Name: LitPop: Global Exposure Data for Disaster Risk Assessment Authors: Samuel Eberenz, Dario Stocker, Thomas Röösli, David N. Bresch. Affiliations: Weather and Climate Risks Group: http://www.wcr.ethz.ch/ 1. ETH Zurich, USYS, WCR, Prof. David N. Bresch 2. Swiss Federal Office of Meteorology and Climatology MeteoSwiss Please cite as: Eberenz, S., Stocker, D., Röösli, T., and Bresch, D. N.: Asset exposure data for global physical risk assessment, Earth Syst. Sci. Data, 12, 817–833, https://doi.org/10.5194/essd-12-817-2020, 2020. Description: Gridded physical asset values per country. National total physical asset values downscaled proportionally to the normalised product of nightlight intensity (Lit, based on NASA Earth at Night [1]) and population count (Pop, based on Gridded Population of the World, Version 4.1 [2]). Gridded exposure is computed with the LitPop-module of the probabilistic natural catastrophe damage model CLIMADA [3]. Please refer to the accompanying publication [4] and the tutorial [5] for more information. National total physical asset values are produced capital of 2014 from The World Bank's "Wealth Accounting" [6] (available for 140 countries). Alternatively, non-financial wealth is estimated from the country's GDP in 2014 and the GDP-to-wealth ratios as estimated in the Credit Suisse Research Institute's "Global Wealth Report 2017" [7] (used for 84 countries). Gridded normalised Lit and Pop are provided for 14 selected country as used in the validation of the LitPop exposure data model [2]. The Python 3.6 script to reproduce this data is available at https://github.com/CLIMADA-project/climada_papers. Keywords: Exposure data model, exposed assets, global, nightlights, satellite imagery, gridded population, produced capital, gross domestic product, GDP, downscale Licence: Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ Versions: v1.2: Date: 2019-04-09 As v1.1, but removed three files of countries without data (field "value" was empty in v1.1): - LitPop_pc_30arcsec_ATF.csv (French Southern Territories) - LitPop_pc_30arcsec_IOT.csv (British Indian Ocean Territory) - LitPop_pc_30arcsec_SGS.csv (South Georgia and the South Sandwich Islands); Updated reference to publication and how to cite; Updated _metadata_countries.csv: more information added. v1.1 Date: 2019-03-14 Reference year produced capital and GDP: 2014 Reference year nightlights NASA Earth at Night: 2016 Reference year Gridded Population of the World: 2015 Resolution: 30 arc-seconds CLIMADA repository: CLIMADA v1.2.0, https://github.com/CLIMADA-project/climada_python/releases/tag/v1.2.0, https://doi.org/10.5905/ethz-1007-226 Files and variables: _metadata_countries.csv : metadata countries Metadata for 224 countries included in the asset exposure data set and 26 countries and areas not included. Each row contains data on one country. Details on columns: country_name: name of country or area (short form); iso3: current official ISO 3166-1 alpha-3 code; region_id: current official ISO 3166-1 numeric code; included: indicates whether the country is included in the data set (1), or no data is available for the country (0); total_value: total asset value used for disaggregation in current USD; data_source: data source for total asset value, either produced capital (pc), non-financial wealth (nfw), or none; evaluation: indicates whether the country was used for evaluation (1) or not (0); produced_capital [USD]: Produced capital of 2014 current USD; GDP [USD]: GDP in current USD; GDP_year: year of GDP value; GDP-to-NFW_ratio: Ratio of GDP to non-financial wealth; NFW [USD]: Non-financial wealth computed as the product of GDP and the GDP-to-NFW ratio; GPW_highest_admin_level: Highest admin level used in gridded population of the world (GPW) data of 2015, high number indicates high granularity; GPW_Number_of_regions: Total number of regions with unique population data in gridded population of the world (GPW) data of 2015. Archive LitPop_v1_2.tar contains: LitPop_pc_30arcsec_XXX.csv : Gridded LitPop exposure data for country with alpha iso3-code (224 countries) value: estimated asset value of 2014 at grid point, current USD latitude: grid point's latitude (decimal degree) longitude: grid point's longitude (decimal degree) region_id: country UN numeric code Archive Lit_Pop_norm_v1.tar contains: Lit_norm_30arcsec_XXX.csv : Gridded normalised nightlight intensity data for country with iso3-code XXX (14 countries) Pop_norm_30arcsec_XXX.csv : Gridded normalised population count data for country with iso3-code XXX (14 countries) value: normalised value per grid point latitude: grid point's latitude (decimal degree) longitude: grid point's longitude (decimal degree) region_id: country UN numeric code References: [1] NASA Earth Observatory, 2017. Earth at Night: Flat Maps. Avaible at: earthobservatory.nasa.gov/features/NightLights/page3.php. (last access: January 2020) [2] Socioeconomic Data and Applications Center (SEDAC): Country-level Information and Sources Revision 10, avail- able at: https://beta.sedac.ciesin.columbia.edu/data/set/ gpw-v4-admin-unit-center-points-population-estimates-rev10/docs, 2017. (last access: 4 April 2020) [3] Python version of CLIMADA. https://github.com/CLIMADA-project/climada_python/ (last accessed April 2020) [4] Publication: https://doi.org/10.5194/essd-12-817-2020 (last accessed April 2020) [5] Tutorial: https://climada-python.readthedocs.io/en/latest/tutorial/climada_entity_LitPop.html (last accessed April 2020) [6] The World Bank. "Wealth Accounting": https://datacatalog.worldbank.org/dataset/wealth-accounting (last accessed April 2020) [7] Credit Suisse Research Institute. "Global Wealth Report 2017", 2017. https://www.credit-suisse.com/corporate/en/articles/news-and-expertise/global-wealth-report-2017-201711.html (last accessed March 2019) HowTo: How to load the CSV-files into CLIMADA (Python 3.6) as entity: ### from climada.entity import Exposures file_path = "FullFilePath.csv" # define the full file path of the CSV-file new_exp = Exposures(pd.read_csv(file_name)) new_exp.check() ###