This is Census 2020 block data specifically formatted for use by the Environmental Protection Agency (EPA) in-development Environmental Justice Analysis Multisite (EJAM) tool, which uses R code to find which block centroids are within X miles of each specified point (e.g., regulated facility), and to find those distances. The datasets have latitude and longitude of each block's internal point, as provided by Census Bureau, and the FIPS code of the block and its parent block group. The datasets also include a weight for each block, representing this block's Census 2020 population count as a fraction of the count for the parent block group overall, for use in estimating how much of a given block group is within X miles of a specified point or inside a polygon of interest. The datasets also have an effective radius of each block, which is what the radius would be in miles if the block covered the same area in square miles but were circular. The datasets also have coordinates in units that facilitate building a quadtree index of locations. They are in R data.table format, saved as .rda or .arrow files to be read by R code.

This layer is no longer being actively maintained. For the latest broadband availability data from FCC, please see the FCC Broadband Data Collection (BDC). This layer shows fixed broadband availability for every neighborhood in the U.S. and outlying areas for June 2023.This layer is a composite of five sublayers with adjacent scale ranges showing the broadband score across the U.S. and outlying areas, at five different geographies – State, County, Tract, Block Group and Block. The broadband score is an index based on the FCC’s minimum standard of broadband of 25 megabits per second (Mbps) download and 3 Mbps upload. A geography with speeds of 25/3 Mbps is awarded 100 points. Each type of geometry contains housing, population, and internet usage data taken from the following sources:US Census Bureau 2010 Census data (2010)USDA Non-Rural Areas (2013)FCC Form 477 Fixed Broadband Deployment Data (January - June 2021)FCC Population, Housing Unit, and Household Estimates (2019). Note that these are derived from Census and other data.Measurement Lab (Jan - June 2021)Broadband offering data from each provider for all geographies are available in related tables. Field Names / Record StructureThis layer includes over 150 attributes relating to reported speed and service information. In addition:Each block includes housing unit, household, and population estimates from the FCC.Each block has an attribute named WaterOnly that indicates if it is entirely water (yes/no).Each block has two attributes indicating whether it is urban or rural (CensusUrbanRural and USDAUrbanRural). For units larger than blocks, block count (urban/rural) was used to determine this. Some tracts and block groups have an equal number of urban and rural blocks—so a new coded value was introduced: S (split). All blocks are either U or R, while tracts and block groups can be U, R, or S.Each block has three attributes indicating whether it is part of a Tribal Block Group, is part of an American Indian/Alaska Native/Native Hawaiian Area (AIANNHA) and the AIANNHA name.US Census and USDA Rurality valuesAmalgamated broadband speed measurement categories based on Form 477. These include:99: All Terrestrial Broadband Plus Satellite98: All Terrestrial Broadband97: Cable Modem96: DSL95: All Other (Electric Power Line, Other Copper Wireline, Other)The FCC Speed Values method is applied to all speeds from all data sources within this service. This includes:Geography: State, County, Tract, Block Group, BlockData source: FCC and M-LabWithin this method, speed values are shown as such:<1 Mbps, reported up to three decimal points>= 1 and < 2 Mbps, rounded to the nearest tenth>= 2 and < 10 Mbps, truncated to the lower integer>= 10 and < 1000 Mbps, rounded to the nearest integer>= 1000 Mbps, the published bandwidth = 1000 MbpsEach sublayer has a varying number of attributes from these sources, depending on what data is available for the level of granularity. The following table displays what information is included with which geometry types: GeometryFCC Form 477 Fixed (Jan - Dec 2020)FCC Demographic Estimates (2019)M-Lab (Jan - Dec 2020)BroadbandNow Avg. Min. Terrestrial Broadband Plan PricesUrban/rural flags (Census and USDA)StateYesYesYesYesNoCountyYesYesYesYesNoTractYesYesNoYesYes (U, R, S)Block GroupYesYesNoYesYes (U, R, S)BlockYesYesNoYesYes (U, R) Additional ResourcesFCC Staff Block EstimatesFixed Broadband Deployment Data from FCC Form 477Digital Divide: Broadband Pricing by State, ZIP Code, and Income Level (BroadbandUSA)Open Internet Measurement (M-Lab)Eligibility Area Map Datasets (USDA)

HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Developments Date Updated: Q1 2025

Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: Q1 2025

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 the number and percentages of opportunity to youth 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

PopAges1619_e

# Population, ages 16-19, 2017

PopAges1619_m

# Population, ages 16-19, 2017 (MOE)

DisconYouth_e

# Disconnected youth: ages 16-19 not in school or in labor force, 2017

DisconYouth_m

# Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

pDisconYouth_e

% Disconnected youth: ages 16-19 not in school or in labor force, 2017

pDisconYouth_m

% Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

OwnChildInFam_e

# Own children in families, 2017

OwnChildInFam_m

# Own children in families, 2017 (MOE)

NoParentLabForce_e

# Own children in families with no parent in the labor force, 2017

NoParentLabForce_m

# Own children in families with no parent in the labor force, 2017 (MOE)

pNoParentLabForce_e

% Own children in families with no parent in the labor force, 2017

pNoParentLabForce_m

% Own children in families with no parent in the labor force, 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.

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 US Congress 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.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This dataset provides the location and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/ Development FAQs - IMS/PIC | HUD.gov / U.S. Department of Housing and Urban Development (HUD), for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Buildings Date Updated: Q1 2025

The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
 ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
Null - Could not be geocoded (does not appear on the map) 

For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

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.

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 the method of transportation that workers use to get to work and their mean travel time 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

Workers16P_e

# Workers 16 years and over, 2017

Workers16P_m

# Workers 16 years and over, 2017 (MOE)

Drove_e

# Car, truck, or van - drove alone to work, 2017

Drove_m

# Car, truck, or van - drove alone to work, 2017 (MOE)

pDrove_e

% Car, truck, or van - drove alone to work, 2017

pDrove_m

% Car, truck, or van - drove alone to work, 2017 (MOE)

Carpool_e

# Car, truck, or van - carpooled to work, 2017

Carpool_m

# Car, truck, or van - carpooled to work, 2017 (MOE)

pCarpool_e

% Car, truck, or van - carpooled to work, 2017

pCarpool_m

% Car, truck, or van - carpooled to work, 2017 (MOE)

PublicTrans_e

# Public transportation (excluding taxicab) to work, 2017

PublicTrans_m

# Public transportation (excluding taxicab) to work, 2017 (MOE)

pPublicTrans_e

% Public transportation (excluding taxicab) to work, 2017

pPublicTrans_m

% Public transportation (excluding taxicab) to work, 2017 (MOE)

Walked_e

# Walked to work, 2017

Walked_m

# Walked to work, 2017 (MOE)

pWalked_e

% Walked to work, 2017

pWalked_m

% Walked to work, 2017 (MOE)

OtherCommute_e

# Other means to work, 2017

OtherCommute_m

# Other means to work, 2017 (MOE)

pOtherCommute_e

% Other means to work, 2017

pOtherCommute_m

% Other means to work, 2017 (MOE)

WorkAtHome_e

# Worked at home, 2017

WorkAtHome_m

# Worked at home, 2017 (MOE)

pWorkAtHome_e

% Worked at home, 2017

pWorkAtHome_m

% Worked at home, 2017 (MOE)

aMeanTravelTimeWork_e

Mean travel time to work (minutes), 2017

aMeanTravelTimeWork_m

Mean travel time to work (minutes), 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.

The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM(r) classification, census block group, and latitude-longitude. PRIZM(r) classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM(r) classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because of its higher spatial accuracy than other databases describing US Census geographies, including those provided by the US Census. This database includes data only for environmental improvement: In regard to the following environmental and quality of life issues, I'd like you to tel... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-bes%2F48%2F580 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 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.

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

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

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

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

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

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

pch

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

chp

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

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

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

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

# Area, Acres, 2017

SqMi

# Area, square miles, 2017

County

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

CountyName

County Name

TotPop_e

# Total population, 2017

TotPop_m

# Total population, 2017 (MOE)

rPopDensity

Population density (people per square mile), 2017

last_edited_date

Last date the feature was edited by ARC

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

Date: 2013-2017

For additional information, please visit the Census ACS website.

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 the number of grandparents living with grandchildren and the number and percentage of grandparents responsible for grandchildren by Zip Code Tabulation Area in the Atlanta region.

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

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

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

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

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

pch

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

chp

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

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

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

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

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

GrandWChild_e

# Grandparents living with grandchildren under age 18, 2017

GrandWChild_m

# Grandparents living with grandchildren under age 18, 2017 (MOE)

GrandRespChild_e

# Grandparents responsible for grandchildren under age 18, 2017

GrandRespChild_m

# Grandparents responsible for grandchildren under age 18, 2017 (MOE)

pGrandRespChild_e

% Grandparents responsible for grandchildren under age 18, 2017

pGrandRespChild_m

% Grandparents responsible for grandchildren under age 18, 2017 (MOE)

PopU18_e

# Population under age 18, 2017

PopU18_m

# Population under age 18, 2017 (MOE)

ChildrenByGrandHH_e

# Children raised by grandparent, 2017

ChildrenByGrandHH_m

# Children raised by grandparent, 2017 (MOE)

pChildrenByGrandHH_e

% Children raised by grandparent, 2017

pChildrenByGrandHH_m

% Children raised by grandparent, 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.

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 the number and percentages of migration 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

Pop1P_e

# Population ages 1 year and over, 2017

Pop1P_m

# Population ages 1 year and over, 2017 (MOE)

SameHouse_e

# Living in the same house as 1 year ago, 2017

SameHouse_m

# Living in the same house as 1 year ago, 2017 (MOE)

pSameHouse_e

% Living in the same house as 1 year ago, 2017

pSameHouse_m

% Living in the same house as 1 year ago, 2017 (MOE)

DiffHouseInUS_e

# Living in a different house in the U.S. 1 year ago, 2017

DiffHouseInUS_m

# Living in a different house in the U.S. 1 year ago, 2017 (MOE)

pDiffHouseInUS_e

% Living in a different house in the U.S. 1 year ago, 2017

pDiffHouseInUS_m

% Living in a different house in the U.S. 1 year ago, 2017 (MOE)

SameCounty_e

# Living in a different house in the same county 1 year ago, 2017

SameCounty_m

# Living in a different house in the same county 1 year ago, 2017 (MOE)

pSameCounty_e

% Living in a different house in the same county 1 year ago, 2017

pSameCounty_m

% Living in a different house in the same county 1 year ago, 2017 (MOE)

DiffCounty_e

# Living in a different county 1 year ago, 2017

DiffCounty_m

# Living in a different county 1 year ago, 2017 (MOE)

pDiffCounty_e

% Living in a different county 1 year ago, 2017

pDiffCounty_m

% Living in a different county 1 year ago, 2017 (MOE)

SameState_e

# Living in a different county, same state 1 year ago, 2017

SameState_m

# Living in a different county, same state 1 year ago, 2017 (MOE)

pSameState_e

% Living in a different county, same state 1 year ago, 2017

pSameState_m

% Living in a different county, same state 1 year ago, 2017 (MOE)

Diff_State_e

# Living in a different state 1 year ago, 2017

Diff_State_m

# Living in a different state 1 year ago, 2017 (MOE)

pDiff_State_e

% Living in a different state 1 year ago, 2017

pDiff_State_m

% Living in a different state 1 year ago, 2017 (MOE)

Abroad_e

# Living abroad 1 year ago, 2017

Abroad_m

# Living abroad 1 year ago, 2017 (MOE)

pAbroad_e

% Living abroad 1 year ago, 2017

pAbroad_m

% Living abroad 1 year ago, 2017 (MOE)

Moved_e

# Moved in the last year, 2017

Moved_m

# Moved in the last year, 2017 (MOE)

pMoved_e

% Moved in the last year, 2017

pMoved_m

% Moved in the last year, 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.

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

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

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

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

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

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

pch

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

chp

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

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

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

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

# Area, Acres, 2017

SqMi

# Area, square miles, 2017

County

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

CountyName

County Name

TotPop_e

# Total population, 2017

TotPop_m

# Total population, 2017 (MOE)

rPopDensity

Population density (people per square mile), 2017

last_edited_date

Last date the feature was edited by ARC

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

Date: 2013-2017

For additional information, please visit the Census ACS website.

This script is used to analyze tree canopy and its change from 2006 to 2011 in Washington, D.C. with in American Community Survey (ACS) boundaries. The script will automatically read a small *.csv file (52kb) into memory and analyze in R. To download the file directly use the first link below. Rows correspond to block groups, data types using the R nomenclature shown in parentheses, the fields (columns) are: [1] "OBJECTID" - created by ArcGIS, unique (integer) [2] "AREAKEY" - the US Census Bureau FIPS code, the unique identifier for joining to other ACS/Census data (factor) [3] "EHHMEDINC" - Median Household Income in $'s (integer) [4] "Shape_Leng" - The length of the perimeter of the block group in meters (num) [5] "Shape_Area" - The area of the block group polygon in square meters (num) [6] "PctCanArea" - The percent of the block group that is covered by the sum of tree canopy datasets three categories 1) no change, 2) loss, and 3) gain. No change indicates that the tree canopy has not changed substantially from 2006 to 2011. Loss indicates that tree canopy was removed from 2006 to 2011. Gain indicates that new tree canopy was established between 2006 and 2011. The canopy data are described using the Letters from the SAL link provided below (num) [7] "PctNo_Chan" - The proportion of "PctCanArea" that is from the no change class (num) [8] "PctLoss" - The proportion of "PctCanArea" that is from the loss class (num) [9] "PctGain"- The proportion of "PctCanArea" that is from the gain class (num) [10] "IncomeQuan" - The median household income from "EHHMEDINC" categorized into quintiles (factor)

The FHA insured Multifamily Housing portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. Please note that this dataset overlaps the Multifamily Properties Assisted layer. The Multifamily property locations represent the approximate location of the property. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about HUD Insured Multifamily Properties visit: https://www.hud.gov/program_offices/housing/mfh Data Dictionary: DD_HUD Insured Multifamilly Properties Date of Coverage: 02/2025

HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 12/2023

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 of occupied housing units with no vehicle available 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

OccHU_e

# Occupied housing units, 2017

OccHU_m

# Occupied housing units, 2017 (MOE)

VehicAvail0_e

# Occupied housing units, no vehicles available, 2017

VehicAvail0_m

# Occupied housing units, no vehicles available, 2017 (MOE)

pVehicAvail0_e

% Occupied housing units, no vehicles available, 2017

pVehicAvail0_m

% Occupied housing units, no vehicles available, 2017 (MOE)

VehicAvail1_e

# Occupied housing units, 1 vehicle available, 2017

VehicAvail1_m

# Occupied housing units, 1 vehicle available, 2017 (MOE)

pVehicAvail1_e

% Occupied housing units, 1 vehicle available, 2017

pVehicAvail1_m

% Occupied housing units, 1 vehicle available, 2017 (MOE)

VehicAvail2_e

# Occupied housing units, 2 vehicles available, 2017

VehicAvail2_m

# Occupied housing units, 2 vehicles available, 2017 (MOE)

pVehicAvail2_e

% Occupied housing units, 2 vehicles available, 2017

pVehicAvail2_m

% Occupied housing units, 2 vehicles available, 2017 (MOE)

VehicAvail3P_e

# Occupied housing units, 3 or more vehicles available, 2017

VehicAvail3P_m

# Occupied housing units, 3 or more vehicles available, 2017 (MOE)

pVehicAvail3P_e

% Occupied housing units, 3 or more vehicles available, 2017

pVehicAvail3P_m

% Occupied housing units, 3 or more vehicles available, 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.