About this dataset

Context

This list ranks the 365 cities in the Florida by Australian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Australian Population: This column displays the rank of city in the Florida by their Australian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Australian Population: The Australian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Australian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Florida Australian Population: This tells us how much of the entire Florida Australian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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.

Inspiration

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/.

Australia Population: Growth data was reported at 2.371 % in 2023. This records an increase from the previous number of 1.273 % for 2022. Australia Population: Growth data is updated yearly, averaging 1.447 % from Dec 1961 (Median) to 2023, with 63 observations. The data reached an all-time high of 3.380 % in 1971 and a record low of 0.141 % in 2021. Australia Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2022 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.;Weighted average;

Comprehensive statistical dataset for Europe, Northern America, Australia, and New Zealand including demographic, economic, and social indicators for the year 2025.

Nice underwater photo of Nautilus in American Samoa

Context

The outbreak of COVID19 pushed Kaggle to launch several competitions to better understand how the new virus spreads.

The data provided by this competition is not only divided by nation (China, US, Canada...), but also sometimes by province/state/dependency/territory (California, Hubei, French Guiana, Saskatchewan...).

Although there are already several Kaggle datasets that provide population estimates by nation, I couldn't find any that provided a population estimate for each one of the constituent states ("provinces/states") included in the data for the 2nd week COVID19 Global Forecasting competition. So here they are, packaged in a super simple two-column CSV file.

Content

Each row in this dataset is a rough estimate of the population in each of the constituent states that appear in the COVID19 Global Forecasting competition. Each row is, of course, one of these inner states. By "constituent state" I mean one of: - the 54 United States of America - the 33 Chinese provinces - 10 Canadian provinces (plus a territory, Northwest Territories) - 11 French overseas territories - 10 British overseas territories - 6 Australian states (plus 2 internal territories) - 5 constituent countries of the Kingdom of the Netherlands - 2 autonomous Danish territories (Faroe Islands and Greenland)

In total, 134 states are listed.

Acknowledgements

The population estimates were collected from the following sources: - Australia: Wikipedia - Canada: worldpopulationreview.com - China: another Kaggle dataset - Denmark: worldpopulationreview.com - France: worldometers.info (retrieved 2020-04-02, 18:00 UTC) - Netherlands: worldometers.info (retrieved 2020-04-02, 18:00 UTC) - US: worldpopulationreview.com - Guam: worldpopulationreview.com - UK: worldometers.info (retrieved 2020-04-02, 18:00 UTC)

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

Property Description

Hylak_id Unique lake identifier. Values range from 1 to 1,427,688.

**Lake_name ** Name of lake or reservoir. This field is currently only populated for lakes with an area of at least 500 km2; for large reservoirs where a name was available in the GRanD database; and for smaller lakes where a name was available in the GLWD database.

Country Country that the lake (or reservoir) is located in. International or transboundary lakes are assigned to the country in which its corresponding lake pour point is located and may be arbitrary for pour points that fall on country boundaries.

Continent Continent that the lake (or reservoir) is located in. Geographic continent: Africa, Asia, Europe, North America, South America, or Oceania (Australia and Pacific Islands)

Poly_src The name of datasets that were used in the column. Source of original lake polygon: CanVec; SWBD; MODIS; NHD; ECRINS; GLWD; GRanD; or Other More information on these data sources can be found in Table 1.

Lake_type Indicator for lake type: 1: Lake 2: Reservoir 3: Lake control (i.e. natural lake with regulation structure) Note that the default value for all water bodies is 1, and only those water bodies explicitly identified as other types (mostly based on information from the GRanD database) have other values; hence the type ‘Lake’ also includes all unidentified smaller human-made reservoirs and regulated lakes.

Grand_id ID of the corresponding reservoir in the GRanD database, or value 0 for no corresponding GRanD record. This field can be used to join additional attributes from the GRanD database.

Lake_area Lake surface area (i.e. polygon area), in square kilometers.

Shore_len Length of shoreline (i.e. polygon outline), in kilometers.

Shore_dev Shoreline development, measured as the ratio between shoreline length and the circumference of a circle with the same area. A lake with the shape of a perfect circle has a shoreline development of 1, while higher values indicate increasing shoreline complexity.

Vol_total Total lake or reservoir volume, in million cubic meters (1 mcm = 0.001 km3). For most polygons, this value represents the total lake volume as estimated using the geostatistical modeling approach by Messager et al. (2016). However, where either a reported lake volume (for lakes ≥ 500 km2) or a reported reservoir volume (from GRanD database) existed, the total volume represents this reported value. In cases of regulated lakes, the total volume represents the larger value between reported reservoir and modeled or reported lake volume. Column ‘Vol_src’ provides additional information regarding these distinctions.

Vol_res Reported reservoir volume, or storage volume of added lake regulation, in million cubic meters (1 mcm = 0.001 km3). 0: no reservoir volume

Vol_src 1: ‘Vol_total’ is the reported total lake volume from literature 2: ‘Vol_total’ is the reported total reservoir volume from GRanD or literature 3: ‘Vol_total’ is the estimated total lake volume using the geostatistical modeling approach by Messager et al. (2016)

Depth_avg Average lake depth, in meters. Average lake depth is defined as the ratio between total lake volume (‘Vol_total’) and lake area (‘Lake_area’).

Dis_avg Average long-term discharge flowing through the lake, in cubic meters per second. This value is derived from modeled runoff and discharge estimates provided by the global hydrological model WaterGAP, downscaled to the 15 arc-second resolution of HydroSHEDS (see section 2.2 for more details) and is extracted at the location of the lake pour point. Note that these model estimates contain considerable uncertainty, in particular for very low flows. -9999: no data as lake pour point is not on HydroSHEDS landmask

Res_time Average residence time of the lake water, in days. The average residence time is calculated as the ratio between total lake volume (‘Vol_total’) and average long-term discharge (‘Dis_avg’). Values below 0.1 are rounded up to 0.1 as shorter residence times seem implausible (and likely indicate model errors). -1: cannot be calculated as ‘Dis_avg’ is 0 -9999: no data as lake pour point is not on HydroSHEDS landmask

Elevation Elevation of lake surface, in meters above sea level. This value was primarily derived from the EarthEnv-DEM90 digital elevation model at 90 m pixel resolution by calculating the majority pixel elevation found within the lake boundaries. To remove some artefacts inherent in this DEM for northern latitudes, all lake values that showed negative elevation for the area north of 60°N were substituted with results using the coarser GTOPO30 DEM of USGS at 1 km pixel resolution, which ensures land surfaces ≥0 in this region. Note that due to the remaining uncertainties in the EarthEnv-DEM90 some small negative values occur along the global oce...

The Aboriginal & Torres Strait Islander Social Health Atlas data presenting the latest Aboriginal & Torres Strait Islander (ATSI) Social Health Atlas indicators are available by Indigenous Areas, including totals for the Capital cities/ Rest of States/Territories, States/ Territories and Australia. Note: The Department of Health has approved for release a set of population estimates by Indigenous status for 2011, and projections to 2016 by Statistical Areas Level 2, Indigenous Region and Primary Health Network. To obtain these data, please contact us. Attribution: Torrens University Australia

This dataset presents counts of payment recipients by payment type for the quarter ending December 2019. The data is based on the recipient's geocoded address aggregated by Statistical Area Level 2 (SA2) from the Australian Statistical Geography Standard (ASGS) 2016.

The Department of Social Services (DSS) is the Australian Government's lead agency in the development and delivery of social policy, and is working to improve the lifetime wellbeing of people and families in Australia. DSS' policies and services respond to need across people's lives - looking after families, children and older people; providing a safety net for people who cannot fully support themselves; enhancing the wellbeing of people with high needs; assisting people who need help with care; and supporting a diverse and harmonious society. DSS supports people and families in Australia by encouraging independence and participation, and supporting a cohesive society.

For more information on this dataset please visit the Australian Government Open Data.

For further details on payments, see 'A guide to Australian Government payments'.

Please note:

• In order to protect individuals' privacy, identified populations between 1 and 4 have been suppressed and replaced with '<5' for confidentiality purposes. Additional data may be suppressed and replaced with 'n.p.' (not provided) to prevent the derivation of identified populations that have values of less than 5.

• Caveats specific to payments type can be found in the relevant payment description of the original data.

  AURIN has spatialised this dataset and have made the following replacements to enforce data type consistency:

• '<5' has been assigned a value of '-1' within the data

• A boolean field has been created for each data field to flag left-censored data. 'True' is assigned to fields that have been censored.

• 'n.p.' (not provided) has been set to Null within the data.

Abstract

This dataset, a product of the Trade Team - Development Research Group, is part of a larger effort in the group to measure the extent of the brain drain as part of the International Migration and Development Program. It measures international skilled migration for the years 1975-2000.

The methodology is explained in: "Tendance de long terme des migrations internationals. Analyse à partir des 6 principaux pays recerveurs", Cécily Defoort.

This data set uses the same methodology as used in the Docquier-Marfouk data set on international migration by educational attainment. The authors use data from 6 key receiving countries in the OECD: Australia, Canada, France, Germany, the UK and the US.

It is estimated that the data represent approximately 77 percent of the world’s migrant population.

Bilateral brain drain rates are estimated based observations for every five years, during the period 1975-2000.

Geographic coverage

Australia, Canada, France, Germany, UK and US

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The size and distribution of home ranges reflects how individuals within a population use, defend, and share space and resources, and may thus be an important predictor of population-level dynamics. In eruptive species like the house mouse in Australian grain growing regions, contrasting space use between stable and outbreaking populations allows us to test predictions regarding social or life history strategies that may contribute to an outbreak. In this study we use spatially explicit capture-recapture models to compare home range overlap (as a proxy for territoriality) in female mice from populations showing different outbreak trajectories. We found that female space use in spring varied between outbreaking and stable populations. Our analysis indicated greater home range overlap in populations with stable trajectories compared to those that would later experience an outbreak, suggesting females in these stable populations may have had greater potential for cooperative group formatio..., The data used for this study were collected from grain paddocks located near Mallala on the Adelaide Plains, South Australia (SA) (-34° 26' 59.99" S, 138° 29' 59.99" E) (paddocks A and B,) and near Parkes, Central West, New South Wales (NSW) (33° 8' 12.56'' S, 148° 10' 22.93'' E) (paddocks C and D). In each paddock (n= 4), two independent live-capture trapping grids, each separated by a minimum of 100 m were established (n= 8 trapping grids). On each live trapping grid, 64 single capture Longworth traps (25 × 6.5 × 8.5 cm, Longworth Scientific, Abingdon, UK) were set at 10 m intervals on an 8 x 8 grid. Traps contained polyester fibre bedding and wheat grains for food. They were checked and closed each morning starting at approximately 0630 hours (h) and opened in the evening at 1700 h. Traps are designed for single capture, however during this study some traps had multiple captures on a single trap night. Near Mallala SA (paddocks A and B), trapping data were collected between October ..., , # Data from: Contrasting patterns of female house mouse spatial organisation among outbreaking and stable populations

This dataset includes house mouse (Mus musculus) capture- mark recapture data collected from grain cropping paddocks in Australia. Data was collected from two grain cropping regions near Mallala South Australia during 2019/20, and Parkes New South Wales during 2020/21.

Description of the data and file structure

File: House mouse capture mark recapture (Van_de_Weyer_et_al_House_Mouse_Captures_Raw_Data.txt) This text file contains house mouse capture mark recapture data from live trapping grids in Australian grain cropping systems.

Table 1. House mouse capture- mark recapture metadata

| Column name | Description | | ----------- | -----------------------------------------------------------------------------------------------------------...

Detta dataset presenterar information över statens skatteintäkter för 31 länder i Europa, Nordamerika och Sydamerika från 1800 (eller självständighet) till 2012. Länderna i datasetet är: Argentina, Australien, Österrike, Belgien, Bolivia, Brasilien, Kanada, Chile, Colombia, Danmark, Ecuador, Finland, Frankrike, Tyskland (Västtyskland mellan 1949 och 1990), Irland, Italien, Japan, Mexiko, Nya Zeeland, Norge, Paraguay, Peru, Portugal, Spanien, Sverige, Schweiz, Nederländerna, USA Storbritannien, USA, Uruguay och Venezuela. Med andra ord innehåller datasetet alla sydamerikanska, nordamerikanska och västeuropeiska länder med en befolkning på mer än en miljon plus Australien, nya Zeeland, Japan och Mexiko. Datasetet innehåller information om den centrala statens offentliga finanser. För att göra denna information jämförbar mellan länder har vi valt att normalisera de nominella intäktssiffrorna på två sätt: (i) som en andel av den totala budgeten och (ii) som en andel av den totala bruttonationalprodukten. Den centrala statens totala skatteintäkter är uppdelade baserat på Internationella valutafondens (IMF) handbok över statsfinanser från 2001. Denna ger en klassificering av intäktstyper och beskriver innehållet i varje klassificeringskategori. Med tanke på den bristfälliga historiska datan och våra projektbehov kombinerade vi några underkategorier. Till att börja med är vi intresserade av totala skatteintäkter (centaxtot), liksom andelarna av totala intäkter som kommer från direkta (centaxdirectsh) och indirekta (centaxindirectsh) skatter. Vidare mäter vi två underkategorier av direkt beskattning, nämligen skatter på egendom (centaxpropertysh) och inkomst (centaxincomesh). För indirekta skatter skiljer vi på punktskatter (centaxexcisesh), konsumtion (centaxconssh) och tullar (centaxcustomssh).

För en mer detaljerad beskrivning av datan och insamlingsprocessen, se kodboken som finns tillgänlig i .zip-filen.

Syfte:

Detta dataset presenterar information över statens skatteintäkter för 31 länder i Europa, Nordamerika och Sydamerika från 1800 (eller självständighet) till 2012. Länderna i datasetet är: Argentina, Australien, Österrike, Belgien, Bolivia, Brasilien, Kanada, Chile, Colombia, Danmark, Ecuador, Finland, Frankrike, Tyskland (Västtyskland mellan 1949 och 1990), Irland, Italien, Japan, Mexiko, Nya Zeeland, Norge, Paraguay, Peru, Portugal, Spanien, Sverige, Schweiz, Nederländerna, USA Storbritannien, USA, Uruguay och Venezuela. Med andra ord innehåller datasetet alla sydamerikanska, nordamerikanska och västeuropeiska länder med en befolkning på mer än en miljon plus Australien, nya Zeeland, Japan och Mexiko. Datasetet innehåller information om den centrala statens offentliga finanser. För att göra denna information jämförbar mellan länder har vi valt att normalisera de nominella intäktssiffrorna på två sätt: (i) som en andel av den totala budgeten och (ii) som en andel av den totala bruttonationalprodukten. Den centrala statens totala skatteintäkter är uppdelade baserat på Internationella valutafondens (IMF) handbok över statsfinanser från 2001. Denna ger en klassificering av intäktstyper och beskriver innehållet i varje klassificeringskategori. Med tanke på den bristfälliga historiska datan och våra projektbehov kombinerade vi några underkategorier. Till att börja med är vi intresserade av totala skatteintäkter (centaxtot), liksom andelarna av totala intäkter som kommer från direkta (centaxdirectsh) och indirekta (centaxindirectsh) skatter. Vidare mäter vi två underkategorier av direkt beskattning, nämligen skatter på egendom (centaxpropertysh) och inkomst (centaxincomesh). För indirekta skatter skiljer vi på punktskatter (centaxexcisesh), konsumtion (centaxconssh) och tullar (centaxcustomssh).

This dataset, released August 2017, contains the Australian residents population by their birthplace divided into English speaking (ES) and non-English speaking (NES) countries, 2016. The following …Show full descriptionThis dataset, released August 2017, contains the Australian residents population by their birthplace divided into English speaking (ES) and non-English speaking (NES) countries, 2016. The following countries are designated as ES: Canada, Ireland, New Zealand, South Africa, United Kingdom and the United States of America; the remaining countries are designated as NES. The dataset also includes the population people born overseas and report poor proficiency in English. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

Sweetpotato (Ipomoea batatas) plays a critical role in food security and is the most important root crop worldwide following potatoes and cassava. In the United States (US), it is valued at over $700 million USD. There are two sweetpotato germplasm collections (Plant Genetic Resources Conservation Unit and US Vegetable Laboratory) maintained by the USDA, ARS for sweetpotato crop improvement. To date, no genome-wide assessment of genetic diversity within these collections has been reported in the published literature. In our study, population structure and genetic diversity of 417 USDA sweetpotato accessions originating from 8 broad geographical regions (Africa, Australia, Caribbean, Central America, Far East, North America, Pacific Islands, and South America) were determined using single nucleotide polymorphisms (SNPs) identified with a genotyping-by-sequencing (GBS) protocol, GBSpoly, optimized for highly heterozygous and polyploid species. Population structure using Bayesian clustering analyses (STRUCTURE) with 32,784 segregating SNPs grouped the accessions into four genetic groups and indicated a high degree of mixed ancestry. A neighbor-joining cladogram and principal components analysis based on a pairwise genetic distance matrix of the accessions supported the population structure analysis. Pairwise FST values between broad geographical regions based on the origin of accessions ranged from 0.017 (Far East – Pacific Islands) to 0.110 (Australia – South America) and supported the clustering of accessions based on genetic distance. The markers developed for use with this collection of accessions provide an important genomic resource for the sweetpotato community, and contribute to our understanding of the genetic diversity present within the US sweetpotato collection and the species.

This dataset provides county-level data for Nitrogen fertilizer applied to county croplands [1000 kg N/yr]. This includes only those crops used in an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. Cropland area statistics are from the National Agricultural Statistical Service (NASS) for 1990 for most crops, though some are 1992 data from the Census of Agriculture. Data represent total of irrigated and non-irrigated areas. (see NASS Crops County Data).

This is based on 'typical' nitrogen fertilization rates for each of the crops. The fertilizer application rates (see Table below) were derived from USDA NASS state agricultural statistics bulletins.

Crop Typical' N Fert. Rate (kg N/ha) Alfalfa 0 Barley 75 Corn (grain & silage) 125 Cotton 100 Edible Bean 0 Idle Cropland 0 Non-Legume Hay 25 Oats 75 Pasture 0 Peanut 0 Potatoes 250 Rice 140 Sorghum 75 Soybean 0 Spring Wheat 50 Sugarbeets 150 Sugarcane 200 Sunflower 100 Tobacco 100 Vegetables 100 Winter Wheat 75

County crop areas were multiplied by the nitrogen fertilization rates given above to determine total N-fertilization of these croplands per year. The 1990 national total N fertilizer use calculated by this method (8.5 million tonnes N/yr) is 83% of the 1990 national N-fertilizer sales (10.3 million tonnes N/yr). The sales total is expected to be larger because it will include fertilizer sold for other uses (eg. lawns, golf courses, other non-crop uses) as well as farm-use fertilizer applied to crops not included in the crop database (eg. vineyards, orchards, sod). The source for N fertilizer sales is American Assoc. of Plant Food Control Officials, 103 Regulatory Services Building; University of Kentucky; Lexington, KY 40546-0275; Phone (606)257-2668 fax (606)257-7351.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

The Prompt Assessment of Global Earthquakes for Response (PAGER) System plays a primary alerting role for global earthquake disasters as part of the U.S. Geological Surveys (USGS) response protocol. PAGER monitors the USGSs near real-time U.S. and global earthquake origins and automatically identifies events that are of societal importance, well in advance of ground-truth or news accounts. Current PAGER notifications and Web pages estimate the population exposed to each seismic intensity level. In addition to being a useful indicator of potential impact, PAGERs intensity/exposure display provides a new standard in the dissemination of rapid earthquake information. This paper provides an overview of the PAGER system, both of its current capabilities and ongoing research and development. Specifically, this paper summarises the underpinning models and datasets developed to improve PAGER exposure and impact modules. These include: global site-response models, enhanced earthquake source and loss databases, the Atlas of ShakeMaps and population exposure catalogue, and a global building inventory. The use of these methods and databases are demonstrated using the USGSs response to the 12 May 2008 Wenchuan, China, earthquake. Finally, we comment on the potential use of PAGER tools and databases for improved near real-time earthquake alerting in Australia.

Australia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 12.700 % in 2018. This records an increase from the previous number of 12.200 % for 2016. Australia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 12.200 % from Dec 1981 (Median) to 2018, with 12 observations. The data reached an all-time high of 13.200 % in 1989 and a record low of 11.200 % in 2014. Australia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

BackgroundAll organisms may be affected by humans' increasing impact on Earth, but there are many potential drivers of population trends and the relative importance of each remains largely unknown. The causes of spatial patterns in population trends and their relationship with animal responses to human proximity are even less known.Methodology/Principal FindingWe investigated the relationship between population trends of 193 species of bird in North America, Australia and Europe and flight initiation distance (FID); the distance at which birds take flight when approached by a human. While there is an expected negative relationship between population trend and FID in Australia and Europe, we found the inverse relationship for North American birds; thus FID cannot be used as a universal predictor of vulnerability of birds. However, the analysis of the joint explanatory ability of multiple drivers (farmland breeding habitat, pole-most breeding latitude, migratory habit, FID) effects on population status replicated previously reported strong effects of farmland breeding habitat (an effect apparently driven mostly by European birds), as well as strong effects of FID, body size, migratory habit and continent. Farmland birds are generally declining.Conclusions/SignificanceFlight initiation distance is related to population trends in a way that differs among continents opening new research possibilities concerning the causes of geographic differences in patterns of anti-predator behavior.