Between 2017 and 2020, the most common last name registered in Mexico was Hernandez with over half a million cases, followed by Garcia and Martinez. Sofia was the most popular female name in Mexico in 2021, while Santiago was the most popular name for male newborns.

The Census Bureau's Census surnames product is a data release based on names recorded in the decennial census. The product contains rank and frequency data on surnames reported 100 or more times in the decennial census, along with Hispanic origin and race category percentages. The latter are suppressed where necessary for confidentiality. The data focus on summarized aggregates of counts and characteristics associated with surnames, and the data do not in any way identify any specific individuals.

In the rich tapestry of French culture, surnames play a fundamental role, not only as family identifiers, but also as reflections of the country's history and traditions. Known for its diversity and heritage, France is home to a wide variety of French surnames that have been shaped over the centuries by historical, regional and social influences. In this article, we will explore the most common surnames in France, highlighting their meaning and origin, as well as how these names have left an indelible mark on contemporary French identity. . Whether you are interested in genealogy or simply curious about the cultural aspects of this nation, discovering the most common surnames will offer you a new perspective on the fascinating mosaic of France modern.

In the heart of Eastern Europe, Romania stands out not only for its rich culture and traditions, but also for the diversity of its Romanian surnames. Throughout history, Romanians have forged a unique identity that is reflected in the most common surnames in Romania, which contain stories of families, regions and cultural influences . This article seeks to explore the most common Romanian surnames, offering a look at how these names contribute to the heritage and legacy of Romanian society. Join us on this tour of the country's name day and discover the richness contained in these family symbols.

Maria was the most popular first name for girls registered in Portugal in 2024, with almost 4,300 registrations. Following from afar was the name Alice, with 992 newborn baby girls under this name. In third place, Benedita was chosen 973 times by parents to name their newborn girls. The names for baby boys in Portugal were dominated, in 2024, by the name Francisco, which was registered 1,270 times. Lourenço, Vicente, and Tomás followed, with around 1,030 registrations each. Women pursue family achievements at an increasingly older age   In Portugal, the mean age of women on their first marriage has increased constantly during the last decade. As of 2023, women were 34.3 years old at the time they got married for the first time, which is an increase of half-year compared to 2022. The mean age with which women get married is higher than the average age at which they give birth to their first child. Even though this value has also increased during the past decade, it has been decreasing since 2021, and, by 2023, it was 30.2 years of age. Payment inequality to the disadvantage of women   Women working in Portugal face a gender pay gap (GPG) which puts them at a disadvantage when compared to men. From 2010 to 2021, the gender pay gap decreased by five percent in the country, which is a step forward in terms of the fight against gender inequality. Nevertheless, the GPG was still more than 13 percent in 2022, which means that Portuguese women received 13 percent lower salaries than their male counterparts. When it comes to the average monthly earnings by gender, the tendency of higher salaries for men is widespread throughout Europe. On average, in 2022, men in Portugal received around 2,090 U.S. dollars per month, while women were paid 1,900 U.S. dollars monthly.

This layer presents the 2010 Census block boundaries for Arlington County. The geography and attributes were sourced from US Census Bureau 2010 Cartographic Boundary Files.Contact: Department of Environmental ServicesData Accessibility: Publicly AvailableUpdate Frequency: NeverDocument Last Revision Date: 11/3/2023Document Creation Date: 11/3/2023Feature Dataset Name: Census_2010Layer Name: Census_2010_Blocks_poly

Abstract

The 2010 Saint Lucia Population and Housing Census is conducted by the Central Statistical Office staff. The island-nation of Saint Lucia recorded an overall household population increase of 5 percent from May 2001 to May 2010 based on estimates derived from a complete enumeration of the population of Saint Lucia during the conduct of the recently completed 2010 Population and Housing Census. Saint Lucia's total resident population as at midnight on Census Day, the 10th May 2010 stood at 166,526 persons. Saint Lucia's total population including non-resident persons was estimated to be 173,720, the total number of non-resident persons was 7,194. The preliminary count of Saint Lucia's enumerated population was 151,864 persons reflecting a response rate to the census of 92%. The total resident population of St. Lucia is comprised of 82,926 males and 83,600 females. Out of this sum, there were 165,595 individuals residing in private households, 931 persons living in institutions.

A modern population and housing census is the process of collecting, compiling, analyzing, and publishing demographic, socio-economic, and environmental data pertaining to all persons in a country and the national housing stock at a specified time. A census is a form of national stocktaking. Since the census is a complete count of the population and living quarters, it provides detailed benchmark data on the size of the population, age structure, educational attainment, economic activity, disability, housing, and household amenities as well as other major socio-economic characteristics.

Geographic coverage

National Coverage includes all Administrative Districts and Political Constituencies

Analysis unit

• Households,
• Individuals.

Universe

The Census covered all de jure household members (usual residents of St Lucia based on the six month criteria). The fertility of all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household. The Census also collected data on dwelling and housing conditions of all resident householders. In the Census Visitation record all de jure household members were counted by sex, in addition, persons present in St Lucia at the time of the census who were not usual residents were also counted to produce the de facto population of St Lucia on census day May 10, 2010.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were bound together into booklets. Each booklet contained a cover page (for identification and the Record of Visits), page 2 for Listing the names of the members of the Household and for any comments needed concerning any member of the household or any part of the enumeration. NATIONAL ARCHIVES, INTERNATIONAL MIGRATION and HOUSING spread over pages 3 to 5.

After these sections, three individual questionnaires (6 pages each) complete the booklet. These booklets provide for three (3) persons and are to be used for households consisting of three (3) or fewer persons. If the household comprises more than three persons, the main booklet plus the number of additional person questionnaires were required. For example,

For a 1, 2, 3-person household, use one booklet;

For a 4-person household, use one booklet plus one additional person questionnaire.

For a 5-person household, use one booklet plus two additional person questionnaires and so on.

The ED Number and the Household number contained on the front cover page of the main questionnaire was transferred to the top of the front page of EVERY person questionnaire whether or not it was an individual questionnaire within the main booklet or whether it was an individual questionnaire applicable to a household with more than three persons.

STRUCTURE OF THE INDIVIDUAL QUESTIONNAIRE

The individual questionnaire starts at Section 3. The questions are divided into eleven groups, each having a central theme and given a section number as follows:

Section 3: Personal Characteristics (for all persons) Section 4: Birthplace & Residence (for all persons) Section 5: Disability (for all persons) Section 6: Health (for all persons) Section 7: Education and Internet Access (for all persons) Section 8: Professional, Technical & Vocational Training (for persons 15 years and over) Section 9: Economic Activity (for persons 15 years and over) Section 10: Income and Livelihood (for females 15 years and over) Section 11: Marital Status and Union Status (for persons 15 years and over) Section 12: Fertility (for persons 15 years and over) Section 13: Where Spent Census Night (for all persons)

Cleaning operations

Data editing took place at a number of stages throughout the processing, including (See External Resource for more information on this item):

a) Field Editing by interviewers and field supervisors The guidelines for the conduct of these edits were laid out in PART IX: Checking Your Questionnaires for Errors in the Enumerators Manual. These instructions are repeated in the supervisors manual and also stated in the contract for payment of enumerators and supervisors. A number of elements of the edits outlined formed the basis for the payment of enumerators and supervisors.

b) Office editing and questionnaire re-numbering When a full set of questionnaires from a completed ED was recieved by the office, persons assigned as census evaluators had the responsibility to review the content of each Questionnaire to check for completeness. They were required to perform checks on the questionnaires and the visitation records for the key geographic variables and perform other checks in line with the requirements of a Census Evaluation form which laid out quality standards for the approval of a completed ED for payment. The Census evaluation form is provided as an external resource for information.

c) Data Capture, Editing and Coding during scanning and data verification The data was captured using TELEform V10.4.1 and the data from the forms was exported to a SQL Server 2005 database as was all other census related information captured on forms, such as the census 2010 Evaluation form, referred to previously, the census visitation record etc.

The names of the SQL Server Databases are as follows: 1) Census2010 containing Tables: Census2010Persons, Census2010House, Census2010Visit, Census2010Evaluation, Census2010ApplicationForms, CensusTestScores, Census2010Institutions 2) Census2010_Validated contained data which was validated on several metrics outline in a VBA program built into the TELEform v10.4 software used to capture the data after scanning.

The correction of geographic variables was completed during this process. The scanner operator would manually enter the ED code for the batch being scanned, he would also enter the first and last household for the batch manually. Later the verifier would independantly verify the ED and the household number entered by the enumerator against the values entered by the scanner operator to ensure that they were either the same as in the case of the ED number or within the range of households expected in the batch as in the case of the household number. This was done using VBA validation code written within the TELEform 10.4.1 software used for the scanning and capture of the data from the Census.

Computer Assisted Coding was built into the TELEform template, this method assisted the enumerator using keywords to identify the code for the entry of the appropriate settlement, industry or occupation code. A listing of the codes used is attached to this document as an external resource. Occupation codes are in the international format of ISCO-08 while the industry code applied is based on ISIC Rev4.

d) Structure checking and completeness in Foxpro

The data was exported to MS Access and then on to MS Foxpro where some basic editing was done.

1) This involved the conversion of descriptions of settlement, ISCO and ISIC data collected in fields to codes 2) Standardizing the lenghts and format of all fields in the dataset in preparation for conversion to CSPRO ASCII data format 3) Transposing data on Migration, deaths, disability and births in the last 12 months to variables in the household and person files 4) Removal of blank and very incomplete records 5) Removal of all duplicates and the cleaning of all inconsistent records between the household and the person file. 6) Creation of CSPRO 4.0 compatible format data file for use in further editing and cleaning

e) Detailed variable level editing using CSPRO 4.0 and hotdecking Detailed programs were developed to clean census data on critical variables in the housing section of the questionnaire such as Type of Dwelling, household assets etc, demographic variables such as age, sex, education and economic activiity variables were cleaned in the first version of the CSPRO 4.0 *.bch program file developed. After the first version of the cleaning program was complete the Statistical Office published the Preliminary Census 2010 Report (Updated April 2010). The first version of this publication released in January contained only data on population counts from the census visitation records. The updated April 2010 Preliminary Census report contained information on all the main variables cleaned in the first version of the cleaning program. The CSPRO 4.0 program employed the use of many 3-dimensional hotdecking programs to correct for items not stated or recorded.

f) Checking of data files using the Tabulation Features of CSPRO 4.0 and SPSS 19 Crosstabulations of variables were used to identify inconsistent data and improve CSPRO 4.0 editing programs

Detailed documentation of

Rank and count of the top names for baby girls, changes in rank since the previous year and breakdown by country, region, mother's age and month of birth.

This dataset contains the digitized treatments in Plazi based on the original journal article Weigend, Maximilian, Hilger, Hartmut H. (2010): Codonaceae-a newly required family name in Boraginales. Phytotaxa 10: 26-30, DOI: 10.11646/phytotaxa.10.1.3

This layer presents the 2010 Census tract boundaries for Arlington County. The geography and attributes were sourced from US Census Bureau 2010 Cartographic Boundary Files.Contact: Department of Environmental ServicesData Accessibility: Publicly AvailableUpdate Frequency: NeverDocument Last Revision Date: 11/3/2023Document Creation Date: 11/3/2023Feature Dataset Name: Census_2010Layer Name: Census_2010_Tracts_poly

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological inference methods in identifying voter race. In this article, we test the viability of employing BISG to redistricting under two posterior allocation methods for race assignment: plurality versus probabilistic. We validate these methods through 10,000 redistricting simulations of North Carolina and Georgia’s congressional districts and compare BISG estimates to actual voter file racial data. We find that probabilistic summing of the BISG posteriors significantly reduces error rates at the precinct and district level relative to plurality racial assignment, and therefore should be the preferred method when using BISG for redistricting. Our results suggest that BISG can aid in the construction of majority minority districts during the redistricting process. The following data set consists of voter lists for NC and GA necessary to estimate the race of individuals via BISG and followed by redistricting simulations.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

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 2012-2016, to show the number of grandparents living with grandchildren, and the number and percentage of grandparents responsible for grandchildren, by census tract 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 2012-2016). 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, click here.Attributes: GEOID10 = 2010 Census tract identifier (combination of Federal Information Processing Series (FIPS) codes for state, county, and census tract) County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county) Area_Name = 2010 Census tract name- - - - - -Total_Population = # Total Population, 2016 Total_Population_MOE_2016 = # Total population (Margin of Error), 2016- - - - - -Num_GrParent_LivW_GChildU18 = # Grandparents living with grandchildren under age 18, 2016 Num_GrParent_LivW_GChildU18_MOE = # Grandparents living with grandchildren under age 18, 2016 (Margin of Error) Num_GrParent_RespFor_GChild = # Grandparents Responsible for grandchildren under age 18, 2016 Num_GrParent_RespFor_GChild_MOE = # Grandparents Responsible for grandchildren under age 18 (Margin of Error), 2016 Pct_GrParent_RespFor_GChild = % Grandparents Responsible for grandchildren under age 18, 2016 Pct_GrParent_RespFor_GChild_MOE = % Grandparents Responsible for grandchildren under age 18 (Margin of Error), 2016- - - - - -Planning_Region = Planning region designation for ARC purposes AcresLand = Land area within the tract (in acres) AcresWater = Water area within the tract (in acres) AcresTotal = Total area within the tract (in acres) SqMi_Land = Land area within the tract (in square miles) SqMi_Water = Water area within the tract (in square miles) SqMi_Total = Total area within the tract (in square miles) TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively. CountyName = County Name last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.