The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in American Samoa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

This map shows density surfaces derived from the 2010 US Census block points.This data shows % of people who identified themselves as single race and whiteThe block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

Estimated density of people per grid-cell, approximately 1km (0.008333 degrees) resolution. The units are number of people per Km² per pixel, expressed as unit: "ppl/Km²". The mapping approach is Random Forest-based dasymetric redistribution. The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty. Acknowledgements information at https://www.worldpop.org/acknowledgements

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps. Population allocated proportionally using 2011 census population counts at enumeration area level.

Year Population Growth Rate of 0.23% has been applied to update population up to 2020

Data from the Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Estimates for rural population were obtained by excluding the Urban Population Areas. This was achieved by removing settled and partly settled grid cells from the Landcover Dataset and removing(limiting) propulation density figures to produce a net result which approximates the known rural population. Data-set has been exported as Binary format.

These maps represent a modeled distribution of population based on the nominal censuses of the town of Curitiba and adjacent towns. Classification of data is normalized for each category to allow comparison between different periods of time.

Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps. Population allocated proportionally using 2017 census population counts at commune level. Population data source. Commune boundaries layers source

Year Population Growth Rate of 0.37 % has been applied to update population up to 2020 The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Abundance measures are almost non-existent for several bird species threatened with extinction, particularly range-restricted Neotropical taxa, for which estimating population sizes can be challenging. Here we use data collected over nine years to explore the abundance of 11 endemic birds from the Sierra Nevada de Santa Marta (SNSM), one of Earth’s most irreplaceable ecosystems. We established 99 transects in the “Cuchilla de San Lorenzo†Important Bird Area within native forest, early successional vegetation, and areas of transformed vegetation by human activities. A total of 763 bird counts were carried out covering the entire elevation range in the study area (~175–2650 m). We applied hierarchical distance-sampling models to assess elevation- and habitat-related variation in local abundance and obtain values of population density and total and effective population size. Most species were more abundant in the montane elevational range (1800–2650 m). Habitat-related differences in abun..., , , # Data from: Abundance models of endemic birds of the Sierra Nevada de Santa Marta, northern South America, suggest small population sizes and dependence on montane elevations

MS Reference Number: ORNITH-APP-23-061R2 Dataset name: Abundance_models_priority_endemics_SNSM.xlsx

The whole dataset contains data for fitting hierarchical distance-sampling models for priority, endemic bird species from the Sierra Nevada de Santa Marta, northern Colombia. Models were used to assess elevation- and habitat-related variation in local abundance and obtain values of population density and total and effective population size for the study species. Details on other methods used for estimating extent of presence (EOP) and area of occupancy (AOO), and for generating abundance maps are provided in the manuscript and the supplementary material file that accompanies it. Abundance maps will be uploaded as distribution hypothesis for each species to the BioModelos online platform (Velásquez-Tibatá et al. ...

This dataset shows all 515 departments in Argentina, which correspond to second-level administrative divisions currently used in said country.

The Excel file includes filters for each column.

Column Description

• Department: Name of the department
• Capital: Capital city of the department
• Province: Province the department belongs to
• Map: Map of the department within the province it belongs to
• Population (2022): Population of the department as of 2022
• Area (squared km): Total land area of the department
• Population density (people per sq. km): Population per square kilometer

NOTES - Within the province of Buenos Aires, departments are not referred to as such, but as "partidos". - There are 135 partidos in the province of Buenos Aires, the other 380 second-level administrative divisions correspond to "departamentos" (departments) spread throughout the rest of the country. - The city of Buenos Aires is classified as "ciudad autónoma" (autonomous city), meaning that it is a separate department in itself.

This dataset shows all 336 municipalities in Venezuela, which correspond to second-level administrative divisions currently used in said country.

The Excel file includes filters for each column.

Column Description

• Municipality: Name of the municipality
• Capital: Capital city of the municipality
• State: State the municipality belongs to
• Map: Map of the municipality within the state it belongs to
• Population (2023): Population of the municipality as of 2023
• Area (squared km): Total land area of the municipality
• Population density (people per sq. km): Population per square kilometer
• Region: Administrative region the municipality belongs to

NOTE: Population numbers are estimates and may not reflect reality with full precision.

These maps represent a modeled distribution of population based on the nominal censuses of the town of Curitiba and adjacent towns, providing a snapshot of the demographic situation in a specific year. Classification of data is normalized for each year to allow comparison between different categories of data for the same year.

As of 2025, Asia was the most densely populated region of the world, with nearly 156 inhabitants per square kilometer, whereas Oceania's population density was just over five inhabitants per square kilometer.

"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

Race is a social and historical construct, and the racial categories counted by the census change over time so the process of constructing stable racial categories for these 50 years out of census data required complex and imperfect decisions. Here we have used historical research on early 20th century southern California to construct historic racial categories from the IPUMS full count data, which allows us to track groups that were not formally classified as racial groups in some census decades like Mexican, but which were important racial categories in southern California. Detailed explanation of how we constructed these categories and the rationale we used for the decisions we made can be found here. Layers are symbolized to show the percentage of each of the following groups from 1900-1940:AmericanIndian Not-Hispanic, AmericanIndian Hispanic, Black non-Hispanic, Black-Hispanic, Chinese, Korean, Filipino and Japanese, Mexican, Hispanic Not-Mexican, white non-Hispanic. The IPUMS Census data is messy and includes some errors and undercounts, making it hard to map some smaller populations, like Asian Indians (in census called Hindu in 1920) and creating a possible undercount of Native American populations. The race data mapped here also includes categories that may not have been socially meaningful at the time like Black-Hispanic, which generally would represent people from Mexico who the census enumerator classified as Black because of their dark skin, but who were likely simply part of Mexican communities at the time. We have included maps of the Hispanic not-Mexican category which shows very small numbers of non-Mexican Hispanic population, and American Indian Hispanic, which often captures people who would have been listed as Indian in the census, probably because of skin color, but had ancestry from Mexico (or another Hispanic country). This category may include some indigenous Californians who married into or assimilated into Mexican American communities in the early 20th century. If you are interested in mapping some of the other racial or ethnic groups in the early 20th century, you can explore and map the full range of variables we have created in the People's History of the IE IE_ED1900-1940 Race Hispanic Marriage and Age Feature layer.Suggested Citation: Tilton, Jennifer. People's History Race Ethnicity Dot Density Map 1900-1940. A People's History of the Inland Empire Census Project 1900-1940 using IPUMS Ancestry Full Count Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2023. 2025Feature Layer CitationTilton, Jennifer, Tessa VanRy & Lisa Benvenuti. Race and Demographic Data 1900-1940. A People's History of the Inland Empire Census Project 1900-1940 using IPUMS Ancestry Full Count Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2023. Additional contributing authors: Mackenzie Nelson, Will Blach & Andy Garcia Funding provided by: People’s History of the IE: Storyscapes of Race, Place, and Queer Space in Southern California with funding from NEH-SSRC Grant 2022-2023 & California State Parks grant to Relevancy & History. Source for Census Data 1900- 1940 Ruggles, Steven, Catherine A. Fitch, Ronald Goeken, J. David Hacker, Matt A. Nelson, Evan Roberts, Megan Schouweiler, and Matthew Sobek. IPUMS Ancestry Full Count Data: Version 3.0 [dataset]. Minneapolis, MN: IPUMS, 2021. Primary Sources for Enumeration District Linework 1900-1940 Steve Morse provided the full list of transcribed EDs for all 5 decades "United States Enumeration District Maps for the Twelfth through the Sixteenth US Censuses, 1900-1940." Images. FamilySearch. https://FamilySearch.org: 9 February 2023. Citing NARA microfilm publication A3378. Washington, D.C.: National Archives and Records Administration, 2003. BLM PLSS Map Additional Historical Sources consulted include: San Bernardino City Annexation GIS Map Redlands City Charter Proposed with Ward boundaries (Not passed) 1902. Courtesy of Redlands City Clerk. Redlands Election Code Precincts 1908, City Ordinances of the City of Redlands, p. 19-22. Courtesy of Redlands City Clerk Riverside City Charter 1907 (for 1910 linework) courtesy of Riverside City Clerk. 1900-1940 Raw Census files for specific EDs, to confirm boundaries when needed, accessed through Family Search. If you have additional questions or comments, please contact jennifer_tilton@redlands.edu.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Polymer-Coated MAP Market Outlook

According to our latest research, the global Polymer-Coated MAP market size reached USD 1.24 billion in 2024, supported by a robust demand from the agriculture and horticulture sectors. The market is expected to grow at a CAGR of 6.7% during the forecast period, with the market size forecasted to reach USD 2.14 billion by 2033. This growth is primarily driven by the increasing adoption of controlled-release fertilizers, rising global food demand, and a shift toward sustainable agricultural practices.

One of the primary growth factors for the Polymer-Coated MAP market is the increasing need for efficient nutrient management in agriculture. Farmers and agribusinesses are continually seeking ways to improve crop yields while minimizing environmental impact. Polymer-coated monoammonium phosphate (MAP) fertilizers offer a controlled release of nutrients, reducing nutrient losses due to leaching and volatilization. This controlled-release characteristic not only enhances nutrient uptake by plants but also supports sustainable farming practices by lowering the frequency of fertilizer applications and minimizing runoff pollution. As governments and regulatory bodies worldwide tighten restrictions on conventional fertilizer use to address environmental concerns, the demand for advanced, eco-friendly solutions like polymer-coated MAP is expected to surge.

Another significant driver for the Polymer-Coated MAP market is the growing awareness and adoption of precision agriculture techniques. Precision agriculture leverages data-driven approaches to optimize field-level management regarding crop farming. The use of polymer-coated MAP aligns perfectly with precision agriculture, as it allows for more precise control over nutrient delivery, matching the nutrient supply with crop demand throughout the growing season. This not only maximizes fertilizer efficiency but also reduces input costs for farmers. The increasing penetration of smart farming technologies and the integration of Internet of Things (IoT) devices in agriculture are further propelling the demand for advanced fertilizers, including polymer-coated MAP products.

Furthermore, the expansion of the horticulture and gardening sectors is contributing to the growth of the Polymer-Coated MAP market. Urbanization and the rising popularity of home gardening, landscaping, and ornamental horticulture have created new opportunities for specialty fertilizers. Polymer-coated MAP products are increasingly being used in these segments due to their ability to provide a steady supply of nutrients, supporting healthy plant growth and vibrant blooms. Additionally, the trend toward organic and sustainable gardening practices is encouraging the adoption of slow-release fertilizers, which are perceived as safer for the environment and less likely to cause nutrient imbalances or toxicity in plants.

From a regional perspective, Asia Pacific remains the dominant market for polymer-coated MAP, accounting for the largest share in 2024. This is attributed to the region's vast agricultural base, high population density, and increasing investments in modern farming technologies. North America and Europe also represent significant markets, driven by stringent environmental regulations and the widespread adoption of sustainable agricultural practices. Meanwhile, Latin America and the Middle East & Africa are emerging as high-growth regions, supported by government initiatives to boost agricultural productivity and the adoption of innovative agronomic solutions.

Product Type Analysis

The Product Type segment of the Polymer-Coated MAP market is primarily divided into Monoammonium Phosphate (MAP), Diammonium Phosphate (DAP), and Others. Monoammonium phosphate stands out as the leading product type due to its high solubility, balanced nutrient content, and compatibility with a wide range of crops. Its polymer-coated variant is especially valued for its ability to provide a steady

National level distribution of each of the three housing construction variables in each survey.

Location: North American boreal forestsSize: 4367 plotsType: ABMI, NFI, PSP plotsProv: province, territory or state where the plot isObs_Density: tree density from field measurement (trees/ha; DBH >= 10.0 cm).Est_Crowther_Density: tree density estimates from Crowther et al.'s biome map (trees/ha).HumanDev: percent of developed and managed landHumanPop: population density (km-2)Landcover: land cover type defined in the Land Cover Classification System (LCCS)Long: longitude (° W)Lat: latitude (° N)Elev: elevation (m)Slope: angle of inclination of the terrain (°)Eastness: sine of aspect of the terrain to northNorthness: cosine of aspect of the terrain to northTRI: terrain ruggedness indexStandht: stand height as mean height of the tallest 3 trees (m)Standht2: square of Standht (m2)Canopyht: canopy height from Sentinel-2 imagery (m)LAI: leaf area indexEVI: enhanced vegetation indexNDVI: normalized difference vegetation indexASM_EVI: angular second moment (orderliness) of EVIHomogeneity_EVI: similarity of EVI between adjacent pixelsContrast_EVI: exponentially weighted difference in EVI between adjacent pixelsDissimilarity_EVI: difference in EVI between adjacent pixelsTCECC: cation exchange capacity of the clay fraction in the topsoil (soil between 0 and 30 cm under the ground) (cmol kg-1)TpHH2O: topsoil pH (in water)TC: topsoil carbon content (kg C m-2)AET: actual evapotranspiration as water use (mm day-1)Alpha: Priestley-Taylor alpha coefficient for soil water deficitnhx: anthropogenic NHx input (g N m-2 year-1)noy: anthropogenic NOy input (g N m-2 year-1)Evapotranspiration: Penman-Monteith reference evapotranspiration (mm day-1)Aridity: ration between mean annual precipitation and mean annual Penman-Monteith reference evapotranspirationSolar_Radiation: incident solar radiation (kJ m-2 day-1)Wind: wind speed at 2 m above the ground (m s-1)BIO1: Mean annual temperature (℃)BIO2: Mean diurnal range (mean of monthly difference between maximum and minimum temperature) (℃)BIO3: Isothermality (BIO2/BIO7 * 100)BIO4: Temperature seasonality (standard deviation of monthly temperature) (℃)BIO5: Maximum temperature of warmest month (℃)BIO6: Minimum temperature of coldest month (℃)BIO7: Temperature annual range (BIO5-BIO6) (℃)BIO8: Mean temperature of wettest quarter (℃)BIO9: Mean temperature of driest quarter (℃)BIO10: Mean temperature of warmest quarter (℃)BIO11: Mean temperature of coldest quarter (℃)BIO12: Annual precipitation (mm)BIO13: Precipitation of wettest month (mm)BIO14: Precipitation: driest month (mm)BIO15: Precipitation: seasonality (coefficient of variation of monthly precipitation; Standard deviation of BIO12/mean of BIO12 * 100)BIO16: Precipitation of wettest quarter (mm)BIO17: Precipitation: driest quarter (mm)BIO18: Precipitation of warmest quarter (mm)BIO19: Precipitation of coldest quarter (mm)