These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

This data comes from the 2010 Census Profile of General Population and Housing Characteristics. Zip codes are limited to those that fall at least partially within LA city boundaries. The dataset will be updated after the next census in 2020. To view all possible columns and access the data directly, visit http://factfinder.census.gov/faces/affhelp/jsf/pages/metadata.xhtml?lang=en&type=table&id=table.en.DEC_10_SF1_SF1DP1#main_content.

Table contains total population and population density summarized at county, city, zip code, and census tract level. Population density is defined as number of people residing per square mile of area. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B01001; data accessed on April 11, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationpop_density (Numeric): Area in square milesarea (Numeric): Population density

Population totals for groupings commonly used in other datasets.

Not all values are available for all years.

Note that because the "Citywide" rows roll up the values from the individual ZIP Codes and the "Age 0-4," "Age 5-11," "Age 12-17," "Age 5+," "Age 18+," and "Age 65+" columns overlap other age categories, as well as each other in some cases, care should be taken in summing values to avoid accidental double-counting. The "Age 5-11" and "Age 12-17" columns only include children who live in households.

Data Sources: U.S. Census Bureau American Community Survey (ACS) 5-year estimates (ZIP Code) and 1-year estimates (Citywide). The U.S. Census Bureau did not release standard 1-year estimates from the 2020 ACS. In 2020 only, 5-year estimates were used for the Citywide estimates.

A dataset listing North Carolina zip codes by population for 2024.

Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

Census data plays a pivotal role in academic data research, particularly when exploring relationships between different demographic characteristics. The significance of this particular dataset lies in its ability to facilitate the merging of various datasets with basic census information, thereby streamlining the research process and eliminating the need for separate API calls.

The American Community Survey is an ongoing survey conducted by the U.S. Census Bureau, which provides detailed social, economic, and demographic data about the United States population. The ACS collects data continuously throughout the decade, gathering information from a sample of households across the country, covering a wide range of topics

Methodology

The Census Data Application Programming Interface (API) is an API that gives the public access to raw statistical data from various Census Bureau data programs.

We used this API to collect various demographic and socioeconomic variables from both the ACS and the Deccenial survey on different geographical levels:

ZCTAs:

ZIP Code Tabulation Areas (ZCTAs) are generalized areal representations of United States Postal Service (USPS) ZIP Code service areas. The USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes.

Census Tract:

Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity that can be updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program (PSAP).

Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census.

Block Groups:

Block groups (BGs) are the next level above census blocks in the geographic hierarchy (see Figure 2-1 in Chapter 2). A BG is a combination of census blocks that is a subdivision of a census tract or block numbering area (BNA). (A county or its statistically equivalent entity contains either census tracts or BNAs; it can not contain both.) A BG consists of all census blocks whose numbers begin with the same digit in a given census tract or BNA; for example, BG 3 includes all census blocks numbered in the 300s. The BG is the smallest geographic entity for which the decennial census tabulates and publishes sample data.

Census Blocks:

Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show total population and change by Zip Code Tabulation Area in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

# Area, Acres, 2017

SqMi

# Area, square miles, 2017

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

TotPop_e

# Total population, 2017

TotPop_m

# Total population, 2017 (MOE)

rPopDensity

Population density (people per square mile), 2017

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Frontier and Remote Area (FAR) codes provide a statistically-based, nationally-consistent, and adjustable definition of territory in the U.S. characterized by low population density and high geographic remoteness.

To assist in providing policy-relevant information about conditions in sparsely settled, remote areas of the U.S. to public officials, researchers, and the general public, ERS has developed ZIP-code-level frontier and remote (FAR) area codes. The aim is not to provide a single definition. Instead, it is to meet the demand for a delineation that is both geographically detailed and adjustable within reasonable ranges, in order to be usefully applied in diverse research and policy contexts. This initial set, based on urban-rural data from the 2000 decennial census, provides four separate FAR definition levels, ranging from one that is relatively inclusive (18 million FAR residents) to one that is more restrictive (4.8 million FAR residents).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: State and ZIP code level tables For complete information, please visit https://data.gov.

This data represents five-digit ZIP Code areas used by the U.S. Postal Service. This is an ArcGIS Online item directly from Esri. For more information see https://www.arcgis.com/home/item.html?id=8d2012a2016e484dafaac0451f9aea24.

This map shows the population density in the United States in 2012. Population density is calculated by dividing the total population count of geographic feature by the area of the feature, in square miles. The area is calculated from the geometry of the geographic feature in projected coordinates. The best use of this map is at the larger scales (tracts and block groups).The data shown is from Esri's 2012 Updated Demographics. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. This map shows Esri's 2012 estimates using Census 2010 geographies.The map is designed to be displayed in conjunction with the Canvas basemap with a transparency of 25%. To use it on other basemaps, try a transparency of 25-50%.Information about the USA Population Density map service used in this map is here.

Health regions are defined by provincial governments as the areas of responsibility for regional healthboards (i.e., legislated) or as regions of interest to health care authorities. In 1998, Statistics Canada, together with the Canadian Institute for Health Information and the Advisory Council on Health Info-Structure (Health Canada),consulted stakeholders across Canada to identify current and future needs for health information. These consultations identified a need for comprehensive and comparable sub-provincial data. In response to this need, health regions were investigated as an alternative geographic unit for disseminating health information. This report provides an overview of health regions in Canada, along with sourcesand methodologies for developing and understanding the health region data linkage and digital boundary files, geographic attributes, and population estimates. The same health region boundaries contained in Health Regions - 2000 have been used in the sample design for the Canadian Community Health Survey. Future boundary changes may cause adjustments to the survey collection and dissemination process, or sample revisions for future survey cycles. For current Health Regions data, refer to Statistics Canada.

** A Newer Version of this data is available here: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6The United States Postal Service (USPS) does not define ZIP codes as fixed geographic boundaries, such as polygons on a map. Instead, ZIP codes are structured as collections of carrier routes designed to optimize mail delivery. These routes are established based on logistical considerations, such as population density, delivery efficiency, and infrastructure changes, rather than adhering to precise geographic outlines.When ZIP codes are mapped, the resulting visualization is essentially an estimation of these delivery routes. However, these approximations are inherently subject to change, as the Postal Service frequently adjusts routes to accommodate new developments, address shifts in demand, or enhance operational efficiency. Consequently, any representation of ZIP codes on a map should be understood as a general reference and not as an exact or permanent delineation.National ZipCodes: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6

The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census.

The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences.

The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences.

An update of the Rural-Urban Commuting Area Codes is planned for late 2013.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Webpage with links to Excel files For complete information, please visit https://data.gov.

2010 Census Data on population, pop density, age and ethnicity per zip code

Health Regions 2005 describes in detail the health region limits as of June 2005 and their correspondence with the 1996 and 2001 Census geography. Health regions are defined by the provinces and represent administrative areas or regions of interest to health authorities. This product contains correspondence files (linking health regions to 2001 Census geographic codes) and digital boundary files. User documentation provides an overview of health regions, sources, methods, limitations and product description (file format and layout).In addition to the geographic files, this product also includes 2001 Census data (basic profile) for health regions. A result of the co-operation of provincial health ministries, Alberta Treasury and BC Stats, Health Regions 2005 is part of the Health Information Roadmap initiative, a joint effort among the Canadian Institute for Health Information, Health Canada and Statistics Canada. Health Regions 2005 was produced by the Health Statistics Division in collaboration with the Geography and Dissemination divisionsHealth regions are definedby provincial governments as the areas of responsibility for regional healthboards (i.e., legislated) or as regions of interest to health care authorities. This product replaces Health Regions 2000. For current Health Regions data, refer to Statistics Canada.

A global database of Direct Marketing Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future. Leverage up-to-date audience targeting population trends for market research, audience targeting, and sales territory mapping.

Self-hosted marketing population dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Demographic Data is standardized, unified, and ready to use.

Use cases for the Global Consumer Behavior Database (Direct Marketing Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Audience targeting

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Demographic data export methodology

Our population data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Consumer databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

A global database of Real Estate Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date urban planning data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial real estate dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Urban Planning Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Urban Planning Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Demographic data export methodology

Our location data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Real Estate databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from U.S. Census: County Business Patterns to show number and density of business establishments and payroll data, for 2005-2015, by zip code in the Atlanta region.

Attributes:

ZIP = Zip code (text)

ZIP_dbl = Zip code (numeric)

Total_Population_2010 = Total Population, 2010 Census

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

Number_of_establishments_2015 = Number of establishments, 2015

Establishments_perSqMi_2015 = Establishments per Square Mile, 2015

Establishments_per1000_Pop_2015 = Establishments per 1,000 population, 2015 (Population is 2010)

Paid_employees_March_2015 = Paid employees for pay period including March 12 (number), 2015

First_quarter_payroll_000s_2015 = First-quarter payroll (000s), 2015

Annual_payroll_000s_2015 = Annual payroll (000s), 2015

Number_of_establishments_2013 = Number of establishments, 2013

Establishments_perSqMi_2013 = Establishments per Square Mile, 2013

Establishments_per1000_Pop_2013 = Establishments per 1,000 population, 2013 (Population is 2010)

Annual_payroll_000s_2013 = Annual payroll (000s), 2013

First_quarter_payroll_000s_2013 = First-quarter payroll (000s), 2013

Paid_employees_March_2013 = Paid employees for pay period including March 12 (number), 2013

Number_of_establishments_2010 = Number of establishments, 2010

Paid_employees_March_2010 = Paid employees for pay period including March 12 (number), 2010

First_quarter_payroll_000s_2010 = First-quarter payroll (000s), 2010

Annual_payroll_000s_2010 = Annual payroll (000s), 2010

Number_of_establishments_2005 = Number of establishments, 2005

Paid_employees_March_2005 = Paid employees for pay period including March 12 (number), 2005

First_quarter_payroll_000s_2005 = First-quarter payroll (000s), 2005

Annual_payroll_000s_2005 = Annual payroll (000s), 2005

Chng_establishments_2005_2010 = Change in the number of establishments between 2005-2010

Chng_establishments_2005_2013 = Change in the number of establishments between 2005-2013

Chng_establishments_2005_2015 = Change in the number of establishments between 2005-2015

Chng_estabmts_PerSqMi_2005_2010 = Change in the number of establishments, Per Sq Mile, between 2005-2010

Chng_estabmts_PerSqMi_2005_2013 = Change in the number of establishments, Per Sq Mile, between 2005-2013

Chng_estabmts_PerSqMi_2005_2015 = Change in the number of establishments, Per Sq Mile, between 2005-2015

Chng_establishments_2010_2015 = Change in the number of establishments between 2010-2015

Chng_estabmts_PerSqMi_2010_2015 = Change in the number of establishments, Per Sq Mile, between 2010-2015All_establishments_2015 = All establishments, 2015 Very_Small_Businesses_2015 = Very Small businesses (1-4) employees, 2015 Small_Businesses_2015 = Small Businesses (5-19 employees), 2015 Medium_Businesses_2015 = Medium-sized businesses (20-99 employees), 2015 Large_Businesses_2015 = Large Businesses (100+ employees), 2015 Pct_Very_Small_Businesses_2015 = %, Very Small businesses (1-4) employees, 2015 Pct_Small_Businesses_2015 = %, Small Businesses (5-19 employees), 2015 Pct_Medium_Businesses_2015 = %, Medium-sized businesses (20-99 employees), 2015 Pct_Large_Businesses_2015 = %, Large Businesses (100+ employees), 2015 All_establishments_2010 = All establishments, 2010 Very_Small_Businesses_2010 = Very Small businesses (1-4) employees, 2010 Small_Businesses_2010 = Small Businesses (5-19 employees), 2010 Medium_Businesses_2010 = Medium-sized businesses (20-99 employees), 2010 Large_Businesses_2010 = Large Businesses (100+ employees), 2010 Pct_Very_Small_Businesses_2010 = % Very Small businesses (1-4) employees, 2010 Pct_Small_Businesses_2010 = % Small Businesses (5-19 employees), 2010 Pct_Medium_Businesses_2010 = % Medium-sized businesses (20-99 employees), 2010 Pct_Large_Businesses_2010 = % Large Businesses (100+ employees), 2010 All_establishments_2005 = All establishments, 2005 Very_Small_Businesses_2005 = Very Small businesses (1-4) employees, 2005 Small_Businesses_2005 = Small Businesses (5-19 employees), 2005 Medium_Businesses_2005 = Medium-sized businesses (20-99 employees), 2005 Large_Businesses_2005 = Large Businesses (100+ employees), 2005 Pct_Very_Small_Businesses_2005 = %, Very Small businesses (1-4) employees, 2005 Pct_Small_Businesses_2005 = %, Small Businesses (5-19 employees), 2005 Pct_Medium_Businesses_2005 = %, Medium-sized businesses (20-99 employees), 2005

Pct_Large_Businesses_2005 = %, Large Businesses (100+ employees), 2005

last_edited_date = Last date the feature was edited by ARCSource: U.S. Census Bureau, Atlanta Regional Commission

Date: 2005-2015