https://www.globaldamwatch.org/goodd)) provide the locations, capacity, and outlines of freshwater reservoirs and catchment areas, which complement the data from OpenStreetMap. The raw spatial attributes are then categorized into different thematic feature groups to reflect the diverse aspects of adaptation strategies. The maps below show examples of different adaptation feature groups for drought, coastal flooding, inland flooding, and wildfire in Santa Cruz, California, USA.
### **Construct thematic features using geospatial operations** In most cases, raw source data alone is insufficient to explain the defined adaptation themes. The raw data must be cleaned, processed, and engineered into the desired thematic features for better explainability and compatibility with other data layers. While each data source and adaptation theme requires a unique processing pipeline, the general workflow is as follows: 1. Clean and fix source data anomalies at native resolution. 2. Enhance the resolution or complete missing areas of the source data if necessary. 3. Convert raw data into structured data with spatial indexes. 4. Apply spatial and statistical operations to obtain the targeted thematic feature. 5. Rescale the thematic feature to a scale of 0-100, with 100 indicating the highest resilience in each theme. This can be achieved using min-max normalization or percentile ranking depending on the distribution of the thematic feature. Using the drought resilience example, vector data from OpenStreetMap is first cleaned and buffered as a proxy for **functional service areas** and to account for spatial uncertainty, and then aggregated into a unified spatial index. Similarly, data from reservoir datasets is processed by buffering reservoir polygons with a corresponding radius. By overlaying these layers, we can identify the number of services or adaptation features available at each location, which is then converted into an adaptation score that captures the **cumulative** contribution of diverse infrastructure types at that location. A location that falls within the service radius of all Water Works, Water Storage, Water Amenity, and Water Well categories would have access to diverse water storage, processing, and access infrastructure, benefit from high water capacity, and thus score 100, indicating the highest resilience for drought.
For inland flooding adaptation, reservoir storage density is a useful first-order proxy for how much a catchment can buffer excess rainfall. Higher storage density implies more potential to absorb/attenuate runoff, while lower storage density implies faster translation of rainfall into river flow (higher peaks). We use the watersheds data from GOODD and reservoir capacity data from GDW to calculate the storage density for each watershed. The result is spatially joined to our H3 spatial index grid to provide additional information or context for each cell's adaptation against flood.
### **Aggregate adaptation scores for each hazard** Finally, all adaptation features for a single hazard are aggregated into one distribution, which is then used to adjust the intensity of the hazard. A location that scores 100 in all constituent themes enjoys the highest possible offset for the relevant hazard. This process is applied to each climate hazard to obtain the **Resilience-adjusted Risk** using the methods described in our [Risk and Resilience Framework](/climate-resilience-suite/climate-risk-and-resilience-index/methodology-1-3-resilience-adjusted-risk-framework.md). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.alphageo.ai/adaptation-data-hub/methodology-2-3-global-adaptation-layer.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.