The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.
The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.
This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.
This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.
This dataset was created on 2020-01-10 18:53:00.508
by merging multiple datasets together. The source datasets for this version were:
Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile
Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile
Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile
Commuting Zone Characteristics: CZ-level characteristics
Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile
This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.
This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.
Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths
This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.
This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/
This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only
This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.
This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.
This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.
Two variables constructed by the Cen
This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st percentile, 10th percentile, and maximum of sensitivity-specificity ). This data bundle contains a single file of occurrence data with abundance information (Nonnative_plants_US.csv) and a subfolder for each species that contains the two raster files associated with the species. Each of the two rasters represent the following: species_code for current climate and species_code.2c for predictions under a +2C climate change scenario. The bundle documentation files are: 1) 'project_metdata.xml' (this file) which contains the project-level metadata 2) Nonnative_plants_US.csv is the occurrence and abundance data. 3) XX.tif where XX is the species code with current climatic conditions and species code with '2c' appended for habitat suitability predictions with +2C of climate change.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022 have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.
Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.
The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.
This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.
Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.
More information on our methods, data sources, and data processing and analysis codes are found on our github page
The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files
The files each have the following fields (no data is blank):
'ID': row ID integer
'Sample_ID': identifier to raw data source
'Sample_Type_Code': code of sample id
'Project': raw datasource project identifier
'dataset': raw dataset major identifier
'Date': date, where specified, and to whatever precision that is specified
'Location_Type': where specified, code indicating type of location information
'latitude': latitude in decimal degrees
'longitude': longitude in decimal degrees
'Contact': where specified, raw data originator
'num_orig_dists': number of unique grain size distributions
'Measured_Distributions': number iof measured grain size distributions
'Grainsize': grain size is sometimes reported without specification
'Mean', mean grain size in mm
'Median', median grain size in mm
'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])
'Kurtosis', kurtosis value (non-dim)
'Kurtosis_Class', kurtosis category
'Skewness', skewness value (non-dim)
'Skewness_Class', skewness category
'Std', standard deviation of grain sizes
'Sorting', sorting category
'd5', grain size distribution 5th percentile
'd10', grain size distribution 10th percentile
'd16', grain size distribution 16th percentile
'd25', grain size distribution 25th percentile
'd30', grain size distribution 30th percentile
'd50', grain size distribution 50th percentile
'd65', grain size distribution 65th percentile
'd75', grain size distribution 75th percentile
'd84',grain size distribution 84th percentile
'd90', grain size distribution 90th percentile
'd95', grain size distribution 95th percentile
'Notes': notes - these can be informative and substantial, do not disregard
Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format
This is a dataset containing the potential distribution of Japanese brome (Bromus japonicus). We developed habitat suitability models for Japanese brome, as suggested by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for three different thresholds (conservative to targeted). This data bundle contains a single file of tabular summaries by management unit (including each species/ ensemble type combination) and a subfolder for each species that contains the merged data sets used to create models, the six raster files associated with the species, and tabular outputs including response curve data, variable importance information, and model assessment metrics. Each of the six rasters represent the following: 1) 0.01 - one percentile threshold 2) 0.1 - ten percentile threshold 3) MaxSS - maximum sensitivity plus specificity threshold 4) 0.01 - one percentile threshold with Restricted Environmental Conditions 5) 0.1 - ten percentile threshold with Restricted Environmental Conditions 6) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'project_metdata.xml' (this file) which contains the project-level metadata 2) managementSummaries.csv is the tabular summaries by management unit. 3) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for each species. 4) XX.tif where XX is the raster type explained above (threshold; masked or not). 5) responseCurves.csv is the tabular information need to produce response curves for each predictor retained in each of the 10 models produced for each species. 6) variableImportance.csv is the tabular summaries indicating predictor importance for each of the 10 models produced for each species. 7) assessmentMetrics.csv is the tabular summaries of assessment metrics for each model or ensemble for each species.
The U.S. Climate Reference Network (USCRN) was designed to monitor the climate of the United States using research quality instrumentation located within representative pristine environments. This Standardized Soil Moisture (SSM) and Soil Moisture Climatology (SMC) product set is derived using the soil moisture observations from the USCRN. The hourly soil moisture anomaly (SMANOM) is derived by subtracting the MEDIAN from the soil moisture volumetric water content (SMVWC) and dividing the difference by the interquartile range (IQR = 75th percentile - 25th percentile) for that hour: SMANOM = (SMVWC - MEDIAN) / (IQR). The soil moisture percentile (SMPERC) is derived by taking all the values that were used to create the empirical cumulative distribution function (ECDF) that yielded the hourly MEDIAN and adding the current observation to the set, recalculating the ECDF, and determining the percentile value of the current observation. Finally, the soil temperature for the individual layers is provided for the dataset user convenience. The SMC files contain the MEAN, MEDIAN, IQR, and decimal fraction of available data that are valid for each hour of the year at 5, 10, 20, 50, and 100 cm depth soil layers as well as for a top soil layer (TOP) and column soil layer (COLUMN). The TOP layer consists of an average of the 5 and 10 cm depths, while the COLUMN layer includes all available depths at a location, either two layers or five layers depending on soil depth. The SSM files contain the mean VWC, SMANOM, SMPERC, and TEMPERATURE for each of the depth layers described above. File names are structured as CRNSSM0101-STATIONNAME.csv and CRNSMC0101-STATIONNAME.csv. SSM stands for Standardized Soil Moisture and SCM represent Soil Moisture Climatology. The first two digits of the trailing integer indicate major version and the second two digits minor version of the product.
CDC child growth charts consist of a series of percentile curves that illustrate the distribution of selected body measurements in U.S. children. Pediatric growth charts have been used by pediatricians, nurses, and parents to track the growth of infants, children, and adolescents in the United States since 1977. Growth charts are not intended to be used as a sole diagnostic instrument. Instead, growth charts are tools that contribute to forming an overall clinical impression for the child being measured.
We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for three different thresholds (conservative to targeted). This data bundle contains a single file of tabular summaries by management unit (including each species/ ensemble type combination) and a subfolder for each species that contains the merged data sets used to create models, the six raster files associated with the species, and tabular outputs including response curve data, variable importance information, and model assessment metrics. Each of the six rasters represent the following: 1) 0.01 - one percentile threshold 2) 0.1 - ten percentile threshold 3) MaxSS - maximum sensitivity plus specificity threshold 4) 0.01 - one percentile threshold with Restricted Environmental Conditions 5) 0.1 - ten percentile threshold with Restricted Environmental Conditions 6) MaxSS - maximum sensitivity plus specificity threshold with Restricted Environmental Conditions The bundle documentation files are: 1) 'INHABIT_V3_metdata.xml' (this file) which contains the project-level metadata 2) managementSummaries.csv is the tabular summaries by management unit. 3) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for each species. 4) XX.tif where XX is the raster type explained above (threshold; masked or not). 5) responseCurves.csv is the tabular information need to produce response curves for each predictor retained in each of the 10 models produced for each species. 6) variableImportance.csv is the tabular summaries indicating predictor importance for each of the 10 models produced for each species. 7) assessmentMetrics.csv is the tabular summaries of assessment metrics for each model or ensemble for each species. These data will be integrated into the third version of the Invasive Species Habitat Tool (INHABIT), a web application displaying visual and statistical summaries of nationwide habitat suitability models for manager identified invasive plant species. These species include: African rue (Peganum harmala), Air potato (Dioscorea bulbifera), Alkali swainsonpea (Sphaerophysa salsula), Amur honeysuckle (Lonicera maackii), Amur maple (Acer ginnala), Amur peppervine (Ampelopsis brevipedunculata), Annual bluegrass (Poa annua), Annual rye (Lolium multiflorum), Asian mustard (Brassica tournefortii), Autumn olive (Elaeagnus umbellata), Balloon vine (Cardiospermum halicacabum), Beefsteak mint (Perilla frutescens), Bermudagrass (Cynodon dactylon), Bigleaf periwinkle (Vinca major), Bird vetch (Vicia cracca), Bishop's goutweed (Aegopodium podagraria), Black henbane (Hyoscyamus niger), Bohemian knotweed (Fallopia bohemica), Bradford pear (Pyrus calleryana), Brazilian peppertree (Schinus terebinthifolius), Briton's wild petunia (Ruellia simplex), Broad leaved helleborine (Epipactis helleborine), Buffelgrass (Cenchrus ciliaris), Bulbous bluegrass (Poa bulbosa), Bull thistle (Cirsium vulgare), Bur buttercup (Ranunculus testiculatus), Burning bush (Euonymus alatus), Caesarweed (Urena lobata), Camelthorn (Alhagi maurorum), Camphortree (Cinnamomum camphora), Canada thistle (Cirsium arvense), Cape-ivy (Delairea odorata), Castor bean (Ricinus communis), Cat's claw creeper (Dolichandra unguis-cati), Cereal rye (Secale cereale), Cheatgrass (Bromus tectorum), Chinaberry (Melia azedarach), Chinese holly (Ilex cornuta), Chinese pistache (Pistacia chinensis), Chinese privet (Ligustrum sinense), Chinese tallowtree (Triadica sebifera), Chinese wisteria (Wisteria sinensis), Chocolate vine (Akebia quinata), Clasping pepperweed (Lepidium perfoliatum), Coco yam (Colocasia esculenta), Cogongrass (Imperata cylindrica), Common buckthorn (Rhamnus cathartica), Common crupina (Crupina vulgaris), Common gorse (Ulex europaeus), Common reed (Phragmites australis), Common tansy (Tanacetum vulgare), Common wormwood (Artemisia vulgaris), Coral ardisia (Ardisia crenata), Crape myrtle (Lagerstroemia indica), Creeping bentgrass (Agrostis stolonifera), Creeping buttercup (Ranunculus repens), Crested wheatgrass (Agropyron cristatum), Crown vetch (Securigera varia), Curly dock (Rumex crispus), Cutleaf blackberry (Rubus laciniatus), Cutleaf teasel (Dipsacus laciniatus), Cypress spurge (Euphorbia cyparissias), Dallisgrass (Paspalum dilatatum), Dalmatian toadflax (Linaria dalmatica), Dames rocket (Hesperis matronalis), Diffuse knapweed (Centaurea diffusa), Dyer's woad (Isatis tinctoria), Eggleaf spurge (Euphorbia oblongata), English holly (Ilex aquifolium), English ivy (Hedera helix), European beachgrass (Ammophila arenaria), European privet (Ligustrum vulgare), False brome (Brachypodium sylvaticum), Field bindweed (Convolvulus arvensis), Field brome (Bromus arvensis), Field pennycress (Thlaspi arvense), Field sowthistle (Sonchus arvensis), Fig buttercup (Ficaria verna), Flowering rush (Butomus umbellatus), Fountaingrass (Cenchrus setaceum), French broom (Genista monspessulana), Fuller's teasel (Dipsacus fullonum), Garden yellow loosestrife (Lysimachia vulgaris), Garlic mustard (Alliaria petiolata), Giant knotweed (Fallopia sachalinensis), Giant reed (Arundo donax), Glossy privet (Ligustrum lucidum), Golden raintree (Koelreuteria elegans), Guinea grass (Megathyrsus maximus), Hairy cat's ear (Hypochaeris radicata), Halogeton (Halogeton glomeratus), Harding grass (Phalaris aquatica), Himalayan blackberry (Rubus bifrons), Himalayan knotweed (Polygonum polystachyum), Hoary alyssum (Berteroa incana), Horse chestnut (Aesculus hippocastanum), Houndstongue (Cynoglossum officinale), Iberian starthistle (Centaurea iberica), Ice plant (Mesembryanthemum crystallinum), Italian arum (Arum italicum), Italian thistle (Carduus pycnocephalus), Japanese barberry (Berberis thunbergii), Japanese bristlegrass (Setaria faberi), Japanese chaff flower (Achyranthes japonica), Japanese climbing fern (Lygodium japonicum), Japanese honeysuckle (Lonicera japonica), Japanese knotweed (Fallopia japonica), Japanese pagoda tree (Styphnolobium japonicum), Japanese snowballs (Viburnum plicatum), Japanese stiltgrass (Microstegium vimineum), Japanese wisteria (Wisteria floribunda), Jetbead (Rhodotypos scandens), Johnsongrass (Sorghum halepense), Jointed goatgrass (Aegilops cylindrica), Kochia (Kochia scoparia), Kudzu (Pueraria montana), Largeleaf lantana (Lantana camara), Leafy spurge (Euphorbia esula/virgata), Leatherleaf mahonia (Berberis bealei), Lehmann lovegrass (Eragrostis lehmanniana), Malta starthistle (Centaurea melitensis), Meadow hawkweed (Hieracium caespitosum), Mediterranean sage (Salvia aethiopis), Medusahead (Taeniatherum caput-medusae), Moneywort (Lysimachia nummularia), Mouse barley (Hordeum murinum), Multiflora rose (Rosa multiflora), Musk thistle (Carduus nutans), Narrowleaf bittercress (Cardamine impatiens), Narrowleaf plantain (Plantago lanceolata), Norway maple (Acer platanoides), Old world climbing fern (Lygodium microphyllum), Oneseed hawthorn (Crataegus monogyna), Orange hawkweed (Hieracium aurantiacum), Orchardgrass (Dactylis glomerata), Oriental bittersweet (Celastrus orbiculatus), Ornamental jewelweed (Impatiens glandulifera), Oxeye daisy (Leucanthemum vulgare), Pale yellow iris (Iris pseudacorus), Pampasgrass (Cortaderia selloana), Paper mulberry (Broussonetia papyrifera), Perennial pepperweed (Lepidium latifolium), Poison hemlock (Conium maculatum), Princesstree (Paulownia tomentosa), Puncture vine (Tribulus cistoides), Puncture vine (Tribulus terrestris), Punktree (Melaleuca quinquenervia), Purple loosestrife (Lythrum salicaria), Purple mustard (Chorispora tenella), Purple pampasgrass (Cortaderia jubata), Purple starthistle (Centaurea calcitrapa), Rattail fescue (Vulpia myuros), Rattlesnake brome (Bromus briziformis), Rattlesnake grass (Briza maxima), Ravennagrass (Saccharum ravennae), Red brome (Bromus rubens), Redstem filaree (Erodium cicutarium), Reed canarygrass (Phalaris arundinacea), Ripgut brome (Bromus diandrus), Robert's geranium (Geranium robertianum), Rose natal grass (Melinis repens), Roundleaf chastetree (Vitex rotundifolia), Rush skeletonweed (Chondrilla juncea), Russian knapweed (Rhaponticum repens), Russian olive (Elaeagnus angustifolia), Russian thistle (Salsola tragus), Rye brome (Bromus secalinus), Sacred bamboo (Nandina domestica), Scotch broom (Cytisus scoparius), Scotch thistle (Onopordum acanthium), Sericea lespedeza (Lespedeza cuneata), Sheep sorrel (Rumex acetosella), Shining geranium (Geranium lucidum), Siberian elm (Ulmus pumila), Siebold's arrowwood (Viburnum sieboldii), Silktree (Albizia julibrissin), Small-Leaf spiderwort (Tradescantia fluminensis), Smooth brome (Bromus inermis), Smooth hawthorn (Crataegus laevigata), Soft brome (Bromus hordeaceus spp. Hordeaceus), Sorghum (Sorghum bicolor), Spanish broom (Spartium junceum), Spiny cocklebur (Xanthium spinosum), Spiny plumeless thistle (Carduus acanthoides), Spotted knapweed (Centaurea stoebe), Squarrose knapweed (Centaurea virgata), St.
The phenylpyrazole insecticide fipronil and its degradates are a potential surface-water contaminant and toxicant to nontarget species such as aquatic macroinvertebrates. To better understand how fipronil, fipronil sulfide, fipronil sulfone, desulfinyl fipronil, and fipronil amide affect aquatic communities, a 30-day mesocosm experiment was run. Rock trays were colonized with natural benthic communities in the Cache La Poudre River in the mountains of northern Colorado and transplanted into a laboratory experimental stream setting. In total, there were 36 experimental streams: 3 controls, 3 solvent controls, and 30 treatments. Water quality metrics and samples for pesticide analysis were collected throughout the experiment. At the end of the experiment, larval invertebrates remaining in each experimental stream were collected, enumerated, and identified. Emergent insects were collected each day of the experiment and identified to lowest taxonomic unit. These data were used to derive species-specific effect concentrations and, along with published data, derive species sensitivity distributions for fipronil(s) and hazard concentrations for the 5th percentile of affected species (HC5). The resulting HC5 values were used to convert fipronil compound concentrations in field samples to the sum of toxic units (∑TUFipronils), and the field invertebrate data were converted into a Species at Risk (SPEAR) pesticides metric (SPEAR_pesticide) and used to explore the relationship between the invertebrate community and ∑TUFipronils.
https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/HM91JNhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/HM91JN
This dataset contains replication files for "Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility" by Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/recentintergenerationalmobility/. A summary of the related publication follows. We present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child’s probability of attending college and her parents’ income rank. We also calculate transition probabilities, such as a child’s chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery” – the parents to whom a child is born – are larger today than in the past. The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US Treasury Department or the Internal Revenue Service or the National Bureau of Economic Research.
Summary:Land Information System (LIS) 0-200 cm layer Soil Moisture Percentile generated by the NASA SPoRT Center over a Contiguous United States domain.The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications and against U.S. Drought Monitor products.SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2 through 4 days prior to the current time, and by the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates from 4 days ago to the present time. A unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of VIIRS Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model.The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific day of the year. Soil moisture percentile maps are constructed for the model layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product.Suggested Use:This product can be used for drought assessment, fire risk assessment, potential for flooding hazards associated with heavy precipitation and high percentiles; contextualizing soil moisture content to historical values.Soil moisture percentiles are shown using a Classified Color Ramp (Multi-Color, 11-classes) that colorize the low percentile categories (≤ 30th) as shown in the U.S. Drought Monitor weekly products, ranging from yellow to dark red. The high percentile categories (≥ 70th) are colorized with increasing blue intensity. Intermediate percentiles in the 30th to 70th range are assigned a nominal gray shade.The 0-200 cm layer combines SPoRT-LIS soil moisture analyses from all four model layers 0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm depths. The 0-200 cm cumulative layer adjusts slowly to precipitation episodes or the lack thereof compared to the other cumulative layered percentile products. It takes considerably longer time periods for intercepted rainfall and snowmelt to infiltrate from the upper layers into the lower layers at 40-100 cm and 100-200 cm, or conversely for the deeper soil layer to dry from evapotranspiration processes. Expect anomalies of soil moisture percentiles in the total column 0-200 cm layer to respond to meteorological features on the order of months to years (especially for drying periods), depending on the soil classification and soil responsiveness.Data Caveats:The SPoRT-LIS is as good as the input forcing analyses, so occasional soil moisture artifacts may appear in the horizontal maps related to quality-control issues of the input datasets. These can be manifested with unusually low or high percentiles, especially along international borders, coastlines, and isolated dry “bulls-eyes” at rain gauge with quality issues.Data Visualization:The Soil Moisture Percentile is the histogram rank of the current day’s soil moisture value compared to the 33-year climatology for the present day. The percentile places into historical context the soil moisture to determine how unusually wet or dry, or typical the conditions are. Percentile thresholds as established by the drought community are used to categorize soil moisture dry anomalies can be found here.
This is a dataset containing the potential distribution of 259 invasive terrestrial plant species. We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies and other managers. We applied the modeling workflow developed in Young et al. (2020, https://doi.org/10.1371/journal.pone.0229253) and adapted by Jarnevich et al. (2023, https://doi.org/10.1016/j.ecoinf.2023.101997). We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based on natural history knowledge to narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling (SAHM 2.2.2, Morisette et al., 2013). For each species, we generated up to three groups of models reflecting various levels of suitability including suitability for occurrence, suitability for abundance (>5% cover), and suitability for high abundance (>25% cover), where there were enough data available to create models. For occurrence, we accounted for uncertainty related to sampling bias by using two alternative sources of background samples. For all three groups of models, we constructed weighted ensembles using up to 20 models (occurrence) or 10 models (abundance) for each species. We also combined the three ensembles using three different thresholds converting the continuous values to suitable/unsuitable, ranging from inclusive to restrictive. This data bundle contains a single file of tabular summaries by management unit (including each species/ensemble type/abundance level combination), a file describing the changes from version 3, and a species metadata file. There is also a subfolder for each species that contains the merged data sets used to create models, up to 9 raster files associated with the species, and tabular outputs including response curve data, variable importance information, and model assessment metrics. The potential nine rasters included in each species subfolders represent the following: 1) Occurrence suitability - Continuous value ensemble 2) Abundance suitability - Continuous value ensemble 3) High abundance suitability - Continuous value ensemble 4) Restricted occurrence suitability - Continuous value ensemble with restricted environmental conditions 5) Restricted abundance suitability - Continuous value ensemble with restricted environmental conditions 6) Restricted high abundance suitability - Continuous value ensemble with restricted environmental conditions 7) 0.01 – first percentile threshold applied to model group ensemble 8) 0.05 – fifth percentile threshold applied to model group ensemble 9) 0.1 – tenth percentile threshold applied to model group ensemble Restricted environmental conditions = only display areas where environmental characteristics are inside the range of the values used to develop the model. For example, a location with a minimum winter temperature of 12 C would be outside the range of -10 to 10 C used in model development. The bundle documentation files are: 1) 'project_metadata_INHABIT_V4.xml' (this file) which contains the project-level metadata. 2) managementSummaries.csv is the tabular summaries by management unit. 3) 'INHABIT_VersionHistory.txt' contains information on the methodological changes incurred between this release and the previous data release. 4) 'species_metadata.csv' contains information on specific model changes of each species from tuning algorithm parameters to ensure model quality. 5) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for each species. 6) XX.tif where XX is the raster type explained above. 7) 'responseCurves.csv' is the tabular information need to produce response curves for each predictor retained in each of the 10 models produced for each species. 8) 'variableImportance.csv' is the tabular summaries indicating predictor importance for each of the models produced for each species. 9) 'assessmentMetrics.csv' is the tabular summaries of assessment metrics for each model or ensemble for each species. These data will be integrated into the fourth version of the Invasive Species Habitat Tool (INHABIT), a web application displaying visual and statistical summaries of nationwide habitat suitability models for manager identified invasive plant species. These species include: Abutilon theophrasti (Velvet leaf), Acacia auriculiformis (Earleaf acacia), Acer ginnala (Amur maple), Acer platanoides (Norway maple), Acer pseudoplatanus (Sycamore maple), Achyranthes japonica (Japanese chaff flower), Aegilops cylindrica (Jointed goatgrass), Aegopodium podagraria (Bishop's goutweed), Aesculus hippocastanum (Horse chestnut), Ageratina adenophora (Sticky snakeroot), Agropyron cristatum (Crested wheatgrass), Agrostis stolonifera (Creeping bentgrass), Ailanthus altissima (Tree of heaven), Akebia quinata (Chocolate vine), Albizia julibrissin (Silktree), Alhagi maurorum (Camelthorn), Alliaria petiolata (Garlic mustard), Alyssum alyssoides (Pale alyssum), Ammophila arenaria (European beachgrass), Ampelopsis brevipedunculata (Amur peppervine), Anthemis cotula (Mayweed chamomile), Anthoxanthum odoratum (Sweet vernalgrass), Anthriscus sylvestris (Wild chervil), Aralia elata (Japanese angelica tree), Ardisia crenata (Coral ardisia), Ardisia elliptica (Shoebutton ardisia), Arrhenatherum elatius (Tall oatgrass), Artemisia vulgaris (Common wormwood), Arthraxon hispidus (Small carpet grass), Arum italicum (Italian arum), Arundo donax (Giant reed), Atriplex semibaccata (Australian saltbush), Avena fatua (Wild oat), Berberis bealei (Leatherleaf mahonia), Berberis thunbergii (Japanese barberry), Berteroa incana (Hoary alyssum), Bothriochloa ischaemum (Yellow bluestem), Brachypodium sylvaticum (False brome), Brassica tournefortii (Asian mustard), Briza maxima (Rattlesnake grass), Bromus arvensis (Field brome), Bromus briziformis (Rattlesnake brome), Bromus diandrus (Ripgut brome), Bromus hordeaceus (Soft brome), Bromus inermis (Smooth brome), Bromus japonicus (Japanese brome), Bromus rubens (Red brome), Bromus secalinus (Rye brome), Bromus tectorum (Cheatgrass), Broussonetia papyrifera (Paper mulberry), Bryonia alba (White bryony), Buddleja davidii (Butterfly bush), Butomus umbellatus (Flowering rush), Camelina microcarpa (Smallseed falseflax), Capsella bursa-pastoris (Shepherd's purse), Cardamine impatiens (Narrowleaf bittercress), Cardiospermum halicacabum (Balloon vine), Carduus acanthoides (Spiny plumeless thistle), Carduus nutans (Musk thistle), Carduus pycnocephalus (Italian thistle), Carum carvi (Caraway), Casuarina equisetifolia (Australian pine), Celastrus orbiculatus (Oriental bittersweet), Cenchrus ciliaris (Buffelgrass), Cenchrus purpureus (Elephantgrass), Cenchrus setaceus (Fountaingrass), Centaurea calcitrapa (Purple starthistle), Centaurea diffusa (Diffuse knapweed), Centaurea melitensis (Malta starthistle), Centaurea solstitialis (Yellow starthistle), Centaurea stoebe (Spotted knapweed), Centaurea virgata (Squarrose knapweed), Chondrilla juncea (Rush skeletonweed), Chorispora tenella (Purple mustard), Cinnamomum camphora (Camphortree), Cirsium arvense (Canada thistle), Cirsium vulgare (Bull thistle), Colocasia esculenta (Coco yam), Conium maculatum (Poison hemlock), Convolvulus arvensis (Field bindweed), Cortaderia jubata (Purple pampasgrass), Cortaderia selloana (Pampasgrass), Corydalis incisa (Incised fumewort), Crataegus monogyna (Oneseed hawthorn), Crupina vulgaris (Common crupina), Cynodon dactylon (Bermudagrass), Cynoglossum officinale (Houndstongue), Cytisus scoparius (Scotch broom), Dactylis glomerata (Orchardgrass), Delairea odorata (Cape-ivy), Descurainia sophia (Herb sophia), Dioscorea bulbifera (Air potato), Dipsacus fullonum (Fuller's teasel), Dipsacus laciniatus (Cutleaf teasel), Dittrichia graveolens (Stinkwort), Dolichandra unguis-cati (Cat's claw creeper), Echium vulgare (Viper's bugloss), Elaeagnus angustifolia (Russian olive), Elaeagnus umbellata (Autumn olive), Epilobium hirsutum (Hairy willow-herb), Epipactis helleborine (Broad leaved helleborine), Eragrostis curvula (Weeping lovegrass), Eragrostis lehmanniana (Lehmanns lovegrass), Erodium cicutarium (Redstem filaree), Euonymus alatus (Burning bush), Euonymus fortunei (Wintercreeper), Euphorbia cyparissias (Cypress spurge), Euphorbia esula/virgata (Leafy spurge), Euphorbia myrsinites (Myrtle spurge), Euphorbia oblongata (Eggleaf spurge), Fallopia japonica (Japanese knotweed), Fallopia sachalinensis (Giant knotweed), Fallopia X bohemica (Bohemian knotweed), Foeniculum vulgare (Sweet fennel), Genista monspessulana (French broom), Geranium lucidum (Shining geranium), Geranium robertianum (Robert's geranium), Glechoma hederacea (Ground ivy), Halogeton glomeratus (Halogeton), Hedera helix (English ivy), Hesperis matronalis (Dames rocket), Hieracium aurantiacum (Orange hawkweed), Hieracium caespitosum (Meadow hawkweed), Hieracium piloselloides (Tall hawkweed), Hirschfeldia incana (Shortpod mustard), Holcus lanatus (Velvet grass), Hordeum murinum (Mouse barley), Hyoscyamus niger (Black henbane), Hypericum perforatum (St. johnswort), Hypochaeris radicata (Hairy cat's ear), Ilex aquifolium (English holly), Ilex cornuta (Chinese holly), Impatiens glandulifera (Ornamental jewelweed), Imperata cylindrica (Cogongrass), Iris pseudacorus (Pale yellow iris), Isatis tinctoria (Dyer's woad), Jacobaea vulgaris (Tansy ragwort), Kochia scoparia (Kochia), Koelreuteria elegans (Golden raintree), Lagerstroemia indica (Crape myrtle), Lantana camara (Largeleaf lantana), Lepidium draba (Whitetop), Lepidium latifolium (Perennial pepperweed), Lepidium perfoliatum (Clasping pepperweed), Lespedeza cuneata (Sericea lespedeza), Leucaena leucocephala
We developed habitat suitability models for three invasive plant species: stiltgrass (Microstegium vimineum), sericea lespedeza (Lespedeza cuneata), and privet (Ligustrum sinense). We applied the modeling workflow developed in Young et al. 2020, developing similar models for occurrence data, but also models trained using species locations with percent cover ≥10%, ≥25%, and ≥50%. We chose predictors from a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1) and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We selected background samples using the target background approach, and took an alternative approach to construct model ensembles by combining first percentile and ten percentile threshold rules (suitability values associated with the lowest one percent and lowest ten percent of the training data) to categorize the continuous output from each algorithm into low (below the one percentile), moderate (between the one and ten percentile), and high (above the ten percentile) suitability. Finally, we summed these to create an ensemble. This data bundle contains the merged data sets used to create the models, the composite raster files for each abundance threshold associated with each species, tabular summaries by management unit (including each species/ composite type combination), and the occurrence points with their associated cover. The spatial data are organized in a separate folder for each species, each containing 5 rasters describing potential habitat suitability for the species at the different abundance thresholds. Each of the rasters represent the composite map (composite_abundX.tif) for each abundance threshold. The bundle documentation files are: 1) 'thresholded_abundance_project_metdata.xml' (this file) which contains the project-level metadata 2) 'mergedDataset.csv' contains the merged data set used to create the models, including location and associated environmental data, for all three species for each thresholded abundance. 3) XX.tif where XX is the raster type explained above (abundance threshold). 4) managementSummary.csv is the tabular summaries by management unit.
This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated.
We developed habitat suitability models for occurrence of three invasive riparian woody plant taxa of concern to Department of Interior land management agencies, as well as for three dominant native riparian woody taxa. Study taxa were non-native tamarisk (saltcedar; Tamarix ramosissima, Tamarix chinensis), Russian olive (Elaeagnus angustifolia) and Siberian elm (Ulmus pumila) and native plains/Fremont cottonwood (Populus deltoides ssp. monilifera and ssp. wislizenii, Populus fremontii), narrowleaf cottonwood (Populus angustifolia), and black cottonwood (Populus balsamifera ssp. trichocarpa and ssp. balsamifera). We generally followed the modeling workflow developed in Young et al. 2020. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples: random (10,000 spatially-filtered (50-kilometer [km]) random background samples) and Salix (10,000 randomly-selected occurrence records of Salix spp.). We constructed model ensembles with the 5 models for each taxon (five algorithms) with each background method, as well as with all 10 models for each taxon (five algorithms by two background methods), for three different occurrence likelihood thresholds (1st percentile, 10th percentile, and MSS (maximum sensitivity and specificity)). We also used the model ensembles to identify major watersheds where each taxon was under-represented in occurrence records relative to predicted habitat suitability, to evaluate risk of undetected or future invasion. For each 6-digit hydrological unit (HUC6, USGS Watershed Boundary Dataset) within the study area, we calculated the difference between actual occurrence record density and the density of occurrence records that would be expected if occurrence records were distributed among watersheds in proportion to habitat suitability in MaxSS 10-model ensembles. This data bundle contains the merged data sets used to create the models, occurrence locations that were used for independent assessments of model accuracy (not used in model training), the raster files associated with each taxon, and tabular summaries of actual and expected occurrence record densities by HUC6. The spatial data are organized in a separate folder for each taxon, each containing 9 rasters. Each of the rasters represent the following: 1) X1st_random - ensemble of 5 models with random background data and 1st percentile threshold 2) X10th_random - ensemble of 5 models with random background data and 10th percentile threshold 3) MaxSS_random - ensemble of 5 models with random background data and MaxSS threshold 4) X1st_Salix_1st - ensemble of 5 models with random background data and 1st percentile threshold 5) X10th_Salix - ensemble of 5 models with random background data and 10th percentile threshold 6) MaxSS_Salix - ensemble of 5 models with random background data and MaxSS threshold 7) X1st_combined - ensemble of 10 models with random and Salix background data and 1st percentile threshold 8) X10th_combined - ensemble of 10 models with random and Salix background data and 10th percentile threshold 9) MaxSS_combined - ensemble of 10 models with random and Salix background data and MaxSS threshold The bundle documentation files are: 1) 'RiparianSDMs_main.xml' (this file), which contains the project-level metadata 2) 'ModelTrainingData.csv' contains the merged data set used to create the models, including location and environmental data. 3) 'IndependentAssessmentData.csv' contains the data set used to assess accuracy of model predictions (occurrence locations not used for model training) 4) XX.tif where XX is the raster type explained above in taxa subfolders. 5) 'HUC6Summaries.csv' contains tabular summaries of actual and expected occurrence record densities by HUC6. 6) 'bison_citations.txt' contains the different data sources with occurrences from the BISON database. This file specifically describes the ModelTrainingData.csv that includes the location data and associated predictor variable values used to train the habitat suitability models.
Solar Footprints in CaliforniaThis GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy https://www.blm.gov/style/medialib/blm/wo/MINERALS_REALTY_AND_RESOURCE_PROTECTION_/energy/solar_and_wind.Par.70101.File.dat/Public%20Webinar%20Dec%203%202014%20-%20Solar%20and%20Wind%20Regulations.pdfBLM CA Renewable Energy Projects | BLM GBP Hub (arcgis.com)Metadata: (5) Quarterly Fuel and Energy Report (QFER) California Power Plants - Overview (arcgis.com)
We developed habitat suitability models for occurrence of three invasive riparian woody plant taxa of concern to Department of Interior land management agencies, as well as for three dominant native riparian woody taxa. Study taxa were non-native tamarisk (saltcedar; Tamarix ramosissima, Tamarix chinensis), Russian olive (Elaeagnus angustifolia) and Siberian elm (Ulmus pumila) and native plains/Fremont cottonwood (Populus deltoides ssp. monilifera and ssp. wislizenii, Populus fremontii), narrowleaf cottonwood (Populus angustifolia), and black cottonwood (Populus balsamifera ssp. trichocarpa and ssp. balsamifera). We generally followed the modeling workflow developed in Young et al. 2020. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples: random (10,000 spatially-filtered (50-kilometer [km]) random background samples) and Salix (10,000 randomly-selected occurrence records of Salix spp.). We constructed model ensembles with the 5 models for each taxon (five algorithms) with each background method, as well as with all 10 models for each taxon (five algorithms by two background methods), for three different occurrence likelihood thresholds (1st percentile, 10th percentile, and MSS (maximum sensitivity and specificity)). We also used the model ensembles to identify major watersheds where each taxon was under-represented in occurrence records relative to predicted habitat suitability, to evaluate risk of undetected or future invasion. For each 6-digit hydrological unit (HUC6, USGS Watershed Boundary Dataset) within the study area, we calculated the difference between actual occurrence record density and the density of occurrence records that would be expected if occurrence records were distributed among watersheds in proportion to habitat suitability in MaxSS 10-model ensembles. This data bundle contains the merged data sets used to create the models, occurrence locations that were used for independent assessments of model accuracy (not used in model training), the raster files associated with each taxon, and tabular summaries of actual and expected occurrence record densities by HUC6. The spatial data are organized in a separate folder for each taxon, each containing 9 rasters. Each of the rasters represent the following: 1) X1st_random - ensemble of 5 models with random background data and 1st percentile threshold 2) X10th_random - ensemble of 5 models with random background data and 10th percentile threshold 3) MaxSS_random - ensemble of 5 models with random background data and MaxSS threshold 4) X1st_Salix_1st - ensemble of 5 models with random background data and 1st percentile threshold 5) X10th_Salix - ensemble of 5 models with random background data and 10th percentile threshold 6) MaxSS_Salix - ensemble of 5 models with random background data and MaxSS threshold 7) X1st_combined - ensemble of 10 models with random and Salix background data and 1st percentile threshold 8) X10th_combined - ensemble of 10 models with random and Salix background data and 10th percentile threshold 9) MaxSS_combined - ensemble of 10 models with random and Salix background data and MaxSS threshold The bundle documentation files are: 1) 'RiparianSDMs_main.xml' (this file), which contains the project-level metadata 2) 'ModelTrainingData.csv' contains the merged data set used to create the models, including location and environmental data. 3) 'IndependentAssessmentData.csv' contains the data set used to assess accuracy of model predictions (occurrence locations not used for model training) 4) XX.tif where XX is the raster type explained above in taxa subfolders. 5) 'HUC6Summaries.csv' contains tabular summaries of actual and expected occurrence record densities by HUC6. 6) 'bison_citations.txt' contains the different data sources with occurrences from the BISON database. This file specifically describes the IndependentAssessmentData.csv that includes the data used to assess the accuracy of the model predictions.
Update Frequency:DailySummary:Land Information System (LIS) 0-200 cm layer Soil Moisture Percentile generated by the NASA SPoRT Center over a Contiguous United States domain.The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications and against U.S. Drought Monitor products.SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2 through 4 days prior to the current time, and by the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates from 4 days ago to the present time. A unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of VIIRS Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model.The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific day of the year. Soil moisture percentile maps are constructed for the model layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product.Suggested Use:This product can be used for drought assessment, fire risk assessment, potential for flooding hazards associated with heavy precipitation and high percentiles; contextualizing soil moisture content to historical values.Soil moisture percentiles are shown using a Classified Color Ramp (Multi-Color, 11-classes) that colorize the low percentile categories (≤ 30th) as shown in the U.S. Drought Monitor weekly products, ranging from yellow to dark red. The high percentile categories (≥ 70th) are colorized with increasing blue intensity. Intermediate percentiles in the 30th to 70th range are assigned a nominal gray shade.The 0-200 cm layer combines SPoRT-LIS soil moisture analyses from all four model layers 0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm depths. The 0-200 cm cumulative layer adjusts slowly to precipitation episodes or the lack thereof compared to the other cumulative layered percentile products. It takes considerably longer time periods for intercepted rainfall and snowmelt to infiltrate from the upper layers into the lower layers at 40-100 cm and 100-200 cm, or conversely for the deeper soil layer to dry from evapotranspiration processes. Expect anomalies of soil moisture percentiles in the total column 0-200 cm layer to respond to meteorological features on the order of months to years (especially for drying periods), depending on the soil classification and soil responsiveness.Data Caveats:The SPoRT-LIS is as good as the input forcing analyses, so occasional soil moisture artifacts may appear in the horizontal maps related to quality-control issues of the input datasets. These can be manifested with unusually low or high percentiles, especially along international borders, coastlines, and isolated dry “bulls-eyes” at rain gauge with quality issues.Data Visualization:The Soil Moisture Percentile is the histogram rank of the current day’s soil moisture value compared to the 33-year climatology for the present day. The percentile places into historical context the soil moisture to determine how unusually wet or dry, or typical the conditions are. Percentile thresholds as established by the drought community are used to categorize soil moisture dry anomalies can be found here.
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Context
The dataset presents a breakdown of households across various income brackets in Alaska, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Alaska reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Alaska households based on income levels.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income Levels:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Alaska median household income. You can refer the same here
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The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.