This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for counties and equivalent entities in United States of America. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

The statistic shows the largest countries in South America, based on land area. Brazil is the largest country by far, with a total area of over 8.5 million square kilometers, followed by Argentina, with almost 2.8 million square kilometers.

The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

This web map represents geographic entities, defined by the United States Office of Management and Budget for use by Federal statistical agencies, based on the concept of a core area with a large population nucleus, plus adjacent communities having a high degree of economic and social integration with that core.A Core-Based Statistical Area consists of a county containing an Incorporated Place or Census Designated Place with a population of at least 10,000 along with any adjacent counties that have at least 25 percent of employed residents of the county who work in the CBSA's core or central county. CBSAs are categorized as being either Metropolitan or Micropolitan. Each Metropolitan Statistical Area must have at least one urbanized area of 50,000 or more inhabitants. Each Micropolitan Statistical Area must have at least one urban cluster of at least 10,000 but less than 50,000 population.The largest scale the layer is suitable for display is 1:100,000.

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

A dataset listing California counties by population for 2024.

This multi-scale map shows counts of the total population the US. Data is from U.S. Census Bureau's 2020 PL 94-171 data for county, tract, block group, and block.County and metro area highlights:The largest county in the United States in 2020 remains Los Angeles County with over 10 million people.The largest city (incorporated place) in the United States in 2020 remains New York with 8.8 million people.312 of the 384 U.S. metro areas gained population between 2010 and 2020.The fastest-growing U.S. metro area between the 2010 Census and 2020 Census was The Villages, FL, which grew 39% from about 93,000 people to about 130,000 people.72 U.S. metro areas lost population from the 2010 Census to the 2020 Census. The U.S. metro areas with the largest percentage declines were Pine Bluff, AR, and Danville, IL, at -12.5 percent and -9.1 percent, respectively.View more 2020 Census statistics highlights on local populations changes.

This graph depicts the size of county-owned city parks in the U.S. in 2010. The Bear Creek Pioneers Park in Houston has an area of 2,168 acres.

Based on land area, Brazil is the largest country in Latin America by far, with a total area of over 8.5 million square kilometers. Argentina follows with almost 2.8 million square kilometers. Cuba, whose surface area extends over almost 111,000 square kilometers, is the Caribbean country with the largest territory.

Brazil: a country with a lot to offer

Brazil's borders reach nearly half of the South American subcontinent, making it the fifth-largest country in the world and the third-largest country in the Western Hemisphere. Along with its landmass, Brazil also boasts the largest population and economy in the region. Although Brasília is the capital, the most significant portion of the country's population is concentrated along its coastline in the cities of São Paulo and Rio de Janeiro.

South America: a region of extreme geographic variation

With the Andes mountain range in the West, the Amazon Rainforest in the East, the Equator in the North, and Cape Horn as the Southern-most continental tip, South America has some of the most diverse climatic and ecological terrains in the world. At its core, its biodiversity can largely be attributed to the Amazon, the world's largest tropical rainforest, and the Amazon river, the world's largest river. However, with this incredible wealth of ecology also comes great responsibility. In the past decade, roughly 80,000 square kilometers of the Brazilian Amazon were destroyed. And, as of late 2019, there were at least 1,000 threatened species in Brazil alone.

A dataset listing Florida counties by population for 2024.

A dataset listing Georgia counties by population for 2024.

The statistic shows the largest countries in Central America, based on land area. Nicaragua is the largest country in the subregion, with a total area of over 130 thousand square kilometers, followed by Honduras, with more than 112 thousand square kilometers.

A dataset listing Alabama counties by population for 2024.

About this dataset

Context

The dataset presents median household incomes for various household sizes in San Bernardino County, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in San Bernardino County. Across the different household sizes in San Bernardino County the mean income is $86,959, and the standard deviation is $25,311. The coefficient of variation (CV) is 29.11%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $35,348. It then further increased to $109,134 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/san-bernardino-county-ca-median-household-income-by-household-size.jpeg" alt="San Bernardino County, CA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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/.

Recommended for further research

This dataset is a part of the main dataset for San Bernardino County median household income. You can refer the same here

A dataset listing Ohio counties by population for 2024.

One the most obvious difficulties in comparing the influence of landscape on crop production across studies is the choice of landscape metric. There exist countless metrics of landscape composition—the categories of land cover found on a landscape—and landscape configuration—the spatial organization of these categories. Common landscape composition metrics include measures of diversity—such as the Shannon Diversity Index or the Simpson Diversity Index—and measures of land cover composition—such as the percent of the landscape classified as natural cover. Common landscape configuration metrics include measures of patch size (contiguous areas of the same land cover) and mixing as well as edge length (linear length of patch boundaries/perimeter) and fragmentation. Even just considering diversity metrics, numerous options to select from can be found in the literature. Each one of these metrics has its own particularities in terms of sensitivity to scale, rare categories, and boundaries that can significantly alter the conclusions of studies examining the relationship between landscape characteristics and crop production. To address this challenge, we assess the sensitivity of our model results to a number of indicators of landscape composition and configuration using the USDA NASS Cropland Data Layer (CDL) as our indicator of land cover. This dataset classifies land cover at a 30-meter resolution nationwide from 2008 to present using satellite imagery and extensive ground truth data. While the 30-meter spatial resolution of this land cover data cannot accurately represent very small or narrow patches of land cover including shelterbelts and wildflower strips, given its relatively high resolution, full coverage, and historical availability, it is the best data for understanding land cover across agricultural landscapes in the U.S. We extract landscape indices from the CDL data using the landscapemetrics package in R, which considers all land cover in each county’s bounding box with the exception of open water and null categories. We measure compositional complexity using a set of six common landscape metrics associated with the number or the predominance of land cover categories across a landscape. Five of these metrics—Shannon Diversity Index, Simpson Diversity Index, Richness, Shannon Evenness Index, and Simpson Evenness Index—can be considered measures of land cover diversity. The sixth metric–Percent Natural Cover–is a simple measure of the predominance of undeveloped and uncultivated land cover classes (such as wetlands, grasslands, and forests) on a landscape. All of the compositional complexity metrics are aspatial, in that their calculation is not contingent on how land cover categories are arranged within the landscape. Configurational complexity is measured using four landscape metrics associated with the size of land cover patches (continuous areas of a single land cover category), shape of land cover patches, or mixing of land cover categories across the landscape. The metrics Mean Patch Area and Largest Patch Index are most strongly associated with patch size, the Contagion metric is a measure of land cover category mixing and strongly related to patch size, and the Edge Density metric is related to patch size and shape. Unlike the landscape composition metrics, the four landscape configuration metrics are spatially explicit and depend on the arrangement of land cover categories across the landscape. All code used to build data can be found here: https://github.com/katesnelson/aglandscapes-what-or-how Resources in this dataset:

Resource Title: County-level Estimates of Landscape Complexity and Configuration in the Coterminous US File Name: landscape_panel.txt Resource Description: GEOID: State and county FIPS codes in format SSCCC YEAR: Year in which CDL data was collected VALUE: Index value INDEX_NAME: Indices with _AG were computed for the subset of agricultural lands in a county. Indices with _ALL were computed for the entire landscape (agricultural and nonagricultural lands) in a county. LSM_AREA_MN_AG/ALL: Mean patch area, a measure of patch structure. Approaches 0 if all patches are small. Increases, without limit, as the patch areas increase. Higher values generally indicate lower complexity. LSM_CONTAG_AG/ALL: Contagion, a measure of dispersion and interspersion of land cover classes where a high proportion of like adjacencies and an uneven distribution of pairwise adjacencies produces a high contagion value. Range of 0 to 100. Higher values generally indicate lower complexity. LSM_ED_AG/ALL: Edge density, a measure of the patchiness of the landscape. Equals 0 if only one land cover is present and increased without limit as more land cover patches are added. Higher values generally indicate higher complexity. LSM_LPI_AG/ALL: Largest patch index, a measure of patch dominance representing the percentage of the landscape covered by the single largest patch. Approaches 0 when the largest patch is becoming small and equals 100 when only one patch is present. Higher values generally indicate lower complexity. LSM_RICH_AG/ALL: Richness, a measure of the abundance of categories. Higher values generally indicate higher complexity. LSM_SHDI_AG/ALL: Shannon Diversity Index, a measure of the abundance and evenness of land cover categories. This index is sensitive to rare land cover categories. Typical values are between 1.5 and 3. Higher values indicate higher complexity. LSM_SHEI_ALL: Simpson Evenness Index, a measure of diversity or dominance calculated as the ratio between the Shannon Diversity Index and the theoretical maximum of the Shannon Diversity Index. Shannon Evenness Index = 0 when there is only one land cover on the landscape and equals 1 when all land cover classes are equally distributed. Higher values generally indicate higher complexity. LSM_SIDI_ALL: Simpson Diversity Index, a diversity measure that considers the abundance and evenness of land cover categories. This index is not sensitive to rare land cover categories. Values range from 0 to 1. Higher values generally indicate higher complexity MODE_AG : Most dominant agricultural land use type found in the data (mode of agricultural CDL categories) MODE_ALL : Most dominant land use type found in the data (mode of all land use categories) PNC : Percent natural cover

Resource Title: Technical Validation File Name: technical_validation.txt

A dataset listing Virginia counties by population for 2024.

This statistics shows the top 20 fastest growing large-metropolitan areas in the United States between July 1st, 2022 and July 1st, 2023. The total population in the Wilmington, North Carolina, metropolitan area increased by 0.05 percent from 2022 to 2023.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth