Data Sources
The foundation of our risk-resilience framework is the extensive collection of geospatial data layers engineered from first-party and third-party sources.
From satellite observations, climate models to socio-economic data and text-based reports, we monitor a growing list of high-quality data sources and curate the best available data onto our platform.
During this process, the data sources go through a stringent quality-control and curation process. Each layer is indexed onto our spatial grid at a governed resolution appropriate to the prepared source. Layers are not resampled at runtime merely to force them onto one shared scoring grid. Source-preparation steps may map pixels onto H3 cells, but do not create independent physical detail beyond what the underlying dataset can support. These curated layers help make AlphaGeo's results reliable and explainable across locations.
Database factsheet:
| Spatial coverage | Global |
| Climate projection coverage | 1950–2100 |
| Supported climate scenarios | SSP2-4.5, SSP3-7.0, SSP5-8.5 |
| Source resolution | Source-dependent, from approximately 25 m to 100 km |
| Runtime spatial resolution | Source-dependent H3 levels; each layer is read at its governed resolution |
| Review cycle | Quarterly, with source updates incorporated after quality and methodology review |
List of data sources:
When curating data sources, the target source must meet one or more criteria below to ensure their reliability. These are:
- Peer-reviewed: the data is published in academic journals that underwent a peer-review process. E.g. Downscaled CMIP6 ensemble models from HighResMIP.
- Well-documented: the data is published with clear explanations of its methodology and/or code repositories that allow third parties to reproduce the results. E.g. OpenStreetMap.
- Well-established: the data is published by reputable organizations and is widely used by the industry. E.g. the Aqueduct project by the World Resources Institute.
The table below presents a partial selection of publicly accessible data sources integrated into AlphaGeo's database.
| Risk Datasets | Data Source | Temporal Coverage | Resolution |
|---|---|---|---|
| Global CMIP6 Climate Projections | NASA NEX-GDDP-CMIP6 | Daily values from 1950-2100 | 0.25 degrees (25km) |
| Aqueduct 4.0 | World Resources Institute (WRI) | Yearly values in 2014, 2030, 2050, 2080 | 15 arcseconds (450 meters) |
| Global Storm Surge Indicator | Copernicus | Yearly values in 2015, 2050 | 0.25 degrees (25km) |
| Global Mean Sea Level Change | Intergovernmental Panel on Climate Change (IPCC) | 2021-2040, 2041-2060, 2081-2100 | 1 degree (100km) |
| Global Coastal Tropical Cyclone Wind Hazard | CHAZ/CLIMADA; Bloemendaal et al. | 1995–2014, 2041–2060 and 2081–2100 windows; 10- to 1,000-year return periods | Prepared at H3 resolution 6 (approximately 4 km) |
| Global Consensus Land Cover | EarthEnv | 2014 | 30 arcseconds (900 meters) |
| Global estimates of damaging hail hazard | PANGAEA | Daily Values from 1979 to 2015 | 0.7 degrees (70km) |
| Global Landslide Susceptibility | NASA LHASA | Static susceptibility model | Read at H3 resolution 8 (approximately 460 m edge length) |
| Global Seismic Hazard Model | GEM Foundation | 2018 model version | Read at H3 resolution 6 (approximately 4 km edge length) |
| Resilience dataset | Data source | Description |
|---|---|---|
| Points of interest | OpenStreetMap | Flood, fire and drought infrastructure used to represent local adaptation capacity. |
| Population | WorldPop | Gridded population estimates derived from approximately 100 m source products. |
| Age and sex structure | WorldPop | Local age-group composition used to estimate the share of the population that may be more vulnerable to climate shocks. |
| Night lights | Earth Observation Group | Annual VIIRS nighttime-light radiance used as part of the broader adaptation context. |
| Human Development Index | Global Data Lab | Subnational health, education and income conditions used in the societal-resilience composite. |
| Gross fixed capital formation | World Bank | Gross fixed capital formation as a percentage of GDP, representing capacity to invest in and maintain physical infrastructure. |
| Global Consensus Land Cover | EarthEnv | Consensus prevalence of 12 land-cover classes at approximately 1 km source resolution. |
| Local Climate Zones | WUDAPT | Urban form and thermal context used in the Heat Stress methodology. |
References
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Thrasher, B., Wang, W., Michaelis, A. et al. NASA Global Daily Downscaled Projections, CMIP6. Sci Data 9, 262 (2022). https://doi.org/10.1038/s41597-022-01393-4
Eyring, Veronika, Sandrine Bony, Gerald A. Meehl, Catherine A. Senior, Bjorn Stevens, Ronald J. Stouffer, and Karl E. Taylor. “Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization.” Geoscientific Model Development 9, no. 5 (May 26, 2016): 1937–58. https://doi.org/10.5194/gmd-9-1937-2016.
Bloemendaal, Nadia, et al. Global coastal tropical cyclone wind hazard. Scientific Data (2025). https://doi.org/10.1038/s41597-025-06452-0
Emanuel, Kerry. “Global Warming Effects on U.S. Hurricane Damage.” Weather, Climate, and Society 3, no. 4 (2011): 261–68. https://doi.org/10.1175/WCAS-D-11-00007.1
Eberenz, Samuel, Samuel Lüthi, and David N. Bresch. “Regional Tropical Cyclone Impact Functions for Globally Consistent Risk Assessments.” Natural Hazards and Earth System Sciences 21, no. 1 (2021): 393–415. https://doi.org/10.5194/nhess-21-393-2021.
Kuzma, Samantha, Marc F. P. Bierkens, Shivani Lakshman, Tianyi Luo, Liz Saccoccia, Edwin H. Sutanudjaja, and Rens Van Beek. “Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators,” August 16, 2023. https://www.wri.org/research/aqueduct-40-updated-decision-relevant-global-water-risk-indicators.
Muis, Sanne, Jeroen C. J. H. Aerts, José A. Á. Antolínez, Job C. Dullaart, Trang Minh Duong, Li Erikson, Rein J. Haarsma, et al. “Global Projections of Storm Surges Using High-Resolution CMIP6 Climate Models.” Earth’s Future 11, no. 9 (2023): e2023EF003479. https://doi.org/10.1029/2023EF003479.
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Tuanmu, Mao-Ning, and Walter Jetz. “A Global 1-Km Consensus Land-Cover Product for Biodiversity and Ecosystem Modelling.” Global Ecology and Biogeography 23, no. 9 (2014): 1031–45. https://doi.org/10.1111/geb.12182.
Prein, Andreas F; Holland, Greg (2018): Daily large hail probability on a global scale (1979 to 2015), Version 2, link to netCDF files [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.893160, Supplement to: Prein, AF; Holland, G (2018): Global estimates of damaging hail hazard. Weather and Climate Extremes, 22, 10-23, https://doi.org/10.1016/j.wace.2018.10.004
Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879–1900. https://doi.org/10.1175/bams-d-11-00019.1
Aslam, A., & Rana, I. A. (2022). The use of local climate zones in the urban environment: A systematic review of data sources, methods, and themes. Urban Climate, 42, 101120. https://doi.org/10.1016/j.uclim.2022.101120
Bechtel, B., Demuzere, M., Mills, G., Zhan, W., Sismanidis, P., Small, C., & Voogt, J. (2019). SUHI analysis using Local Climate Zones—A comparison of 50 cities. Urban Climate, 28, 100451. https://doi.org/10.1016/j.uclim.2019.01.005
Verdonck, M., Demuzere, M., Hooyberghs, H., Beck, C., Cyrys, J., Schneider, A., Dewulf, R., & Van Coillie, F. (2018). The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data. Landscape and Urban Planning, 178, 183–197. https://doi.org/10.1016/j.landurbplan.2018.06.004
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