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A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, and others. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions. In version 1.10, the completeness of daily PM2.5 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily PM2.5 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual PM2.5 predictions allow public health researchers to respectively estimate the short- and long-term effects of PM2.5 exposures on human health, supporting the U.S. Environmental Protection Agency (EPA) for the revision of the National Ambient Air Quality Standards for 24-hour average and annual average concentrations of PM2.5. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.", "license": "proprietary" }, @@ -51228,7 +51228,7 @@ "bbox": "-180, 17, -65, 72", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2563727886-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2563727886-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_AQDH_PM25O3NO2_ZIPCODE_1.00", "description": "The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, environmental justice, health equity, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, U.S. Census Bureau American CommUnity Survey (ACS), and Area Deprivation Index (ADI). The data are particularly useful for studies on rural populations who are under-represented due to the lack of air monitoring sites in rural areas. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by lowering the key barrier to participation in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.", "license": "proprietary" }, @@ -51488,7 +51488,7 @@ "bbox": "-180, -58, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1418651576-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1418651576-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_CROPCLIM_EFCCGPSRES_1.00", "description": "The Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios is an update to a major crop modeling study by the NASA Goddard Institute for Space Studies (GISS). The initial study was published in 1997, based on output of HadCM2 model forced with greenhouse gas concentration from the IS95 emission scenarios in 1997. Results of the initial study are presented at SEDAC's Potential Impacts of Climate Change on World Food Supply: Data Sets from a Major Crop Modeling Study, released in 2001. The co-authors developed and tested a method for investigating the spatial implications of climate change on crop production. The Decision Support System for Agrotechnology Transfer (DSSAT) dynamic process crop growth models, are specified and validated for one hundred and twenty seven sites in the major world agricultural regions. Results from the crop models, calibrated and validated in the major crop-growing regions, are then used to test functional forms describing the response of yield changes in the climate and environmental conditions. This updated version is based on HadCM3 model output along with GHG concentrations from the Special Report on Emissions Scenarios (SRES). The crop yield estimates incorporate some major improvements: 1) consistent crop simulation methodology and climate change scenarios; 2) weighting of model site results by contribution to regional and national, and rainfed and irrigated production; 3) quantitative foundation for estimation of physiological CO2 effects on crop yields; 4) Adaptation is explicitly considered; and 5) results are reported by country rather than by Basic Linked System region. The data are produced by A. Iglesias and C. Rosenzweig and the maps are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).", "license": "proprietary" }, @@ -51813,7 +51813,7 @@ "bbox": "-105.27, -26.87, 75.69, 38.5", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1947985418-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1947985418-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_FOOD_FIH_V1_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_FOOD_FIH_V1_1.00", "description": "The Food Insecurity Hotspots Data Set consists of grids at 250 meter (~7.2 arc-seconds) resolution that identify the level of intensity and frequency of food insecurity over the 10 years between 2009 and 2019, as well as hotspot areas that have experienced consecutive food insecurity events. The gridded data are based on subnational food security analysis provided by FEWS NET (Famine Early Warning Systems Network) in five (5) regions, including Central America and the Caribbean, Central Asia, East Africa, Southern Africa, and West Africa. Based on the Integrated Food Security Phase Classification (IPC), food insecurity is defined as Minimal, Stressed, Crisis, Emergency, and Famine.", "license": "proprietary" }, @@ -51826,7 +51826,7 @@ "bbox": "-117, 14, -87, 33", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001920-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001920-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_GEO-MEX_GIS_SMI_1.00", "description": "The GIS of Mexican States, Municipalities and Islands consists of attribute and boundary data for 1990. The attribute data include population, language, education, literacy, housing Units and land cover classification from the 1990 Mexican population and housing census. The boundary data associated with the United States-Mexico border are consistent with the U.S. Census Bureau TIGER95 data. 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The data include population by gender and age group for approximately 83.7% of the Mexican population. 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The vast majority of the locations are administrative level 1 (typically state/province/region).", "license": "proprietary" }, @@ -53581,7 +53581,7 @@ "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1401421022-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1401421022-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_SDEI_NO2_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_SDEI_NO2_1.00", "description": "The Global 3-Year Running Mean Ground-Level Nitrogen Dioxide (NO2) Grids from GOME, SCIAMACHY and GOME-2 represent a series of three-year running mean grids (1996-2012) of ground level NO2 that were derived from Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment-2 (GOME-2) satellite retrievals. For each satellite-derived NO2 source, the relationship between satellite observations of tropospheric NO2 column densities and the NO2 concentrations at ground level relevant to human exposure is simulated, using the Goddard Earth Observing System chemical transport model (GEOS-Chem) to produce a mean NO2 concentration raster grid. The grid cell resolution is six arc-minutes (0.1 degree, or approximately 10 km at the equator) covering the global land surface.", "license": "proprietary" }, @@ -53737,7 +53737,7 @@ "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001924-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001924-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_SDP_GGDP_B2_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_SDP_GGDP_B2_1.00", "description": "The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "license": "proprietary" }, @@ -53750,7 +53750,7 @@ "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C179001925-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C179001925-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_SDP_GPOP_B2_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_SDP_GPOP_B2_1.00", "description": "The Global 15x15 Minute Grids of the Downscaled Population Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of the downscaled population per Unit area (population densities). These global grids were generated using the Country-level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. The 1990 GPW was used as the base distribution and the country-level downscaled projections were used to replace population estimates of 1990 in GPW and 2025. The fractional distribution of the population at each grid cell is the same as the 1990 GPW, sub-nationally. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).", "license": "proprietary" }, @@ -53854,7 +53854,7 @@ "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2161983020-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2161983020-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_SSP_1-kmDULEPBYGSSP_1.00", "description": "The Global 1-km Downscaled Urban Land Extent Projection and Base Year Grids by SSP Scenarios, 2000-2100 consists of global SSP-consistent spatial urban land fraction data for the base year 2000 and projections at ten-year intervals for 2010-2100 at a resolution of 1-km (about 30 arc-seconds). An algorithm was developed and validated to downscale the 1/8-degree resolution data set to 1-km resolution. For a given decade, the downscaling algorithm allocates the 1/8-degree decadal amount of urban land expansion to 1-km grid cells in proportion to their total urban land amounts at the beginning of the decade. The algorithm uses an iterative process to collect any overflows from already highly-developed 1-km grid cells, and then allocates them to 1-km grid cells that are not yet fully developed. This iterative process repeats itself until all 1/8-degree amounts of urban land expansion are allocated to 1-km grid cells with no overflow. The downscaling process is applied decade by decade throughout the 21st century for each urban land expansion scenario. The final product is a set of global maps displaying the 1-km fraction of urban land, updated at decadal intervals throughout the 21st century, for five different urban land expansion scenarios consistent with the Shared Socioeconomic Pathways (SSPs).", "license": "proprietary" }, @@ -54075,7 +54075,7 @@ "bbox": "-7.000879, -39.073198, 39.365646, 6.568272", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1418646880-SEDAC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1418646880-SEDAC.html", - "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsOF0iLCJ1bW0iOiJbXCJkYWlseSBhbmQgYW5udWFsIHBtMi41IGNvbmNlbnRyYXRpb25zIGZvciB0aGUgY29udGlndW91cyB1bml0ZWQgc3RhdGVzLCAxLWttIGdyaWRzLCB2MSAoMjAwMCAtIDIwMTYpXCIsXCJTRURBQ1wiLFwiQ0lFU0lOX1NFREFDX0FRREhfREFQTTI1X1VTXzFLTVwiLFwiMS4wXCIsMjA5MTc2NDUwNiw4XSJ9/CIESIN_SEDAC_USPAT_DESLUIS_1.00", + "href": "https://cmr.earthdata.nasa.gov/stac/SEDAC/collections?cursor=eyJqc29uIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIiwidW1tIjoiW1wiZGFpbHkgYW5kIGFubnVhbCBwbTIuNSBjb25jZW50cmF0aW9ucyBmb3IgdGhlIGNvbnRpZ3VvdXMgdW5pdGVkIHN0YXRlcywgMS1rbSBncmlkcywgdjEgKDIwMDAgLSAyMDE2KVwiLFwiU0VEQUNcIixcIkNJRVNJTl9TRURBQ19BUURIX0RBUE0yNV9VU18xS01cIixcIjEuMFwiLDIwOTE3NjQ1MDYsMTBdIn0%3D/CIESIN_SEDAC_USPAT_DESLUIS_1.00", "description": "The Dar es Salaam Land Use and Informal Settlement Data Set represents urban land use and consolidation of informal settlements for the years 1982, 1992, 1998, and 2002, in Dar es Salaam, Tanzania. 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