Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Current block groups do not always maintain these same block number to block group relationships due to boundary and feature changes that occur throughout the decade. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the GEOID for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block GEOIDs are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain 600 to 3,000 people. Local participants delineated most block groups as part of the Census Bureau's PSAP. The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains one or more block groups and block groups have unique numbers within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and AIANNH areas.Block groups have a valid range of zero (0) through nine (9). Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS). The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/). Block Groups (BGs) are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used to present data and control block numbering. A block group consists of clusters of blocks within the same census tract that have the same first digit of their four-digit census block number. For example, blocks 3001, 3002, 3003, . . . , 3999 in census tract 1210.02 belong to BG 3 in that census tract. Most BGs were delineated by local participants in the Census Bureau’s Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where a local or tribal government declined to participate in PSAP, and a regional organization or the State Data Center was not available to participate. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within the census tract. Within the standard census geographic hierarchy, BGs never cross state, county, or census tract boundaries, but may cross the boundaries of any other geographic entity. Tribal census tracts and tribal BGs are separate and unique geographic areas defined within federally recognized American Indian reservations and can cross state and county boundaries (see “Tribal Census Tract” and “Tribal Block Group”). The tribal census tracts and tribal block groups may be completely different from the standard county-based census tracts and block groups defined for the same area. Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/BG/ on June 22, 2023

🇺🇸 United States English Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3). Current block groups do not always maintain these same block number to block group relationships due to boundary and feature changes that occur throughout the decade. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the GEOID for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block GEOIDs are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain 600 to 3,000 people. Local participants delineated most block groups as part of the Census Bureau's PSAP. The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains one or more block groups and block groups have unique numbers within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and AIANNH areas.Block groups have a valid range of zero (0) through nine (9). Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

A. SUMMARY Census Block groups are the next level above census blocks in the geographic hierarchy. Block groups are a combination of census blocks that is a subdivision of a census tract.A block group consists of all census blocks whose numbers begin with the same digit in a given census tract; for example, block group 3 includes all census blocks numbered in the 300s. More information on the census tracts can be found here. B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau. C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco Clipped to SF Shoreline

A. SUMMARY Census blocks with Pacific Ocean and San Francisco Bay water clipped out. Census Block groups are the next level above census blocks in the geographic hierarchy. Block groups are a combination of census blocks that is a subdivision of a census tract.A block group consists of all census blocks whose numbers begin with the same digit in a given census tract; for example, block group 3 includes all census blocks numbered in the 300s. More information on the census tracts can be found here. B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau and clipped using the water boundaries provided by the U.S. Census Bureau. C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

USA Census Block Groups (CBG) for Urban Search and Rescue. This layer can be used for search segment planning. Block groups generally contain between 600 and 5,000 people and the boundaries generally follow existing roads and waterways. The field segment_designation is the last 6 digits of the unique identifier and matches the field in the SARCOP Segment layer.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.* *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block group itself.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

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 numbers and percentages for voting age population 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 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

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

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

CountyName

County Name

VotingAgeCitizen_e

# Citizen, 18 and over population, 2017

VotingAgeCitizen_m

# Citizen, 18 and over population, 2017 (MOE)

VotingAgeCitizenMale_e

# Male citizen, 18 and over population, 2017

VotingAgeCitizenMale_m

# Male citizen, 18 and over population, 2017 (MOE)

pVotingAgeCitizenMale_e

% Male citizen, 18 and over population, 2017

pVotingAgeCitizenMale_m

% Male citizen, 18 and over population, 2017 (MOE)

VotingAgeCitizenFemale_e

# Female citizen, 18 and over population, 2017

VotingAgeCitizenFemale_m

# Female citizen, 18 and over population, 2017 (MOE)

pVotingAgeCitizenFemale_e

% Female citizen, 18 and over population, 2017

pVotingAgeCitizenFemale_m

% Female citizen, 18 and over population, 2017 (MOE)

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.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

Current (2021) and projected numbers of Plug-in Electrical Vehicles (PEVs) at the census block group level for the Delaware Valley region. The projected PEV distribution is based on a scenario in which 5 percent of passenger vehicles in the Greater Philadelphia region (or about 200,000 vehicles) are PEVs.

Also includes data projecting workplace charging demand in number of charging events and kilowatt-hours of demand by census block group based on the aforementioned PEV projections for the following three scenarios:

- workplace charging is free (free charging),

- workplace charging is twice the cost of home charging (paid charging).

The following datasets were used to obtain these results using the EV Planning Toolkit:

- 2021 vehicle registration data provided by the Pennsylvania Department of Transportation

- 2021 vehicle registration data from the New Jersey Motor Vehicle Commission provided by the New Jersey Department of Environmental Protection

• 2015-2019 American Community Survey 5-year Estimates

-

• LEHD Origin-Destination Employment Statistics (LODES), version 7

- Block group-to-block group commuting distances from DVRPC's Travel Improvement Model 2.1, accessed December 2017.

• Block group-to-block group commuting distances calculated from Esri’s ArcGIS Network Analyst, used in 2017.

Field	Alias	Description
GEOID10	GEOID10	Census Block Group identifier
Mun_Name	Municipality Name	The name of the municipality in which the Block Group lies
GEOID_Muni	GEOID of Municipality	Municipality identifier
SQMI_LAND	Land area	Square miles of land area
POP	Population	Number of people
HOUSUNIT	Housing Units	Number of housing units
JOBS	Jobs	Number of jobs
PASS_VEH	Number of Passenger Vehicles	Number of passenger vehicles per block group as of 2021
CurPEV	Current Number of PEVs	Number of PEVs per block group as of 2021
FutPEV	Projected Number of PEVs	Number of projected PEVs per block group at 5% regional penetration
CuPEV_SM	Current PEVs per square mile	Number of PEVs per square mile in the block group as of 2021
FUPEV_SM	Projected PEVs per square mile	Number of projected PEVs per square mile per block group at 5% regional penetration
CuPEVPop	Current number of PEVs per 100 people	Number of PEVs per 100 people per block group as of 2021
FuPEVPop	Projected number of PEVs per 100 people	Number of projected PEVs per 100 people per block group at 5% regional penetration
CuPEV_HU	Current number of PEVs per 100 housing units	Number of PEVs per 100 housing units per block group as of 2021
FuPEV_HU	Projected number of PEVs per 100 housing units	Number of projected PEVs per 100 housing units per block group at 5% regional penetration
PerCuPEV	Current Percentage of Passenger Vehicles That Are PEVs	Percentage of total passenger vehicles that are PEVs per block group as of 2021
PerFuPEV	Projected Percentage of Passenger Vehicles That Are PEVs	Percentage of total passenger vehicles that are projected to be PEVs per block group at 5% regional penetration
FC_KD	Free Charging - kWh of Demand	Kilowatt-hours of workplace charging demand per day per block group when workplace charging is free at 5% regional PEV penetration
FC_CE	Free Charging - Number of Charging Events	Number of workplace charging events per day per block group when workplace charging is free at 5% regional PEV penetration
FC_KD_SM	Free Charging - kWh of Demand per sq. mi.	Kilowatt-hours of workplace charging demand per day per square mile per block group when workplace charging is free at 5% regional PEV penetration
FC_CE_SM	Free Charging - Charging Events per sq. mi.	Number of workplace charging events per day per square mile per block group when workplace charging is free at 5% regional PEV penetration
FC_KPE	Free Charging - kWh per charging event	Kilowatt-hours per workplace charging event per block group when workplace charging is free at 5% regional PEV penetration
FC_KD_JB	Free Charging - kWh of Demand per Job	Kilowatt-hours of workplace charging demand per day per job per block group when workplace charging is free at 5% regional PEV penetration
FC_CE_JB	Free Charging - Charging Events per Job	Number of workplace charging events per job per block group when workplace charging is free at 5% regional PEV penetration
PC_KD	Paid Charging - kWh of Demand	Kilowatt-hours of workplace charging demand per day per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration
PC_CE	Paid Charging - Number of Charging Events	Number of workplace charging events per day per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration
PC_KD_SM	Paid Charging - kWh of Demand per sq. mi.	Kilowatt-hours of workplace charging demand per day per square mile per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration
PC_CE_SM	Paid Charging - Charging Events per sq. mi.	Number of workplace charging events per day per square mile per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration
PC_KPE	Paid Charging - kWh per charging event	Kilowatt-hours per workplace charging event per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration
PC_KD_JB	Paid Charging - kWh of Demand per Job	Kilowatt-hours of workplace charging demand per day per job per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration
PC_CE_JB	Paid Charging - Charging Events per Job	Number of workplace charging events per job per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration

If you have any questions regarding this analysis or datasets used in the analysis, please contact: Sean Greene, Manager, Air Quality Programs | sgreene@dvrpc.org | (215) 238-2860

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 unemployment numbers and percentages 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 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

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

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

CountyName

County Name

Pop16P_e

# Population 16 years and over, 2017

Pop16P_m

# Population 16 years and over, 2017 (MOE)

InLabForce_e

# In labor force, 2017

InLabForce_m

# In labor force, 2017 (MOE)

pInLabForce_e

% In labor force, 2017

pInLabForce_m

% In labor force, 2017 (MOE)

CivLabForce_e

# In civilian labor force, 2017

CivLabForce_m

# In civilian labor force, 2017 (MOE)

pCivLabForce_e

% In civilian labor force, 2017

pCivLabForce_m

% In civilian labor force, 2017 (MOE)

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

pCivEmployed_e

% Civilian employed, 2017

pCivEmployed_m

% Civilian employed, 2017 (MOE)

Unemployed_e

# Civilian unemployed, 2017

Unemployed_m

# Civilian unemployed, 2017 (MOE)

pUnemployed_e

% Civilian unemployed, 2017

pUnemployed_m

% Civilian unemployed, 2017 (MOE)

ArmedForce_e

# In armed forces, 2017

ArmedForce_m

# In armed forces, 2017 (MOE)

pArmedForce_e

% In armed forces, 2017

pArmedForce_m

% In armed forces, 2017 (MOE)

NotLabForce_e

# Not in labor force, 2017

NotLabForce_m

# Not in labor force, 2017 (MOE)

pNotLabForce_e

% Not in labor force, 2017

pNotLabForce_m

% Not in labor force, 2017 (MOE)

pUnempOLabForce_e

% Unemployed as part of total labor force (including armed forces), 2017

pUnempOLabForce_m

% Unemployed as part of total labor force (including armed forces), 2017 (MOE)

UnempCivLabForce_e

# Civilian Unemployed, 2017

UnempCivLabForce_m

# Civilian Unemployed, 2017 (MOE)

pUnempCivLabForce_e

% Unemployment Rate, 2017

pUnempCivLabForce_m

% Unemployment Rate, 2017 (MOE)

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.

2023 Tract-level Indicators of Potential Disadvantage for the DVRPC Region Title VI of the Civil Rights Act states that "no person in the United States, shall, on the grounds of race, color, or national origin be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving federal financial assistance.” Under Title VI of the Civil Rights Act, Metropolitan Planning Organizations (MPOs) are directed to create a method for ensuring that Title VI compliance issues are investigated and evaluated in transportation decision-making. There is additional guidance from the FHWA’s Title VI and Additional Nondiscrimination requirements (2017), and FTA’s Title VI requirements and guidelines (2012). The Indicators of Potential Disadvantage (IPD) analysis is used throughout DVRPC to demonstrate compliance with Title VI of the Civil Rights Act.

This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2019-2023 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field)

Field	Alias	Description	Source
year	IPD analysis year		DVRPC
geoid20	11-digit tract GEOID	Census tract identifier	ACS 5-year
statefp	2-digit state GEOID	FIPS Code for State	ACS 5-year
countyfp	3-digit county GEOID	FIPS Code for County	ACS 5-year
tractce	Tract number	Tract Number	ACS 5-year
name	Tract number	Census tract identifier with decimal places	ACS 5-year
namelsad	Tract name	Census tract name with decimal places	ACS 5-year
d_class	Disabled percentile class	Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average	calculated
d_est	Disabled count estimate	Estimated count of disabled population	ACS 5-year
d_est_moe	Disabled count margin of error	Margin of error for estimated count of disabled population	ACS 5-year
d_pct	Disabled percent estimate	Estimated percentage of disabled population	ACS 5-year
d_pct_moe	Disabled percent margin of error	Margin of error for percentage of disabled population	ACS 5-year
d_pctile	Disabled percentile	Tract's regional percentile for percentage disabled	calculated
d_score	Disabled percentile score	Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4	calculated
em_class	Ethnic minority percentile class	Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average	calculated
em_est	Ethnic minority count estimate	Estimated count of Hispanic/Latino population	ACS 5-year
em_est_moe	Ethnic minority count margin of error	Margin of error for estimated count of Hispanic/Latino population	ACS 5-year
em_pct	Ethnic minority percent estimate	Estimated percentage of Hispanic/Latino population	calculated
em_pct_moe	Ethnic minority percent margin of error	Margin of error for percentage of Hispanic/Latino population	calculated
em_pctile	Ethnic minority percentile	Tract's regional percentile for percentage Hispanic/Latino	calculated
em_score	Ethnic minority percentile score	Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4	calculated
f_class	Female percentile class	Classification of tract's female percentage as: well below average, below average, average, above average, or well above average	calculated
f_est	Female count estimate	Estimated count of female population	ACS 5-year
f_est_moe	Female count margin of error	Margin of error for estimated count of female population	ACS 5-year
f_pct	Female percent estimate	Estimated percentage of female population	ACS 5-year
f_pct_moe	Female percent margin of error	Margin of error for percentage of female population	ACS 5-year
f_pctile	Female percentile	Tract's regional percentile for percentage female	calculated
f_score	Female percentile score	Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4	calculated
fb_class	Foreign-born percentile class	Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average	calculated
fb_est	Foreign-born count estimate	Estimated count of foreign born population	ACS 5-year
fb_est_moe	Foreign-born count margin of error	Margin of error for estimated count of foreign born population	ACS 5-year
fb_pct	Foreign-born percent estimate	Estimated percentage of foreign born population	calculated
fb_pct_moe	Foreign-born percent margin of error	Margin of error for percentage of foreign born population	calculated
fb_pctile	Foreign-born percentile	Tract's regional percentile for percentage foreign born	calculated
fb_score	Foreign-born percentile score	Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4	calculated
le_class	Limited English proficiency percentile class	Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average	calculated
le_est	Limited English proficiency count estimate	Estimated count of limited english proficiency population	ACS 5-year
le_est_moe	Limited English proficiency count margin of error	Margin of error for estimated count of limited english proficiency population	ACS 5-year
le_pct	Limited English proficiency percent estimate	Estimated percentage of limited english proficiency population	ACS 5-year
le_pct_moe	Limited English proficiency percent margin of error	Margin of error for percentage of limited english proficiency population	ACS 5-year
le_pctile	Limited English proficiency percentile	Tract's regional percentile for percentage limited english proficiency	calculated
le_score	Limited English proficiency percentile score	Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4	calculated
li_class	Low-income percentile class	Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average	calculated
li_est	Low-income count estimate	Estimated count of low income (below 200% of poverty level) population	ACS 5-year
li_est_moe	Low-income count margin of error	Margin of error for estimated count of low income population	ACS 5-year
li_pct	Low-income percent estimate	Estimated percentage of low income (below 200% of poverty level) population	calculated
li_pct_moe	Low-income percent margin of error	Margin of error for percentage of low income population	calculated
li_pctile	Low-income percentile	Tract's regional percentile for percentage low income	calculated
li_score	Low-income percentile score	Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4	calculated
oa_class	Older adult percentile class	Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average	calculated
oa_est	Older adult count estimate	Estimated count of older adult population (65 years or older)	ACS 5-year
oa_est_moe	Older adult count margin of error	Margin of error for estimated count of older adult population	ACS 5-year
oa_pct	Older adult percent estimate	Estimated percentage of older adult population (65 years or older)	ACS 5-year
oa_pct_moe	Older adult percent margin of error	Margin of error for percentage of older adult population	ACS 5-year
oa_pctile	Older adult percentile	Tract's regional percentile for percentage older adult	calculated
oa_score	Older adult percentile score	Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4	calculated
rm_class	Racial minority percentile class	Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average	calculated
rm_est	Racial minority count estimate	Estimated count of non-white population	ACS 5-year
rm_est_moe	Racial minority count margin of error	Margin of error for estimated count of non-white population	ACS 5-year
rm_pct	Racial minority percent estimate	Estimated percentage of non-white population	calculated
rm_pct_moe	Racial minority percent margin of error	Margin of error for

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: 2020 US Census Bureau, TIGER/Line Shapefiles Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field)

Field	Alias	Description	Source
year	IPD analysis year		DVRPC
geoid20	11-digit tract GEOID	Census tract identifier	ACS 5-year
statefp	2-digit state GEOID	FIPS Code for State	ACS 5-year
countyfp	3-digit county GEOID	FIPS Code for County	ACS 5-year
tractce	Tract number	Tract Number	ACS 5-year
name	Tract number	Census tract identifier with decimal places	ACS 5-year
namelsad	Tract name	Census tract name with decimal places	ACS 5-year
d_class	Disabled percentile class	Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average	calculated
d_est	Disabled count estimate	Estimated count of disabled population	ACS 5-year
d_est_moe	Disabled count margin of error	Margin of error for estimated count of disabled population	ACS 5-year
d_pct	Disabled percent estimate	Estimated percentage of disabled population	ACS 5-year
d_pct_moe	Disabled percent margin of error	Margin of error for percentage of disabled population	ACS 5-year
d_pctile	Disabled percentile	Tract's regional percentile for percentage disabled	calculated
d_score	Disabled percentile score	Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4	calculated
em_class	Ethnic minority percentile class	Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average	calculated
em_est	Ethnic minority count estimate	Estimated count of Hispanic/Latino population	ACS 5-year
em_est_moe	Ethnic minority count margin of error	Margin of error for estimated count of Hispanic/Latino population	ACS 5-year
em_pct	Ethnic minority percent estimate	Estimated percentage of Hispanic/Latino population	calculated
em_pct_moe	Ethnic minority percent margin of error	Margin of error for percentage of Hispanic/Latino population	calculated
em_pctile	Ethnic minority percentile	Tract's regional percentile for percentage Hispanic/Latino	calculated
em_score	Ethnic minority percentile score	Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4	calculated
f_class	Female percentile class	Classification of tract's female percentage as: well below average, below average, average, above average, or well above average	calculated
f_est	Female count estimate	Estimated count of female population	ACS 5-year
f_est_moe	Female count margin of error	Margin of error for estimated count of female population	ACS 5-year
f_pct	Female percent estimate	Estimated percentage of female population	ACS 5-year
f_pct_moe	Female percent margin of error	Margin of error for percentage of female population	ACS 5-year
f_pctile	Female percentile	Tract's regional percentile for percentage female	calculated
f_score	Female percentile score	Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4	calculated
fb_class	Foreign-born percentile class	Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average	calculated
fb_est	Foreign-born count estimate	Estimated count of foreign born population	ACS 5-year
fb_est_moe	Foreign-born count margin of error	Margin of error for estimated count of foreign born population	ACS 5-year
fb_pct	Foreign-born percent estimate	Estimated percentage of foreign born population	calculated
fb_pct_moe	Foreign-born percent margin of error	Margin of error for percentage of foreign born population	calculated
fb_pctile	Foreign-born percentile	Tract's regional percentile for percentage foreign born	calculated
fb_score	Foreign-born percentile score	Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4	calculated
le_class	Limited English proficiency percentile class	Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average	calculated
le_est	Limited English proficiency count estimate	Estimated count of limited english proficiency population	ACS 5-year
le_est_moe	Limited English proficiency count margin of error	Margin of error for estimated count of limited english proficiency population	ACS 5-year
le_pct	Limited English proficiency percent estimate	Estimated percentage of limited english proficiency population	ACS 5-year
le_pct_moe	Limited English proficiency percent margin of error	Margin of error for percentage of limited english proficiency population	ACS 5-year
le_pctile	Limited English proficiency percentile	Tract's regional percentile for percentage limited english proficiency	calculated
le_score	Limited English proficiency percentile score	Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4	calculated
li_class	Low-income percentile class	Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average	calculated
li_est	Low-income count estimate	Estimated count of low income (below 200% of poverty level) population	ACS 5-year
li_est_moe	Low-income count margin of error	Margin of error for estimated count of low income population	ACS 5-year
li_pct	Low-income percent estimate	Estimated percentage of low income (below 200% of poverty level) population	calculated
li_pct_moe	Low-income percent margin of error	Margin of error for percentage of low income population	calculated
li_pctile	Low-income percentile	Tract's regional percentile for percentage low income	calculated
li_score	Low-income percentile score	Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4	calculated
oa_class	Older adult percentile class	Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average	calculated
oa_est	Older adult count estimate	Estimated count of older adult population (65 years or older)	ACS 5-year
oa_est_moe	Older adult count margin of error	Margin of error for estimated count of older adult population	ACS 5-year
oa_pct	Older adult percent estimate	Estimated percentage of older adult population (65 years or older)	ACS 5-year
oa_pct_moe	Older adult percent margin of error	Margin of error for percentage of older adult population	ACS 5-year
oa_pctile	Older adult percentile	Tract's regional percentile for percentage older adult	calculated
oa_score	Older adult percentile score	Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4	calculated
rm_class	Racial minority percentile class	Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average	calculated
rm_est	Racial minority count estimate	Estimated count of non-white population	ACS 5-year
rm_est_moe	Racial minority count margin of error	Margin of error for estimated count of non-white population	ACS 5-year
rm_pct	Racial minority percent estimate	Estimated percentage of non-white population	calculated
rm_pct_moe	Racial minority percent margin of error	Margin of error for percentage of non-white population	calculated
rm_pctile	Racial minority percentile	Tract's regional percentile for percentage non-white	calculated
rm_score	Racial minority percentile score	Corresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4	calculated
tot_pp	Total population estimate	Estimated total population of tract (universe [or

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 occupation type numbers and percentages 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 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

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

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

CountyName

County Name

CivEmployed_e

# Civilian employed, 2017

CivEmployed_m

# Civilian employed, 2017 (MOE)

ManBusSciArtOcc_e

# Management, business, science, and arts occupations, 2017

ManBusSciArtOcc_m

# Management, business, science, and arts occupations, 2017 (MOE)

pManBusSciArtOcc_e

% Management, business, science, and arts occupations, 2017

pManBusSciArtOcc_m

% Management, business, science, and arts occupations, 2017 (MOE)

ServiceOcc_e

# Service occupations, 2017

ServiceOcc_m

# Service occupations, 2017 (MOE)

pServiceOcc_e

% Service occupations, 2017

pServiceOcc_m

% Service occupations, 2017 (MOE)

SalesOffOcc_e

# Sales and office occupations, 2017

SalesOffOcc_m

# Sales and office occupations, 2017 (MOE)

pSalesOffOcc_e

% Sales and office occupations, 2017

pSalesOffOcc_m

% Sales and office occupations, 2017 (MOE)

NatlConsMaintOcc_e

# Natural resources, construction, and maintenance occupations, 2017

NatlConsMaintOcc_m

# Natural resources, construction, and maintenance occupations, 2017 (MOE)

pNatlConsMaintOcc_e

% Natural resources, construction, and maintenance occupations, 2017

pNatlConsMaintOcc_m

% Natural resources, construction, and maintenance occupations, 2017 (MOE)

ProdTransOcc_e

# Production, transportation, and material moving occupations, 2017

ProdTransOcc_m

# Production, transportation, and material moving occupations, 2017 (MOE)

pProdTransOcc_e

% Production, transportation, and material moving occupations, 2017

pProdTransOcc_m

% Production, transportation, and material moving occupations, 2017 (MOE)

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.

Field Definition:GEOID - "Census tract identifier; a concatenation of 2020 Census state FIPS code, county FIPS code, and census tract code"NAMELSAD - Census translated legal/statistical area description and the census tract nameALAND - Census Area LandAWATER - Census Area waterINTPTLAT - Census Internal Point (Latitude)INTPTLON - Census Internal Point (Longitude)NAME20 - "2020 Census tract name, this is the census tract code converted to an integer or integer plus two-digit decimal if the last two characters of the code are not both zeros"POPULATION - Total PopulationP18PLUS - Population 18 years and olderHHPOP - Household PopulationGQ - Group Quarters PopulationHOUSING - Total Housing unitsOCCUNITS - Occupied Housing Units (Households)VACUNITS - Vacant Housing UnitsVACRATE -Vacancy RateHISPANIC - Hispanic or Latino NH_WHT - Not Hispanic or Latino, White alone NH_BLK - Not Hispanic or Latino, Black or African American alone NH_IND - Not Hispanic or Latino, American Indian and Alaska Native aloneNH_ASN - Not Hispanic or Latino, Asian aloneNH_HWN - Not Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone NH_OTH - Not Hispanic or Latino, Some Other Race alone NH_TWO - Not Hispanic or Latino, Population of two or more races