This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

Federal Financial Institutions Examination Council (FFIEC) Census Reports This dataset represents the 2020 FFIEC Census Reports for Population, Income and Housing Units in Jersey City (based on Census Tracts)For field definitions, please visit - Census Info Sheet

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

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

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

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

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

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

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

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

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

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

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

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

Big Three: San Jose, San Francisco, Oakland

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

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

Unincorporated: all unincorporated towns

Each of the State of Maryland’s 1,406 2010 census tracts was analyzed to determine whether it represented a typical census tract as defined by the U. S. Bureau of the Census. Nationally these are census tracts that optimally are 4,000 inhabitants but generally range from 1,200 to 8,000 persons. In Maryland the average census tract contains 4,106 persons. Nationally the housing unit threshold for each census tract generally ranges from 480 to 3,200 housing units, with an optimum size of 1,600 housing units. In Maryland the average census tract contains 1,692 housing units. The Emergency Management Planning Database and the Emergency Planning Vulnerable Population Index are intended to assist State agency emergency officials plan tactics, develop strategies, allocate resources and prioritize responses for emergencies and to identify potentially vulnerable population areas for special attention. Statewide, there are 222 census tracts containing persons at “Very High” socio – economic risk or vulnerability in the event of an emergency. “Very High” risk census tracts account for 16 – percent of the State’s 1,390 specified census tracts. These census tracts are located throughout the State in 20 of 24 jurisdictions. There are 773,808 persons living in these areas making up 13.4 percent of the State’s 2010 Census population of 5,773,552 persons.

VITAL SIGNS INDICATOR Commute Time (T4)

FULL MEASURE NAME Commute time by employment location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Table B08536 (2018 only; by place of employment) Table B08601 (2018 only; by place of employment) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

Census tracts are designated as urban, rural center, or rural through SB 1000 analysis. These designations are being used for the REV 2.0 and Community Charging in Urban Areas GFOs. Rural centers are contiguous urban census tracts with a population of less than 50,0000. Urban census tracts are tracts where at least 10 percent of the tract’s land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria. Rural communities are census tracts where less than 10 percent of the tract’s land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria. Urban communities are contiguous urban census tracts with a population of 50,000 or greater. Urban census tracts are tracts where at least 10 percent of the tract’s land area is designated as urban by the Census Bureau using the 2020 urbanized area criteria.Data Dictionary:OBJECTID: Unique IDSTATEFP: State FIPS CodeCOUNTYFP: County FIPS CodeTRACTCE: Census Tract IDGEOID: Geographic IdentifierName: Census Tract ID Name (short)NAMELSAD: Census Tract ID Name (long)ALAND: Land Area (square meters)AWATER: Water Area (square meters)DAC: Whether or not a census tract is a disadvantaged community as defined by SB 535 and designated by CalEPA using CalEnviroScreen 4.0 (May 2022 update)Income_Group: Whether or not a census tract is low-, middle-, or high-income as defined by AB 1550 and designated by CARB and the CEC (June 2023 update)Urban_Rural_RuralCenter: Whether or not a census tract is urban, rural, or rural center as defined and designated by the CEC through the SB 1000 Assessment (2024 update)PerCap_100k_L2DCFC: Number of public Level 2 and DC fast chargers per 100,000 people in a census tractDAC_andor_LIC: Whether or not a census tract is a disadvantaged or low-income community as defined by SB 535 and AB 1550 and designated by CalEPA and CARBUCC_eligible: Whether or not the census tract is an eligible area for the Community Charging in Urban Areas GFO. For a site to be eligible, it must be in a census tract that is either a disadvantaged or low-income community, and urban, and has below the state average for per capita public Level 2 and DC fast chargers as defined by the CEC.REV2_eligible: Whether or not the census tract is an eligible area for the Rural Electric Vehicle Charging 2.0 GFO. For a site to be eligible, it must be in a rural or rural center census tract as defined by the CEC.Shape_Area: Census tract shape area (square meters)Shape_Length: Census tract shape length (square meters)

Census tracts are created by the U.S. Census Bureau to be small, relatively permanent statistical subdivisions of a county. Census tracts average about 4,000 inhabitants: minimum population –1,200 and maximum population –8,000. Census tracts are split or merged every 10 years, depending on population change, with local feedback through the Participant Statistical Areas Program (PSAP).

VITAL SIGNS INDICATOR
Commute Time (T3)

FULL MEASURE NAME
Commute time by residential location

LAST UPDATED
January 2023

DESCRIPTION
Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE
U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2006-2021
Form C08136
Form C08536
Form B08301
Form B08301
Form B08301

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey (ACS) datasets, 1-year rolling average data was used for all metros, region and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute times were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography.

Census tract data is not available for tracts with insufficient numbers of residents. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary metropolitan statistical areas (MSAs) for the nine other major metropolitan areas.

VITAL SIGNS INDICATOR Displacement Risk (EQ3)

FULL MEASURE NAME Share of lower-income households living in tracts at risk of displacement

LAST UPDATED December 2018

DESCRIPTION Displacement risk refers to the share of lower-income households living in neighborhoods that have been losing lower-income residents over time, thus earning the designation “at risk”. While “at risk” households may not necessarily be displaced in the short-term or long-term, neighborhoods identified as being “at risk” signify pressure as reflected by the decline in lower-income households (who are presumed to relocate to other more affordable communities). The dataset includes metropolitan area, regional, county and census tract tables.

DATA SOURCE U.S. Census Bureau: Decennial Census 1980-1990 Form STF3 https://nhgis.org

U.S. Census Bureau: Decennial Census 2000 Form SF3a https://nhgis.org

U.S. Census Bureau: Decennial Census 1980-2010 Longitudinal Tract Database http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 2010-2015 Form S1901 5-year rolling average http://factfinder2.census.gov

U.S. Census Bureau: American Community Survey 2010-2017 Form B19013 5-year rolling average http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Aligning with the approach used for Plan Bay Area 2040, displacement risk is calculated by comparing the analysis year with the most recent year prior to identify census tracts that are losing lower-income households. Historical data is pulled from U.S. Census datasets and aligned with today’s census tract boundaries using crosswalk tables provided by LTDB. Tract data, as well as regional income data, are calculated using 5-year rolling averages for consistency – given that tract data is only available on a 5-year basis. Using household tables by income level, the number of households in each tract falling below the median are summed, which involves summing all brackets below the regional median and then summing a fractional share of the bracket that includes the regional median (assuming a simple linear distribution within that bracket).

Once all tracts in a given county or metro area are synced to today’s boundaries, the analysis identifies census tracts of greater than 500 lower-income people (in the prior year) to filter out low-population areas. For those tracts, any net loss between the prior year and the analysis year results in that tract being flagged as being at risk of displacement, and all lower-income households in that tract are flagged. To calculate the share of households at risk, the number of lower-income households living in flagged tracts are summed and divided by the total number of lower-income households living in the larger geography (county or metro). Minor deviations on a year-to-year basis should be taken in context, given that data on the tract level often fluctuates and has a significant margin of error; changes on the county and regional level are more appropriate to consider on an annual basis instead.

A. SUMMARY Census tracts boundaries in San Francisco county. Census tracts are small, relatively permanent statistical subdivisions of a county. They are uniquely numbered in each county with a numeric code. Census tracts average about 4,000 inhabitants ranging from 1,200 – 8,000. 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 tract boundaries are tracked in multiple datasets. See here for 2000 and 2010 census tract boundaries. D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. E. RELATED DATASET 2020 Census Tracts and Analysis Neighborhoods

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset contains urban and rural LST, DEM, and NDVI data for annual, summer, and winter daytime and nighttime for all census tracts in US urbanized areas, as well as the mean values for the entire urbanized area.

METADATA DEM: Digital Elevation Model NDVI: Normalized Difference Vegetation Index LST: Land Surface Temperature
_urb: Urban values (all urban pixels within urbanized areas) _rur: Rural reference (Spatial mean of the non-urban, non-water pixels within the region of interest) Regions of Interest: _CT: Spatial mean of pixels intersecting the Census Tract clipped to the urbanized area (one value per census tract). This should be equal to the _CT for census tracts that are completely within the urbanized areas (the census tracts with the green dots in the image below) _all: Spatial mean of all pixels intersecting the urbanized area, as defined by the US census (one value for one urbanized area) _CT_act: Spatial mean of all available pixels intersecting the Census Tract (one value per census tract) [This should be equal to the previous values I calculated]

For the UHI: The ideal configuration is LST_urb_all-LST_rur for the entire urbanized area and LST_urb_CT_act-LST_rur for individual census tracts within the urbanized areas

For the equity analysis: Either _CT or CT_act can be used if we are only concerned with spatial variation. Using CT_act leads to mismatch between census data for the tracts crossing the urban boundary and the remotely sensed data. Using _CT leads to mismatch between the UHI analysis and the equity analysis.

VITAL SIGNS INDICATOR Displacement Risk (EQ3)

FULL MEASURE NAME Share of lower-income households living in tracts at risk of displacement

LAST UPDATED December 2018

DESCRIPTION Displacement risk refers to the share of lower-income households living in neighborhoods that have been losing lower-income residents over time, thus earning the designation “at risk”. While “at risk” households may not necessarily be displaced in the short-term or long-term, neighborhoods identified as being “at risk” signify pressure as reflected by the decline in lower-income households (who are presumed to relocate to other more affordable communities). The dataset includes metropolitan area, regional, county and census tract tables.

DATA SOURCE U.S. Census Bureau: Decennial Census 1980-1990 Form STF3 https://nhgis.org

U.S. Census Bureau: Decennial Census 2000 Form SF3a https://nhgis.org

U.S. Census Bureau: Decennial Census 1980-2010 Longitudinal Tract Database http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 2010-2015 Form S1901 5-year rolling average http://factfinder2.census.gov

U.S. Census Bureau: American Community Survey 2010-2017 Form B19013 5-year rolling average http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Aligning with the approach used for Plan Bay Area 2040, displacement risk is calculated by comparing the analysis year with the most recent year prior to identify census tracts that are losing lower-income households. Historical data is pulled from U.S. Census datasets and aligned with today’s census tract boundaries using crosswalk tables provided by LTDB. Tract data, as well as regional income data, are calculated using 5-year rolling averages for consistency – given that tract data is only available on a 5-year basis. Using household tables by income level, the number of households in each tract falling below the median are summed, which involves summing all brackets below the regional median and then summing a fractional share of the bracket that includes the regional median (assuming a simple linear distribution within that bracket).

Once all tracts in a given county or metro area are synced to today’s boundaries, the analysis identifies census tracts of greater than 500 lower-income people (in the prior year) to filter out low-population areas. For those tracts, any net loss between the prior year and the analysis year results in that tract being flagged as being at risk of displacement, and all lower-income households in that tract are flagged. To calculate the share of households at risk, the number of lower-income households living in flagged tracts are summed and divided by the total number of lower-income households living in the larger geography (county or metro). Minor deviations on a year-to-year basis should be taken in context, given that data on the tract level often fluctuates and has a significant margin of error; changes on the county and regional level are more appropriate to consider on an annual basis instead.

VITAL SIGNS INDICATOR Commute Time (T3)

FULL MEASURE NAME Commute time by residential location

LAST UPDATED April 2020

DESCRIPTION Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey Form B08013 (2006-2018; place of residence; overall time) Form C08136 (2006-2018; place of residence; time by mode) Form B08301 (2006-2018; place of residence) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey datasets, 1-year rolling average data was used for all metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute time were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography. Census tract data is not available for tracts with insufficient numbers of residents.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

Displacement risk indicator classifying census tracts according to single-family home sale prices. Each census tract where at least 100 single-family homes were sold in arms-length transactions are classified along two dimensions:

1. The average price of sales within the census tract for the specified year, balancing between nominal sale price and sale price per square foot.
2. The change in average sale price (again balanced between nominal sale price and price per square foot) from the previous year.

This dataset provides access to Qualified Census Tracts (QCTs) in Connecticut to assist in administration of American Rescue Plan (ARP) funds. The Secretary of HUD must designate QCTs, which are areas where either 50 percent or more of the households have an income less than 60 percent of the AMGI for such year or have a poverty rate of at least 25 percent. HUD designates QCTs based on new income and poverty data released in the American Community Survey (ACS). Specifically, HUD relies on the most recent three sets of ACS data to ensure that anomalous estimates, due to sampling, do not affect the QCT status of tracts. QCTs are identified for the purpose of Low-Income Housing Credits under IRC Section 42, with the purpose of increasing the availability of low-income rental housing by providing an income tax credit to certain owners of newly constructed or substantially rehabilitated low-income rental housing projects. Also included are the number of households from the 2010 census (the “p0150001” variable), the average poverty rate using the 2014-2018 ACS data (the “pov_rate_18” variable), and the ratio of Tract Average Household Size Adjusted Income Limit to Tract Median Household Income using the 2014-2018 ACS data (the “inc_factor_18” variable). For the last variable mentioned in the previous paragraph, the income limit is the limit for being considered a very low income household (size-adjusted and based on Area Mean Gross Income). This value is divided by the median household income for the given tract, to get a sense of how the limit and median incomes compare. For example, if ratio>1, it implies that the tract is very low income because the limit income is greater than the median income. This ratio is a compact way to include the separate variables for the household income limit and median household income for each tract.

This dataset provides modeled predictions of ozone levels from the EPA's Downscaler model. Data are at the census tract level for 2016-2020. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network.Web. Accessed: insert date. www.cdc.gov/ephtracking.

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2016-2020. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana 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)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data