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 whereby all data layers are aligned to a uniform spatial index and enhanced when necessary. These curated data layers ensure that the insights generated by AlphaGeo are reliable and explainable everywhere in the world.
Database factsheet:
| Spatial coverage | Global |
| Temporal range (applicable to climate projections and timeseries data) | 1975-2100 |
| Supported Climate Scenarios | SSP245, SSP370, SSP585 |
| Highest Data Resolution | 30m |
| Number of Engineered Features | 154 features |
| Number of Curated Datapoints | 41 billion |
| Number of data sources | 85 unique sources |
| Update Cycle | Every Quarter |
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 explanation of methodologies and/or code repositories for third parties to reproduce the results. E.g. OpenStreetMap.
- Well-established: the data is published by reputable organizations and are widely used by the industry. E.g. the IBTrACS Project by NOAA.
The table below presents a partial selection of publicly accessible data sources that are integrated into AlphaGeo's database. Last Updated: 28 September 2024.
| 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) |
| International Best Track Archive for Climate Stewardship (IBTrACS) | National Oceanic and Atmospheric Administration (NOAA) | Daily values from 1841-2023 | 0.1 degrees (10km) |
| Global Consensus Land Cover | EarthEnv | 2014 | 30 arcseconds (900 meters) |
| FEMA National Flood Hazard Layer | FEMA | 2023 | 10 arcseconds (300 meters) |
| Global estimates of damaging hail hazard | PANGAEA | Daily Values from 1979 to 2015 | 0.7 degrees (70km) |
| Resilience Datasets | Data Source | Description |
|---|---|---|
| POIs | OpenStreetMap | For this product, flood, fire, and drought infrastructure data points were queried from OSM. |
| Population | WorldPop | This product utilized the estimated total number of people per grid-cell (at the equator) by mosaicking 1km resolution global datasets using 100m resolution population count datasets. |
| Age and Sex Structure | WorldPop | This dataset provides estimates of the total number of people per grid square, broken down by sex and age groupings (including 0-1 years and in 5-year increments up to 80+ years) for the year 2020. The units are the estimated number of males and females in each age group per grid square. |
| Night Light | Earth Observation Group | This product uses the annual global VIIRS nighttime lights V2.2 cloud-free median radiance grids spanning 2020. |
| Income Index | Global Data Lab | The data used in this product includes the log of Gross National Income (GNI) per capita in thousands of US Dollars (2011 PPP). |
| Gross National Income | World Bank | For this product, data on GNI, PPP (current international $) was utilized. |
| Global Consensus Land Cover | EarthEnv | The datasets integrate multiple global remote sensing-derived land-cover products and provide consensus information on the prevalence of 12 land-cover classes at 1km resolution. |
| Local Climate Zones | WUDAPT | The global map of Local Climate Zones, published in the open-access data journal Earth System Science Data, was used in this product. |
References
(carbon)plan. “CMIP6 Downscaling,” n.d. https://carbonplan.org/.
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.
Knapp, Kenneth R., Michael C. Kruk, David H. Levinson, Howard J. Diamond, and Charles J. Neumann. “The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data.” Bulletin of the American Meteorological Society 91, no. 3 (March 1, 2010): 363–76. https://doi.org/10.1175/2009BAMS2755.1.
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.
O’Neill, Brian C., Elmar Kriegler, Keywan Riahi, Kristie L. Ebi, Stephane Hallegatte, Timothy R. Carter, Ritu Mathur, and Detlef P. van Vuuren. “A New Scenario Framework for Climate Change Research: The Concept of Shared Socioeconomic Pathways.” Climatic Change 122, no. 3 (February 1, 2014): 387–400. https://doi.org/10.1007/s10584-013-0905-2.
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
Sohn, W., Kim, J., Li, M., Brown, R. D., & Jaber, F. H. (2020). How does increasing impervious surfaces affect urban flooding in response to climate variability? Ecological Indicators, 118, 106774. https://doi.org/10.1016/j.ecolind.2020.106774
Blum, A. G., Ferraro, P. J., Archfield, S. A., & Ryberg, K. R. (2020). Causal effect of impervious cover on annual flood magnitude for the United States. Geophysical Research Letters, 47(5). https://doi.org/10.1029/2019gl086480
Yang, W., Yang, H., Yang, D., & Hou, A. (2021). Causal effects of dams and land cover changes on flood changes in mainland China. Hydrology and Earth System Sciences, 25(5), 2705–2720. https://doi.org/10.5194/hess-25-2705-2021
Dedekorkut-Howes, A., Torabi, E., & Howes, M. (2020). When the tide gets high: a review of adaptive responses to sea level rise and coastal flooding. Journal of Environmental Planning and Management, 63(12), 2102–2143. https://doi.org/10.1080/09640568.2019.1708709
Zhu, X., Linham, M. M., & Nicholls, R. J. (2010). Technologies for Climate Change Adaptation - Coastal Erosion and Flooding. Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi. TNA Guidebook Series
Nazarnia, H., Nazarnia, M., Sarmasti, H., & Wills, W. O. (2020). A Systematic review of civil and environmental infrastructures for coastal adaptation to sea level rise. Civil Engineering Journal, 6(7), 1375–1399. https://doi.org/10.28991/cej-2020-03091555
Tariq, M. a. U. R., Farooq, R., & Van De Giesen, N. (2020). A critical review of flood risk management and the selection of suitable measures. Applied Sciences, 10(23), 8752. https://doi.org/10.3390/app10238752
Cea L, Costabile P. Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review. Hydrology. 2022; 9(3):50. https://doi.org/10.3390/hydrology9030050