This data set describes metropolitan areas in the conterminous United States, developed from U.S. Bureau of the Census boundaries of Consolidated Metropolitan Statistical Areas (CMSA) and Metropolitan Statistical Areas (MSA), that have been processed to extract the largest contiguous urban area within each MSA or CMSA.

U.S. Core Based Statistical Areas represents geographic entities, defined by the United States Office of Management and Budget (OMB) for use by Federal statistical agencies, based on the concept of a core area with a large population nucleus, plus adjacent communities having a high degree of social and economic integration with that core. A Core Based Statistical Area (CBSA) consists of a U.S. county or counties or equivalent entities associated with at least one urban core (urbanized area or urban cluster) with a population of at least 10,000 along with any adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. CBSAs are categorized as being either Metropolitan or Micropolitan. Each Metropolitan Statistical Area must have at least one urbanized area of 50,000 or more inhabitants. Each Micropolitan Statistical Area must have at least one urban cluster with a population of at least 10,000 but less than 50,000.Estimated 2018 total population is included along with many demographic attributes from the 2010 Census. CBSA features that are new since the 2010 Census have null demographic attributes values.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The Metropolitan Divisions boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2017.

VITAL SIGNS INDICATOR Daily Miles Traveled (T15)

FULL MEASURE NAME Per-capita vehicle miles traveled

LAST UPDATED July 2017

DESCRIPTION Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for per-capita vehicle miles traveled.

DATA SOURCE Federal Highway Administration: Highway Statistics Series 2015 Table HM-71; limited to urbanized areas https://www.fhwa.dot.gov/policyinformation/statistics.cfm

U.S. Census Bureau: Summary File 1 2010 http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) "Vehicle miles traveled reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examine county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas within each metropolitan area (9-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available outside of other urbanized areas; it is only available for intraregional analysis purposes.

VMT per capita is calculated by dividing VMT by an estimate of the traveling population. The traveling population does not include people living in institutionalized facilities, which are defined by the Census. Because institutionalized population is not estimated each year, the proportion of people living in institutionalized facilities from the 2010 Census was applied to the total population estimates for all years."

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Divisions are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The generalized boundaries in this file are based on those defined by OMB based on the 2010 Census, published in 2013, and updated in 2018.

The TIGER/Line Shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System. The MAF/TIGER System represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line Shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGERweb REST Services allows users to integrate the Census Bureau's Topologically Integrated Geographic Encoding and Referencing database (TIGER) data into their own GIS or custom web-based applications.For a more detailed description of the areas listed or terms below, refer to TIGER/Line documentation or the Geographic Areas Reference Manual, (GARM).This REST service contains Combined New England City and Town Area (CNECTA), Combined Statistical Area (CSA), Metropolitan Division, Core Based Statistical Areas (CBSA), and New England City and Town Area (NECTA) boundaries.Combined New England City and Town Areas (CNECTAs) consist of two or more adjacent NECTAs that have significant employment interchanges. The NECTAs that combine to create a CNECTA retain separate identities within the larger combined statistical areas.

Combined Statistical Areas (CSAs) consist of two or more adjacent CBSAs that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSAs.

Metropolitan Divisions are smaller groupings of counties or equivalent entities within a metropolitan statistical area that contains a single core with 2.5 million inhabitants.

Core Based Statistical Area Codes (CBSA) are the metropolitan statistical areas, micropolitan statistical areas, NECTAs, metropolitan divisions, and NECTA divisions use a 5-character code. Each metropolitan statistical area must have one urbanized area of 50,000 or more inhabitants. Each micropolitan statistical area must have one urban cluster of 10,000 to 49,999 inhabitants.

New England City and Town Area (NECTA) Divisions are smaller groupings of cities and towns within a NECTA that contains a single core with 2.5 million inhabitants. A NECTA Division consists of a main city or town that represents an employment center, as well as adjacent cities and towns associated with the main city or town through commuting ties. Each NECTA Division must contain a total population of 100,000 or more.

Additional resources to obtain Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas are listed below.

Combined New England City and Town Area (CNECTA) Shapefile - https://www2.census.gov/geo/tiger/TIGER2020/CNECTA/

Combined Statistical Area (CSA) Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/CSA/

Metropolitan Division Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/METDIV/

Core Based Statistical Areas (CBSA) Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/CBSA/

New England City and Town Area (NECTA) Shapefile- https://www2.census.gov/geo/tiger/TIGER2020/NECTA/.

2023 Functional Urban Area update For the 2023 FUA, there have been minor updates from the 2018 FUAs to align with changes to urban rural (UR) boundaries and statistical area 1 (SA1) composition. FUA 2023 is still based on the analysis of 2018 Census of Population and Dwellings commuting data. The Wanaka urban area, whose population has grown to be more than 10,000 based on population estimates, has been reclassified to a medium urban area in the 2023 UR and a medium regional centre in the FUA type. Description This dataset is the definitive version of the Functional Urban Area boundaries as at 1 January 2023, as defined by Stats NZ. The functional urban area (FUA) classification identifies small urban areas and rural areas that are integrated with major, large, and medium urban areas to create FUAs. In 2023, there are 53 FUAs,excluding ‘land area outside functional urban area’ (9001) and ‘water area outside functional urban area’ (9002). The FUA classificationuses the urban rural (UR) geography to demarcate urban areas, and statistical area 1 areas(SA1s) to demarcate surrounding hinterland (the commuting zone) within FUAs, and rural and water areas outside FUAs. FUAs represent a populated urban core/s and its commuting zone. Workplace address and usual residence address data from the 2018 Census of Population and Dwellings were used to identify satellite urban areas (1,000–4,999 residents), rural settlements and other rural SA1s from which at least 40 percent of workers commuted to urban areas with more than 5,000 residents. FUA numbering and naming The FUA classification identifies FUAs by the name of the most highly populated urban area it contains, for example, the Christchurch FUA includes the Christchurch urban core and Rangiora, Kaiapoi, and Rolleston secondary urban cores. There is one exception to the naming rule. The Paraparaumu-Waikanae-Paekakariki conurbation and surrounding hinterland is named Kapiti Coast. The FUA classification has a two-level hierarchical structure, joined together to create each FUA code. Level 1 is classified by FUA type (TFUA) a one-digit code and level 2, which has three-digit codes numbered approximately north to south. Some examples are: 1001 Auckland, 2001 Whangārei, 3001 Cambridge, and 4001 Kaitāia. FUA type (TFUA) FUAs are further categorised by population size. The urban core’s population rather than the entire FUA’s population is used to maintain consistency between the descriptions of UR urban area and FUA type. The categories are, by code: 1 Metropolitan area – more than 100,000 residents living in the urban core, 2 Large regional centre – urban core population 30,000–99,999, 3 Medium regional centre – urban core population 10,000–29,999, 4 Small regional centre – urban core population 5,000–9,999, and, 9 Area outside functional urban area. The Greymouth urban area population is less than 10,000 but is classified as a medium regional centre, consistent with its treatment as a medium urban area in the UA classification. To differentiate from the UR classification, when referring to FUAs by name, their FUA type should also be mentioned, for example, Christchurch metropolitan area, Whangarei regional centre. FUA indicator (IFUA) The IFUA classifies UR2023 urban areas and rural SA1s according to their character within their FUA. The indicators, with their codes in brackets, are: • urban area within functional urban area – urban core (101), secondary urban core (102), satellite urban area (103), • rural area within functional urban area – hinterland (201), • area outside functional urban area – land area outside functional urban area (901), water area outside functional urban area (902). ​ Further information on the urban rural indicator is available on the Stats NZ classification tool Ariā. For more information please refer to the Statistical standard for geographic areas 2023. ​ Generalised version This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes. ​ Macrons Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’. ​ Digital data Digital boundary data became freely available on 1 July 2007.

This is a series-level metadata record. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The metropolitan division boundaries are those defined by OMB based on the 2020 Census and published in 2023.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The metropolitan division boundaries are those defined by OMB based on the 2020 Census and published in 2023.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The Metropolitan Divisions for the 2010 Census are those defined by OMB and published in December 2009.

FAF domestic region level datasets and products (NTAD) provide information for states, state portions of large metropolitan areas, and remainders of states. Metropolitan areas consist of Metropolitan Statistical Areas or Consolidated Statistical Areas as defined by the Office of Management and Budget. When a metropolitan area is entirely within a state or when a state's portion of a multi-state metropolitan area is large enough to support the sampling procedures in the Commodity Flow Survey, the area becomes a separate FAF region. Small single-state metropolitan areas and small portions of a multi-state metropolitan area are part of the State or Remainder of State. FAF has two metropolitan areas that are each divided into three FAF regions, four that are each divided into two FAF regions, and several that have small pieces combined with States or Remainders of States.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan Divisions subdivide a Metropolitan Statistical Area containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all Metropolitan Statistical Areas with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties. Because Metropolitan Divisions represent subdivisions of larger Metropolitan Statistical Areas, it is not appropriate to rank or compare Metropolitan Divisions with Metropolitan and Micropolitan Statistical Areas. The Metropolitan Divisions boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2017.

VITAL SIGNS INDICATOR
Displacement Risk (EQ3)

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

LAST UPDATED
January 2023

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 - https://nhgis.org
Form STF3 (1990-2000)

U.S. Census Bureau: American Community Survey (5-year rolling average) - https://data.census.gov/
2009-2021
Form B19001, B19013

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. 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 uniform 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.

This dataset was generated for analyzing the economic impacts of subway networks on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impacts and we focused on the economic impacts realized through housing prices. As a proxy of housing price, we consider the price of condominiums, the dominant housing type in South Korea. Although our focus is transit accessibility and housing prices, the presented dataset is applicable to other studies. In particular, it provides a wide range of variables closely related to housing price, including housing properties, local amenities, local demographic characteristics, and control variables for the seasonality. Many of these variables were scientifically generated by our research team. Various distance variables were constructed in a geographic information system environment based on public data and they are useful not only for exploring environmental impacts on housing prices, but also for other statistical analyses in regard to real estate and social science research. The four metropolitan areas covered by the data—Busan, Daegu, Daejeon, and Gwangju—are independent of the transit systems of Greater Seoul, providing accurate information on the metropolitan structure separate from the capital city.

This table presents the 2021 population counts for census metropolitan areas and census agglomerations, and their population centres and rural areas.

"This dataset contains measurements and iTree output (www.itreetools.org) of woody plant species observed in residential yards and nearby natural areas. Data were collected to assess biotic ecological homogenization in six cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis/St. Paul, MN, and Phoenix, AZ."

U.S. Chronic Disease Indicators (CDI) CDC's Division of Population Health provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data that are important to public health practice and available for states, territories and large metropolitan areas. In addition to providing access to state-specific indicator data, the CDI web site serves as a gateway to additional information and data resources.

Unemployment Rates for Large Metropolitan Areas . Rates shown are a percentage of the labor force. Data refer to place of residence. Estimates for the current month are subject to revision the following month.