This is a Dataset of the World Population Consisting of Each and Every Country. I have attempted to analyze the same data to bring some insights out of it. The dataset consists of 234 rows and 17 columns. I will analyze the same data and bring the below pieces of information regarding the same.

1. Continent Population Characteristics Analysis.
2. Analysis of Countries.
   • Top 10 Most Populated and Least Populated Countries
   • Top 10 Largest and Smallest Countries as per Area
   • Population Growth From 1970 to 2020 (50 Years)
3. Countries Represent % Of World Population.
   • Countries that represent below 0.1% of the World Population.
   • Countries that represent above 2% of the world Population
   • Top 10 Over Populated Countries based on Density Per Sq KM.
   • Top 10 Least Populated Countries based on Density Per Sq KM.

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

This data set has been generated using data from the Gapminder website, which focuses on gathering and sharing statistics and other information about social, economic and environmental development at local, national and global levels.

This particular data set describes the values of several parameters (see the list below) between 1998 and 2018 for a total of 175 countries, having a total of 3675 rows. The parameters included in the data set and the column name of the dataframe are as follows:

• Country (country). Describes the country name
• Continent (continent). Describes the continent to which the country belongs
• Year (year). Describes the year to which the data belongs
• Life expectancy (life_exp). Describes the life expectancy for a given country in a given year
• Human Development Index (hdi_index). Describes the HDI index value for a given country in a given year
• CO2 emissions per person(co2_consump). Describes the CO2 emissions in tonnes per person for a given country in a given year
• Gross Domestic Product per capita (gdp). Describes the GDP per capita in dollars for a given country in a given year
• % Service workers (services). Describes the the % of service workers for a given country in a given year

The Gridded Population of the World, Version 3 (GPWv3): Population Count Grid, Future Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population counts that the grids are derived from are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

Homininos_DataSet(1).csv is the original///////// Homininos_DataSet.csv It already has the categorical values ​​encoded

Exploring Human Evolution Through a Comprehensive Dataset

Introduction:

In this dataset, we delve into the fascinating story of human evolution. With 720 rows and 28 columns, this dataset covers a wide range of characteristics of different hominids, from the earliest consensual ancestors to modern Homo sapiens. This comprehensive compilation aims to facilitate the search for relationships between various key variables, thereby providing a more complete and detailed understanding of human evolution.

Objectives:

The main objective of this dataset is to facilitate the exploration and understanding of human evolution from a broader and more detailed perspective. Some specific objectives include:

Seeking relationships between important columns of the dataset. Understanding human evolution considering the collected data. Investigating the possible linearity of evolution over time. Analyzing potential relationships between brain size, developed technologies, diet, and physiological modifications over time. Significance:

This dataset is crucial for advancing our understanding of human evolution and history. It provides a solid foundation for research in various fields, from anthropology and evolutionary biology to archaeology and genetics. By allowing us to examine relationships and patterns among different variables, this dataset helps us trace the course of human evolution and gain a better understanding of our place in the tree of life.

Conclusions:

In summary, this comprehensive dataset provides us with a valuable tool for exploring human evolution in depth. With its numerous rows and columns, it allows us to delve into the complexity and diversity of our evolutionary history. By analyzing and understanding the collected data, we can gain new insights into how we have come to be what we are today and how our species has evolved over time.

This dataset not only expands our knowledge of human evolution but also inspires us to continue researching and discovering more about our shared past as a species.

I studied Biological Anthropology for 4 years at the National University of La Palta, and I had the opportunity to compile these data from classes and books such as Carbonell's "Homínidos: las primeras ocupaciones de los continentes," published in 2005.

INFO About Columns: Genus & Species: (categorical) This column contains the genus and specific name of the species. It provides taxonomic information about each hominid included in the dataset, allowing for precise identification

Time : (categorical) This column indicates the time period during which each hominid species lived. It helps to establish chronological context and understand the temporal distribution of different hominid groups.

Location: (categorical) This column records the continent location where each hominid species lived.

Zone: (categorical) Describes either east, west, south or north of the continent

Current Country: (categorical) Records the modern-day country associated with the location where each hominid species lived, facilitating geographical comparisons.

Habitat: (categorical) This column describes the typical habitat or environment inhabited by each hominid species. It provides information about the ecological niche and adaptation strategies of different hominids throughout history.

Cranial Capacity: (numeric) This column provides data on the cranial capacity of each hominid species. Cranial capacity is a key indicator of brain size and can offer insights into cognitive abilities and evolutionary trends.

Height: (numeric) Describes the average height or stature of each hominid species

Incisor Size: (categorical) Indicates the size of the incisors in each hominid species

Jaw Shape: (categorical) Describes the shape or morphology of the jaw in each hominid species

Torus Supraorbital: (categorical) Specifies the shape or morphology of a supraorbital torus in each hominid species

Prognathism: (categorical) Indicates the degree of facial prognathism or protrusion in each hominid species

Foramen Mágnum Position: (categorical) Describes the position of the foramen magnum in each hominid species

Canine Size: (categorical) Indicates the size of the canines in each hominid species

Canines Shape: (categorical) Describes the shape of the canines in each hominid species, providing information about their dietary adaptations and social behavior.

Tooth Enamel: (categorical) Specifies the characteristics of tooth enamel in each hominid species, which may indicate aspects of dietary ecology and dental health.

Tecno: (categorical) Records the presence or absence of technological advancements

Tecno Type: (categorical) Describes the specific type or style of technology associated with each hom...

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 Gridded Population of the World, Version 3 (GPWv3): Population Count Grid, Future Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population counts that the grids are derived from are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population count grids contain estimates of the number of persons per grid cell. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

In recent years, the African continent has emerged as a hub of growth, progress, and cultural diversity. With 54 recognized sovereign nations, Africa boasts a tapestry of vibrant cultures, breathtaking landscapes, and promising economies. From the vast deserts of the Sahara to the lush rainforests of the Congo Basin, each African nation has a unique story to tell. In this article, we will delve into the diverse and dynamic African nations, shedding light on their rich history, cultural heritage, and economic advancements. Africa, often referred to as the cradle of civilization, is home to some of the oldest human civilizations on Earth. Egypt, with its iconic pyramids and ancient pharaohs, stands as a testament to the continent's remarkable heritage. The Nile River, the lifeblood of ancient Egyptian civilization, continues to be a source of sustenance and culture today. Moving southwards, we encounter nations like Nigeria, the most populous country in Africa, and South Africa, known for its vibrant multicultural society. Nigeria, with its rich cultural tapestry, has produced renowned artists, musicians, and authors who have made significant contributions to the global cultural landscape. South Africa, on the other hand, is celebrated for its remarkable post-apartheid transition, vibrant democracy, and impressive economic growth. Venturing into East Africa, we encounter Ethiopia, often referred to as the "cradle of humanity" due to the discovery of the oldest known human remains in the region. Ethiopia showcases a unique blend of ancient traditions and modern development, with its stunning landscapes, diverse wildlife, and rich historical sites attracting visitors from around the world. In West Africa, Ghana stands as a shining example of political stability and economic progress. Known as the "Gateway to Africa," Ghana has made significant strides in areas such as education, healthcare, and infrastructure, positioning itself as an attractive investment destination. The continent's southern region features nations like Botswana, known for its commitment to wildlife conservation and sustainable tourism. With its vast national parks and awe-inspiring wildlife, Botswana offers visitors a chance to experience Africa's natural wonders firsthand. As we travel across the continent, it becomes evident that Africa's potential for growth and development is immense. From the technological advancements in countries like Rwanda to the agricultural innovations in Kenya, African nations are harnessing their resources and investing in their future. Furthermore, regional collaborations such as the African Union and the African Continental Free Trade Area (AfCFTA) are fostering deeper economic integration and creating new opportunities for trade and investment across the continent. These initiatives aim to unlock Africa's vast potential and promote sustainable development for the benefit of all African nations and their people. In conclusion, the African continent is a mosaic of nations that captivate with their diverse cultures, breathtaking landscapes, and a shared commitment to progress. From the ancient wonders of Egypt to the vibrant democracies of South Africa and Ghana, African nations are forging their paths towards a prosperous future. As the world focuses its attention on Africa's growth story, it is crucial to recognize and celebrate the achievements and potential of each African nation on the continent.

Ecologists are increasingly turning to historical abundance data to understand past changes in animal abundance and more broadly the ecosystems in which animals occur. However, developing reliable ecological or management interpretations from temporal abundance data can be difficult because most population counts are subject to measurement or estimation error.

There is now widespread recognition that counts of animal populations are often subject to detection bias. This recognition has led to the development of a general framework for abundance estimation that explicitly accounts for detection bias and its uncertainty, new methods for estimating detection bias, and calls for ecologists to estimate and account for bias and uncertainty when estimating animal abundance. While these methodological developments are now being increasingly accepted and used, there is a wealth of historical population count data in the literature that were collected before these developments. These historical abundance data may, in their original published form, have inherent unrecognised and therefore unaccounted biases and uncertainties that could confound reliable interpretation. Developing approaches to improve interpretation of historical data may therefore allow a more reliable assessment of extremely valuable long-term abundance data.

This dataset contains details of over 200 historical estimates of Adelie penguin breeding populations across the Australian Antarctic Territory (AAT) that have been published in the scientific literature. The details include attributes of the population count (date and year of count, count value, count object, count precision) and the published estimate of the breeding population derived from those attributes, expressed as the number of breeding pairs. In addition, the dataset contains revised population estimates that have been re-constructed using new estimation methods to account for detection bias as described in the associated publication. All population data used in this study were sourced from existing publications.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The most important key figures about population, households, population growth, births, deaths, migration, marriages, marriage dissolutions and change of nationality of the Dutch population.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1899

Status of the figures: The 2023 figures on stillbirths and perinatal mortality are provisional, the other figures in the table are final.

Changes as of 23 December 2024: Figures with regard to population growth for 2023 and figures of the population on 1 January 2024 have been added. The provisional figures on the number of stillbirths and perinatal mortality for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

Changes as of 15 December 2023: None, this is a new table. This table succeeds the table Population; households and population dynamics; 1899-2019. See section 3. The following changes have been made: - The underlying topic folders regarding 'migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will the new figures be published? The figures for the population development in 2023 and the population on 1 January 2024 will be published in the second quarter of 2024.

Terrestrial mammals are found nearly everywhere on earth. Yet, most taxa are endemic to a single continent; geological, evolutionary, ecological or physiological filters constrain geographic distributions. Here, we synthesize data on geography, taxonomy, lineage age, dispersal, body size, and diet for >4,000 terrestrial mammals prior to detectable human-mediated biodiversity losses and quantify factors correlated with the likelihood of dispersal between continents. We confirm the uniqueness of being on multiple continents: excluding humans and commensals, only 260 mammals are found on two continents, while six span three or more continents (the red deer, red fox, brown bear, least weasel, and common bent-wing bat), and just a single species—the lion—once had a geographic range that included four continents. Clearly, the challenges of colonizing and persisting on multiple continents are severe. No single characteristic enables taxa to be on more than one continent. Rather, a suite of ..., Data Collection We used the updated Body Mass of Late Quaternary Mammals dataset (Smith et al. 2003) to version 11.1. See supplemental information of manuscript for deatils. We additionally collected contitnet of family origin ("familyOrigin_Oct2024.csv"). We also added in generic first appearance from the PaleobioDB (see Analysis.R) and Faurby et al. 2018 (PHYLACINE). We also combined data about geographic range, home range, and age of dispersal from Jones et al. 2009 (PanTHERIA), natural ranges from Faurby et al. 2018 (PHYLACINE), as well as generation length from Pacifici et al. 2013. We do not republish existing datasets here. Data cleaning Data for Analysis We removed all species records not on a continent (i.e., insular and marine species). We also removed non-native species, including introduced and domesticated species. This is in "Analysis.R" under "TRIM DATA". Since we do not include previously published data, the script “Analysis.R†includes instructions for finding and ..., , # Data and scripts for Most mammals do not wander: little taxonomic overlap among continents

https://doi.org/10.5061/dryad.1g1jwsv69

Description of the data and file structure

Files and variables

File: familyOrigin_Oct2024.xlsx

Description:Â A dataset of mammalian families and the continent of first fossil occurrence to as of Oct. 2024. Sheet 1 has the data; Sheet 2 "origin of place reference" contains the references used to determine continent of origin for the family; Sheet 3 "origin of date reference" contains the references for the first occurrence of that family. Empty cells indicate that no data was available at the time of the dataset's creation.

Variables for Sheet 1

• family:Â Accepted mammalian family based off Wilson & Reeder 2005 or the literature
• continent: Continent where the family originated, which can be Eurasia, Africa, Australia, North America (North.America), or South America (South.America).
• c...,

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The burden of animal disease is widespread globally and is especially severe for developing countries dependent on livestock production. Ethiopia has the largest livestock population in Africa and the second-largest human population on the continent. Ethiopia is one of the fastest-growing economies in Africa; however, much of the population still lives in extreme poverty, and most households depend on agriculture. Animal disease negatively affects domestic livestock production and limits growth potential across the domestic agricultural supply chain. This research investigates the economic effects of livestock disease burden in Ethiopia by employing a computable general equilibrium model in tandem with animal health loss estimates from a compartmental livestock population model. Two scenarios for disease burden are simulated to understand the effects of improved animal health on domestic production, prices, trade, gross domestic product (GDP), and economic welfare in Ethiopia. Results show that improved animal health may increase Ethiopian GDP by up to 3.6%, which improves national welfare by approximately $US 2.5 billion. This research illustrates the economic effects of improved livestock health, which is critical for Ethiopian households and the national economy.

Retiriment Notice: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofmaize harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by the International Food Policy Research Institute in 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing the Spatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of maize as a staple food see the Harvest Choice webpage. The source data for this layer are available here.

Emissions by Country

Quantifying Sources and Emission Levels

By [source]

About this dataset

This dataset provides an in-depth look into the global CO2 emissions at the country-level, allowing for a better understanding of how much each country contributes to the global cumulative human impact on climate. It contains information on total emissions as well as from coal, oil, gas, cement production and flaring, and other sources. The data also provides a breakdown of per capita CO2 emission per country - showing which countries are leading in pollution levels and identifying potential areas where reduction efforts should be concentrated. This dataset is essential for anyone who wants to get informed about their own environmental footprint or conduct research on international development trends

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How to use the dataset

This dataset provides a country-level survey of global fossil CO2 emissions, including total emissions, emissions from coal, oil, gas, cement, flaring and other sources as well as per capita emissions.

For researchers looking to quantify global CO2 emission levels by country over time and understand the sources of these emissions this dataset can be a valuable resource.

The data is organized using the following columns: Country (the name of the country), ISO 3166-1 alpha-3 (the three letter code for the country), Year (the year of survey data), Total (the total amount of CO2 emitted by the country in that year), Coal (amount of CO2 emitted by coal in that year), Oil (amount emitted by oil) , Gas (amount emitted by gas) , Cement( amount emitted by cement) , Flaring(flaring emission levels ) and Other( other forms such as industrial processes ). In addition there is also one extra column Per Capita which provides an insight into how much personal carbon dioxide emission is present in each Country per individual .

To make use of these columns you can aggregate sum up Total column for a specific region or help define how much each source contributes to Total column such as how many percent it accounts for out of 100 or construct dashboard visualizations to explore what sources are responsible for higher level emission across different countries similar clusters or examine whether individual countries Focusing on Flaring — emissions associated with burning off natural gas while drilling—can improve overall Fossil Fuel Carbon Emission profiles better understanding of certain types nuclear power plants etc.

The main purpose behind this dataset was to facilitate government bodies private organizations universities NGO's research agencies alike applying analytical techniques tracking environment changes linked with influence cross regions providing resources needed analyze process monitor developing directed ways managing efficient ways get detailed comprehensive verified information

With insights gleaned from this dataset one can begin identify strategies efforts pollutant mitigation climate change combat etc while making decisions centered around sustainable developments with continent wide unified plans policy implementations keep an eye out evidences regional discrepancies being displayed improving quality life might certainly seem likely assure task easy quickly done “Global Fossil Carbon Dioxide Emissions:Country Level Survey 2002 2022 could exactly what us

Research Ideas

• Using the per capita emissions data, develop a reporting system to track countries' progress in meeting carbon emission targets and give policy recommendations for how countries can reach those targets more quickly.
• Analyze the correlation between different fossil fuel sources and CO2 emissions to understand how best to reduce CO2 emissions at a country-level.
• Create an interactive map showing global CO2 levels over time that allows users to visualize trends by country or region across all fossil fuel sources

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: GCB2022v27_MtCO2_flat.csv | Column name | Description ...

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper Roughly 0.4947 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0029 and 0.3665 (in million kms), corressponding to 0.5881% and 74.094% respectively of the total road length in the dataset region. 0.1252 million km or 25.3179% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0 million km of information (corressponding to 0.0006% of total missing information on road surface) It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications. This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications. AI features: pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved." pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved). osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved." combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved." combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved." n_of_predictions_used: Number of predictions used for the feature length estimation. predicted_length: Predicted length based on the DL model’s estimations, in meters. DL_mean_timestamp: Mean timestamp of the predictions used, for comparison. OSM features may have these attributes(Learn what tags mean here): name: Name of the feature, if available in OSM. name:en: Name of the feature in English, if available in OSM. name:* (in local language): Name of the feature in the local official language, where available. highway: Road classification based on OSM tags (e.g., residential, motorway, footway). surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt). smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad). width: Width of the road, where available. lanes: Number of lanes on the road. oneway: Indicates if the road is one-way (yes or no). bridge: Specifies if the feature is a bridge (yes or no). layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels). source: Source of the data, indicating the origin or authority of specific attributes. Urban classification features may have these attributes: continent: The continent where the data point is located (e.g., Europe, Asia). country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States). urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban) urban_area: Name of the urban area or city where the data point is located. osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature. osm_type: Type of OSM element (e.g., node, way, relation). The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer. This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information. We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.