> For the complete documentation index, see [llms.txt](https://docs.alphageo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.alphageo.ai/global-adaptation-layer/methodology-2-3-global-adaptation-layer.md).
# Global Adaptation Layer
{% columns %}
{% column valign="middle" %} Access Free Trial
{% endcolumn %}
{% column %}
{% endcolumn %}
{% endcolumns %}
## Overview
AlphaGeo’s **Global Adaptation Layer** is a **global,multi-hazard dataset that identifies and quantifies hazard-specific adaptation infrastructure.** It captures the local features that shape real-world resilience, from drainage systems and flood barriers to urban greenery, water infrastructure, and fire response coverage.
This gives users a clearer view of **defended conditions on the ground,** not just hazard exposure. The Global Adaptation Layer supports **resilience-adjusted risk analysis, site screening, adaptation planning, and downstream climate impact models**.
## Our approach: Measuring adaptation capacity
Most climate datasets measure hazard. They do not measure whether a location is protected. The Global Adaptation Layer addresses this gap by translating raw geospatial data into hazard-specific adaptation signals. It identifies, then quantifies both (1) proximity to; and (2) adequacy of the infrastructure, land cover, and built-environment features that reduce physical vulnerability or improve local resilience.
This allows users to distinguish between places that are equally exposed but differently defended. It also provides a core input into AlphaGeo’s resilience-adjusted modelling stack, including the [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index.md) and [Climate GDP Impact Forecast](/macro-suite/clima-metrics.md).
An overview of how the Global Adaptation Layer is developed.
Raw spatial features (e.g., filtration systems, wastewater treatment plants, and water storage solutions) are categorized into its relevant thematic feature groups (e.g., "Availability and Capacity of Water Works") that correspond to specific hazards.These adaptation feature groups are then aggregated into hazard-specific Adaptation Scores.
The maps below show examples of different adaptation feature groups for drought, coastal flooding, inland flooding, and wildfire in Santa Cruz, California, USA.
## What's included
The Global Adaptation Layer includes:
* **Hazard-specific adaptation scores** for major acute and chronic hazards
* **Underlying feature layers** that explain the drivers of adaptation capacity
* **Global coverage** at high spatial resolution for asset- and neighborhood-level analysis (up to 30m)
These outputs are designed for direct use in resilience-adjusted (i.e.,defended) climate risk modelling, resilience screening, as well as adaptation gap analysis and decision-making.
Example of our Inland Flooding Adaptation score and underlying adaptation data features for a location
## Hazard coverage
The Global Adaptation Layer currently includes adaptation data for 9 hazard categories, plus societal resilience:
* **Heat Stress Adaptation** — building density, urban greenery, and surface context
* **Inland Flooding Adaptation** — porosity, drainage systems, barriers, and storage infrastructure
* **Coastal Flooding Adaptation** — coastal defences, natural buffers, and drainage capacity
* **Hurricane Wind Adaptation** — building strength and nearby protective infrastructure
* **Drought Adaptation** — water treatment, storage, amenities, and access
* **Wildfire Adaptation** — fire response, fire prevention, and fire detection features
* **Hail Adaptation** — building strength and local protection proxies
* **Landslide Adaptation** — vegetation cover and manmade slope barriers
* **Earthquake Adaptation** — building strength and building sparsity
## Use cases
* Resilience-adjusted climate risk and impact modelling
* Adaptation planning and resilience investment
* Adaptation gap and vulnerability analysis
* Resilience-adjusted insurance underwriting
## Data details
* **Geographic coverage:** Global
* **Resolution:** Up to 30 meters
* **Output types:** Adaptation scores and underlying feature layers
* **Hazard coverage:** 9 hazard categories plus societal resilience
## Detailed Methodology
Please fill out our Methodology Request Form, and a member of our team will get back to you, typically within one business day.
Request form
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## 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/global-adaptation-layer/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.