# Data Sources

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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:**

<table data-header-hidden><thead><tr><th width="378"></th><th></th></tr></thead><tbody><tr><td>Spatial coverage</td><td>Global</td></tr><tr><td>Temporal range (applicable to climate projections and timeseries data)</td><td>1975-2100</td></tr><tr><td>Supported Climate Scenarios</td><td>SSP245, SSP370, SSP585</td></tr><tr><td>Highest Data Resolution</td><td>30m</td></tr><tr><td>Number of Engineered Features</td><td>154 features</td></tr><tr><td>Number of Curated Datapoints</td><td>41 billion</td></tr><tr><td>Number of data sources</td><td>85 unique sources</td></tr><tr><td>Update Cycle</td><td>Every Quarter</td></tr></tbody></table>

## List of data sources:

When curating data sources, the target source must meet one or more criteria below to ensure their reliability. These are:

1. **Peer-reviewed:** the data is published in academic journals that underwent a peer-review process. E.g. Downscaled CMIP6 ensemble models from HighResMIP.
2. **Well-documented: t**he data is published with clear explanation of methodologies and/or code repositories for third parties to reproduce the results. E.g. OpenStreetMap.
3. **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)         |

<table><thead><tr><th width="233">Resilience Datasets</th><th width="154">Data Source</th><th>Description</th></tr></thead><tbody><tr><td>POIs</td><td>OpenStreetMap</td><td>For this product, flood, fire, and drought infrastructure data points were queried from OSM.</td></tr><tr><td>Population</td><td>WorldPop</td><td>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.</td></tr><tr><td>Age and Sex Structure</td><td>WorldPop</td><td>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.</td></tr><tr><td>Night Light</td><td>Earth Observation Group</td><td>This product uses the annual global VIIRS nighttime lights V2.2 cloud-free median radiance grids spanning 2020.</td></tr><tr><td>Income Index</td><td>Global Data Lab</td><td>The data used in this product includes the log of Gross National Income (GNI) per capita in thousands of US Dollars (2011 PPP).</td></tr><tr><td>Gross National Income</td><td>World Bank</td><td>For this product, data on GNI, PPP (current international $) was utilized.</td></tr><tr><td>Global Consensus Land Cover</td><td>EarthEnv</td><td>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.</td></tr><tr><td>Local Climate Zones</td><td>WUDAPT</td><td>The global map of Local Climate Zones, published in the open-access data journal Earth System Science Data, was used in this product.</td></tr></tbody></table>

## 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>


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