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 American Community Survey (ACS) 5 Year 2016-2020 socioeconomic estimate data is a subset of information derived from the following census tables:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By Tenure;B17021 - Poverty Status Of Individuals In The Past 12 Months By Living Arrangement;B19001 - Household Income In The Past 12 Months;B19013 - Median Household Income In The Past 12 Months;B19025 - Aggregate Household Income In The Past 12 Months;B19113 - Median Family Income In The Past 12 Months;B19202 - Median Non-family Household Income In The Past 12 Months;B23001 - Sex By Age By Employment Status For The Population 16 Years And Over;B25014 - Tenure By Occupants Per Room;B25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into Unit;B25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 Months;C24010 - Sex By Occupation For The Civilian Employed Population 16 Years And Over;B20004 - Median Earnings In the Past 12 Months (In 2015 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and Over;B23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, and;B24021 - Occupation By Median Earnings In The Past 12 Months (In 2015 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Socioeconomic Estimate Data by CountyDate of Coverage: 2016-2020

Description Population Projections by County reports the projected population of Connecticut, disaggregated by county, gender and age cohort, in 5-year intervals.

This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Estimates should be interpreted with caution. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

DPH Annual Population Estimates by County reports population by County, as estimated by the Connecticut Department of Public Health.

Per Capita Income by County reports the 5-year estimated mean income for every individual, disaggregated by Race or Ethnicity.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census, the MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The boundaries of most legal MCDs are as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CCDs are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

The 2020 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. In MCD states where no MCD exists or no MCD is defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The generalized boundaries of legal MCDs are based on those as of January 1, 2020 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CCDs, delineated in 21 states, are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

A broad and generalized selection of 2014-2018 US Census Bureau 2018 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico counties). The selection, while not comprehensive, provides a first-level characterization of the race and/or ethnicity of populations in New Mexico, along with citizenship status and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users. The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. As in the decennial census, strict confidentiality laws protect all information that could be used to identify individuals or households.The ACS combines population or other data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. 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. While each full Data Profile contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by New Mexico county boundaries, based on TIGER/Line Files: shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database.

This layer contains data on the number of establishments, total employment, and total annual payroll for for 20 selected 4- and 5-digit North American Industry Classification System (NAICS) codes. This is shown by county and state boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. This layer is symbolized to show the total number of establishments depicted by size, and the average annual pay per employee, depicted by color.

Current Vintage: 2017

CBP Table: CB1700CBP

Data downloaded from: Census Bureau's API for County Business Patterns

Date of API call: June 1, 2019

The United States Census Bureau's County Business Patterns Program (CBP):

About this Program Data Technical Documentation News & Updates

This 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 Bureau and CBP when using this data.

Data Processing Notes: Boundaries come from the US Census Bureau TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census Bureau. These are Census Bureau boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. 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 56 records - all US states, Washington D.C., Puerto Rico, and U.S. Island Areas Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.

This dataset contains estimates of proportional area of 18 major crops for each county in the United States at roughly decadal time steps between 1840 and 2017, and was used for analyses of historical changes in crop area, diversity, and distribution published in:Crossley, MS, KD Burke, SD Schoville, VC Radeloff. (2020). Recent collapse of crop belts and declining diversity of US agriculture since 1840. Global Change Biology (in press).The original data used to curate this dataset was derived by Haines et al. (ICPSR 35206) from USDA Agricultural Census archives (https://www.nass.usda.gov/AgCensus/). This dataset builds upon previous work in that crop values are georeferenced and rectified to match 2012 county boundaries, and several inconsistencies in the tabular-formatted data have been smoothed-over. In particular, smoothing included conversion of values of production (e.g. bushels, lbs, typical of 1840-1880 censuses) into values of area (using USDA NASS yield data), imputation of missing values for certain crop x county x year combinations, and correcting values for counties whose crop totals exceeded the possible land area.Please contact the PI, Mike Crossley, with any questions or requests: mcrossley3@gmail.com

For all counties or county equivalents, this file provides provisional population estimates for July 1, 1985 as well as revised population estimates for July 1 of 1981, 1982, 1983, and 1984. Also included are data on net migration and the number of births and deaths from 1980 to 1985.

Of the total population in Sweden of 10.55 million people, around half resided in the counties Stockholm, Västra Götaland or Skåne. This is also the three counties where the three largest cities in Sweden, Stockholm, Göteborg, and Malmö, are located. In the capital region Stockholm county, there lived nearly 2.5 million inhabitants in 2023. Västra Götaland county had close to 1.8 million inhabitants, while Skåne county, the southernmost region, had roughly 1.4 million inhabitants. The island Gotland had the lowest number of inhabitants with only 60,000.

The highest population density

Stockholm, Skåne and Västra Götaland were also the three counties in Sweden with the highest population density. In 2022, 374.6 inhabitants per square kilometer lived in Stockholm county, while the corresponding figures for Skåne and Västra Götaland were 129 and 73.9, respectively.

The highest rents

Unsurprisingly. Stockholm county is the county in Sweden with the highest rents for rented dwellings, with average prices for one square meter amounting to over 1,400 Swedish kronor in 2022. The lowest average renting prices were in the northwestern region Jämtland, one square meter costing 1,000 Swedish kronor.

The table Population by County is part of the dataset Example Workflow Dataset, available at https://redivis.com/datasets/fgd4-ffrp1ze26. It contains 838 rows across 4 variables.

Provide statistics on the latest changes in importers by county and city (including new establishment, cancellation, and restoration; but changes in registration are not included in the statistics, so columns AB-CD may not necessarily equal E).

All of Taiwan's kindergarten profile data is classified by school locations in counties and cities.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data, and they include legally minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census, the legal MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas is covered by county subdivisions. The boundaries of all 2010 Census legal MCDs are as of January 1, 2010 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). For the 2010 Census, CCDs or their equivalents are delineated in 21 States. The boundaries of all 2010 Census statistical CCDs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP).

The Census Bureau's Population Estimates Program produces July 1 estimates for years after the last published decennial census, as well as for past decades. This dataset includes population estimates by county in Texas and New Mexico for the years 1980-2019.

This is a polygon dataset for county boundaries as well as for city, township and unorganized territory (CTU) boundaries in the Twin Cities 7-county metropolitan area. The linework for this dataset comes from individual counties and is assembled by the Metropolitan Council for the MetroGIS community. This is a MetroGIS Regionally Endorsed dataset https://metrogis.org/.

The County CTU Lookup Table here https://gisdata.mn.gov/dataset/us-mn-state-metc-bdry-counties-and-ctus-lookup
is also included in this dataset and contains various data related to cities, townships, unorganized territories (CTUs) and any divisions created by county boundaries splitting them is also included in the dataset.

This dataset is updated quarterly. This dataset is composed of three shape files and one dbf table.
- Counties.shp = county boundaries
- CTUs.shp = city, township and unorganized territory boundaries
- CountiesAndCTUs.shp = combined county and CTU boundaries
- CountyCTULookupTable.dbf = various data related to CTUs and any divisions created by county boundaries splitting them is also included in the dataset, described here: https://gisdata.mn.gov/dataset/us-mn-state-metc-bdry-counties-and-ctus-lookup

NOTES:

- On 3/17/2011 it was discovered that the CTU ID used for the City of Lake St. Croix Beach was incorrect. It was changed from 2394379 to 2395599 to match GNIS.

- On 3/17/2011 it was discovered that the CTU ID used for the City of Lilydale was incorrect. It was changed from 2394457 to 2395708 to match GNIS.

- On 11/9/2010 it was discovered that the CTU ID used for the City of Crystal was incorrect. It was changed from 2393541 to 2393683 to match GNIS.

- Effective April 2008, a change was made in GNIS to match the FIPS place codes to the "civil" feature for each city instead of the "populated place" feature. Both cities and townships are now "civil" features within GNIS. This means that the official GNIS unique ID for every city in Minnesota has changed.

- The five digit CTU codes in this dataset are identical to the Federal Information Processing Standard (FIPS) ''Place'' codes. They are also used by the Census Bureau and many other organizations and are proposed as a MN state data coding standard.

- Cities and townships have also been referred to as ''MCDs'' (a census term), however this term technically refers to the part of each city or township within a single county. Thus, a few cities in the metro area that are split by county boundaries are actually comprised of two different MCDs. This was part of the impetus for a proposed MN state data standard that uses the ''CTU'' terminology for clarity.

- The boundary line data for this dataset comes from each county.

- A variety of civil divisions of the land exist within the United States. In Minnesota, only three types exist - cities, townships and unorganized territories. All three of these exist within the Twin Cities seven county area. The only unorganized territory is Fort Snelling (a large portion of which is occupied by the MSP International Airport).

- Some cities are split between two counties. Only those parts of cities within the 7-county area are included.

- Prior to the 2000 census, the FIPS Place code for the City of Greenwood in Hennepin County was changed from 25928 to 25918. This dataset reflects that change.

About this dataset

Context

The dataset tabulates the Philadelphia County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Philadelphia County. The dataset can be utilized to understand the population distribution of Philadelphia County by age. For example, using this dataset, we can identify the largest age group in Philadelphia County.

Key observations

The largest age group in Philadelphia County, PA was for the group of age 30 to 34 years years with a population of 151,121 (9.55%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Philadelphia County, PA was the 80 to 84 years years with a population of 25,123 (1.59%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Philadelphia County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Philadelphia County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Philadelphia County Population by Age. You can refer the same here