Mean and standard deviation for demographic attributes of the 200 Metropolitan Statistical Areas (MSAs) considered in our study.Statistics of metropolitan statistical areas.

U.S. Population Grids (Summary File 1), 2000: New Orleans Metropolitan Statistical Area, Alpha Version contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). To provide gridded demographic data, including characteristics of age, race, ethnicity, and housing, for metropolitan statistical areas at a finer resolution than is available in the 30 arc-second grids used for the United States as a whole.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Owner Characteristics of Nonemployer Firms by Industry, Sex, Ethnicity, Race, and Veteran Status for the U.S., States, Metro Areas, and Counties: 2023.Table ID.ABSNESDO2023.AB2300NESD04.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Nonemployer Statistics by Demographics Characteristics of Business Owners.Source.U.S. Census Bureau, 2023 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-11-20.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Table Universe.Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys..Methodology.Data Items and Other Identifying Records.Number of owners of nonemployer firmsPercent of number of owners of nonemployer firms (%)These data are aggregated at the owner level for up to four persons owning the largest percentages of the business by sex, ethnicity, race, and veteran status.Using administrative records, owner characteristics were assigned for the following categories: Place of Birth (USBORN) Owner was born in the U.S. Owner was born outside the U.S. U.S. Citizenship (USCITIZEN) Owner is a citizen of the U.S. Owner is not a citizen of the U.S. Owner Age (OWNRAGE) Under 25 25 to 34 35 to 44 45 to 54 55 to 64 65 or over Question Description codes for the topics are in parenthesis.Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D are nonemployer companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2023 data are shown for the total of all sectors (00) and the 2- to 4-digit NAICS code levels for:United StatesIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:States and the District of ColumbiaThe remaining geographies are available at the total of all sectors (00):Metropolitan Statistical AreasMicropolitan Statistical AreasCountiesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 4-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes various administrative records (AR) and the Census Bureau data sources that include the Business Register (BR), Internal Revenue Service (IRS) tax Form 1040 data, tax Schedule K-1 data, Decennial Census and American Community Survey (ACS) data, Social Security Administration's database (Numident), and AR from the Department of Veterans Affairs (VA).For more information, see Nonemployer Statistics by Demographics Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7504866, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0412).For the nonemployer data, the NES-D uses noise infusion as the primary method of disclosure avoidance for receipts, and In certain circumstances, some individual cells may be suppressed for additional disclosure avoidance. More information on nonemployer firm disclosure avoidance is available in the Nonemployer Statistics by Demographics Methodology..Technical Documenta...

The U.S. Population Grids (Summary File 1), 2000: Houston Metropolitan Statistical Area, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the 2000 census. The grids have a resolution of 7.5 arc-seconds (0.002075 decimal degrees), or approximately 250 square meters. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (population, households, and housing variables) from Summary File 1. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). To provide gridded demographic data, including characteristics of age, race, ethnicity, and housing, for metropolitan statistical areas at a finer resolution than is available in the 30 arc-second grids used for the United States as a whole.

This data collection provides information on characteristics of housing units in 11 selected Metropolitan Statistical Areas (MSAs) of the United States. Although the unit of analysis is the housing unit rather than its occupants, the survey also is a comprehensive source of information on the demographic characteristics of household residents. Data collected include general housing characteristics such as the year the structure was built, type and number of living quarters, occupancy status, presence of commercial or medical establishments on the property, and property value. Data are also provided on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air-conditioning equipment. Questions about housing quality include condition of walls and floors, adequacy of heat in winter, information on heating equipment breakdowns, availability of electrical outlets in rooms, concealed wiring, basement and roof water leakage, and exterminator service for mice and rats. Data related to housing expenses include mortgage or rent payments, utility costs, fuel costs, property insurance costs, real estate taxes, and garbage collection fees. Questions are also asked about neighborhood conditions such as quality of roads, and presence of crime, trash, litter, street noise, abandoned structures, commercial activity, and odors or smoke. Other items cover the adequacy of services such as public transportation, schools, shopping facilities, police protection, recreation facilities, and hospitals or clinics. In addition to housing characteristics, data on age, sex, race, marital status, income, and relationship to householder are provided for each household member. Additional data are supplied for the householder, including years of school completed, Spanish origin, and length of residence. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06188.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This dataset contains metropolitan statistical area (MSA) level information for licensed child care facilities in the State of Michigan. A count of programs, type of programs, and capacity per program is included in the dataset. Program point data was obtained from Great Start to Quality and aggregated to metropolitan statistical area level by Data Driven Detroit in September 2018.

This data set provides statistics about employer and nonemployer businesses from 2021 for the nation, states, counties, and metropolitan statistical areas (MSA). It includes the number of firms, revenue, number of employees, and annual payroll, broken down by industry and owner demographics including as sex, ethnicity, race, and veteran status.About NES-DThe Nonemployer Statistics by Demographics series (NES-D) provides information on the demographic characteristics of nonemployer businesses. The NES-D is the result of a research project by the Census Bureau to complete the picture of U.S. business ownership by demographics for the United States. Historically, the quinquennial Survey of Business Owners (SBO) provided the only comprehensive source of information on both employer and nonemployer businesses by demographic characteristics of the business owners. In 2017, the SBO was replaced by the Annual Business Survey (ABS). The ABS is an annual survey that collects demographic characteristics from employer businesses. However, the ABS excludes the collection of demographic data from nonemployer businesses. The NES-D was developed to produce similar estimates as ABS on owner demographics for nonemployer businesses. The NES-D is not a survey; rather, it leverages existing individual-level administrative records to assign demographic characteristics to the universe of nonemployer businesses. Demographic characteristics including sex, ethnicity, race, veteran status, owner age, place of birth, and U.S. citizenship are assigned to nonemployer business owners.Together, the NES-D and the ABS will continue to provide the only source of detailed and comprehensive statistics on the scope, nature and activities of all U.S. businesses by the demographic characteristics of the business owners. NES-D data will be available annually by detailed geography and industry levels, receipt-size class, and legal form of organization (LFO). Beginning with the 2019 NES-D, the data will include urban and rural classification.

This data set provides statistics about employer and nonemployer businesses from 2020 for the nation, states, and metropolitan statistical areas (MSA). It includes the number of firms, revenue, number of employees, and annual payroll, broken down by industry and owner demographics including as sex, ethnicity, race, and veteran status.About NES-DThe Nonemployer Statistics by Demographics series (NES-D) provides information on the demographic characteristics of nonemployer businesses. The NES-D is the result of a research project by the Census Bureau to complete the picture of U.S. business ownership by demographics for the United States. Historically, the quinquennial Survey of Business Owners (SBO) provided the only comprehensive source of information on both employer and nonemployer businesses by demographic characteristics of the business owners. In 2017, the SBO was replaced by the Annual Business Survey (ABS). The ABS is an annual survey that collects demographic characteristics from employer businesses. However, the ABS excludes the collection of demographic data from nonemployer businesses. The NES-D was developed to produce similar estimates as ABS on owner demographics for nonemployer businesses. The NES-D is not a survey; rather, it leverages existing individual-level administrative records to assign demographic characteristics to the universe of nonemployer businesses. Demographic characteristics including sex, ethnicity, race, veteran status, owner age, place of birth, and U.S. citizenship are assigned to nonemployer business owners.Together, the NES-D and the ABS will continue to provide the only source of detailed and comprehensive statistics on the scope, nature and activities of all U.S. businesses by the demographic characteristics of the business owners. NES-D data will be available annually by detailed geography and industry levels, receipt-size class, and legal form of organization (LFO). Beginning with the 2019 NES-D, the data will include urban and rural classification.

This data collection provides information on characteristics of housing units in eight selected Metropolitan Statistical Areas (MSAs) of the United States. Although the unit of analysis is the housing unit rather than its occupants, the survey also is a comprehensive source of information on the demographic characteristics of household residents. Data collected include general housing characteristics such as the year the structure was built, type and number of living quarters, occupancy status, presence of commercial or medical establishments on the property, and property value. Data are also provided on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air-conditioning equipment. Questions about housing quality include condition of walls and floors, adequacy of heat in winter, information on heating equipment breakdowns, availability of electrical outlets in rooms, concealed wiring, basement and roof water leakage, and exterminator service for mice and rats. Data related to housing expenses include mortgage or rent payments, utility costs, fuel costs, property insurance costs, real estate taxes, and garbage collection fees. Questions are also asked about neighborhood conditions such as quality of roads, and presence of crime, trash, litter, street noise, abandoned structures, commercial activity, and odors or smoke. Other items cover the adequacy of services such as public transportation, schools, shopping facilities, police protection, recreation facilities, and hospitals or clinics. In addition to housing characteristics, data on age, sex, race, marital status, income, and relationship to householder are provided for each household member. Additional data are supplied for the householder, including years of school completed, Spanish origin, and length of residence. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06464.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing.

Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Keith A. Burghardt, USC Information Sciences Institute, USA

Codebook: these CBSA statistics are stratified by degree of aggregation. - CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015 - CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015 - CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. - Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015 - Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.

The statistics are the following:

msaid: CBSA code id: (if patch statistics) arbitrary int unique to each patch within the CBSA that year year: year of statistics pop: population within all CBSA counties patch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA) patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA) patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA) all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patches all_bupl: Same as above but for all data in 2015 regardless of whether properties were in patches all_bua: Same as above but for all data in 2015 regardless of whether properties were in patches num_nodes: number of nodes (intersections) num_edges: number of edges (roads between intersections) distance: total road length in km k_mean: mean number of undirected roads per intersection k1: fraction of nodes with degree 1 k4plus: fraction of nodes with degree 4+ bearing: histogram of different bearings between intersections entropy: entropy of bearing histogram mean_local_gridness: Griddedness used in text mean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles)

Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems 95: 101803.
doi: 10.1016/j.compenvurbsys.2022.101803

Key Table Information.Table Title.Geographical Mobility in the Past Year for Current Residence--Metropolitan Statistical Area Level in the United States.Table ID.ACSDT1Y2024.B07201.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimat...

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Owner Characteristics of Nonemployer Firms by Industry, Sex, Ethnicity, Race, and Veteran Status for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESDO2022.AB2200NESD04.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Characteristics of Business Owners.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Table Universe.Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys..Methodology.Data Items and Other Identifying Records.Number of owners of nonemployer firmsPercent of number of owners of nonemployer firms (%)These data are aggregated at the owner level for up to four persons owning the largest percentages of the business by sex, ethnicity, race, and veteran status.Using administrative records, owner characteristics were assigned for the following categories: Place of Birth (USBORN) Owner was born in the U.S. Owner was born outside the U.S. U.S. Citizenship (USCITIZEN) Owner is a citizen of the U.S. Owner is not a citizen of the U.S. Owner Age (OWNRAGE) Under 25 25 to 34 35 to 44 45 to 54 55 to 64 65 or over Question Description codes for the topics are in parenthesis.Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D are nonemployer companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 4-digit NAICS code levels for:United StatesIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:States and the District of ColumbiaThe remaining geographies are available at the total of all sectors (00):Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 4-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sampling.NES-D nonemployer data are not conducted through sampling. Nonemployer Statistics (NES) data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The NES-D adds demographic characteristics to the NES data and produces the total firm counts and the total receipts by those demographic characteristics. The NES-D utilizes various administrative records (AR) and the Census Bureau data sources that include the Business Register (BR), Internal Revenue Service (IRS) tax Form 1040 data, tax Schedule K-1 data, Decennial Census and American Community Survey (ACS) data, Social Security Administration's database (Numident), and AR from the Department of Veterans Affairs (VA).For more information, see Nonemployer Statistics by Demographics Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7504866, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0195).For the nonemployer data, the NES-D uses noise infusion as the primary method of disclosure avoidance for receipts, and In certain circumstances, some individual cells may be suppressed for additional disclosure avoidance. More information on nonemployer firm disclosure avoidance is available in the ...

The RAND Center for Population Health and Health Disparities (CPHHD) Data Core Series is composed of a wide selection of analytical measures, encompassing a variety of domains, all derived from a number of disparate data sources. The CPHHD Data Core's central focus is on geographic measures for census tracts, counties, and Metropolitan Statistical Areas (MSAs) from two distinct geo-reference points, 1990 and 2000. The current study, Decennial Census Abridged, has two cross-sectional datasets, one longitudinal (interpolated) dataset, and one longitudinal (extrapolated) dataset containing a large number and variety of population and housing characteristics-related measures. These data are summarized at five different geographic levels: tract, county (FIPS), county (Geographic), MSA (Geographic), and state. The following types of measures constructed from the Census Bureau Population and Housing Characteristics data are included in the data for this collection: housing characteristics (stock, quality, ownership, costs, expenditures, occupancy, etc.), crowding (housing and population density), urbanicity, racial and ethnic composition, language, nationality, and citizenship. Further measures cover family/household structure, transportation, educational attainment, labor force, employment status, disabilities, income, poverty, and demographics (e.g., age, gender, and race).

This dataset contains ACS 1y estimates for selected social characteristics of the population. Topics include education, marital status, relationships, fertility, etc. The data are available at a range of geographic levels in Washington and within the 4-county PSRC region (King, Kitsap, Pierce, and Snohomish Counties): state, county, MSA (metropolitan statistical area), and city with population of 65,000+. For more information, please visit the ACS Design and Methodology Report (https://www2.census.gov/programs-surveys/acs/methodology/design_and_methodology/acs_design_methodology_report_2014.pdf)ACS Handbook for Data Users: https://www.census.gov/programs-surveys/acs/guidance/handbooks.html ACS Technical Documentation: https://www.census.gov/programs-surveys/acs/technical-documentation.html

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)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 2017-2021). 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: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

United States Urban Areas Dataset

Spatial and attribute data for US urban areas

By Homeland Infrastructure Foundation [source]

About this dataset

The Urban Areas dataset provides comprehensive spatial and attribute data on urban areas in the United States. These urban areas represent densely developed territories consisting of residential, commercial, and other nonresidential land uses. The dataset includes geospatial information such as longitude and latitude coordinates, area measurements (land and water), shape length, and shape area for each urban area.

Each urban area is identified by a unique 5-character numeric census code, which distinguishes between two types of urban areas: urbanized areas (UAs) with populations of 50,000 or more people, and urban clusters (UCs) with populations ranging between 2,500 to 50,000 people (except in the U.S. Virgin Islands and Guam where some UCs have populations greater than 50,000).

Other important attributes provided include the functional status code indicating the functional classification of the urban area along with its name description. The land area measurement gives insight into the extent of developed territory in square meters for each urban area.

Furthermore, this dataset contains MAF/TIGER feature class codes that provide additional information about specific features within an urban area. These codes help in identifying various characteristics or components within an urban footprint.

By utilizing this dataset researchers can analyze different aspects related to population density patterns across various urbanscapes within the United States. This includes studying demographic trends as well as exploring correlations between land usage patterns - whether residential or commercial - in relation to geographical location.

Overall, this dataset serves as a valuable resource for conducting detailed spatial analysis on a wide range of topics related to population distribution and development across diverse metropolitan areas throughout the United States

How to use the dataset

1. Understanding the Urban Area Types

The dataset categorizes urban areas into two types: Urbanized Areas (UAs) and Urban Clusters (UCs). UAs are densely developed territories with populations of 50,000 or more people. UCs are also densely developed territories but have populations ranging from at least 2,500 people to fewer than 50,000 people.

2. Familiarize Yourself with Key Attributes

The dataset includes various attributes that provide valuable information about each urban area:

• NAME10: The name of the urban area.
• NAMELSAD10: The legal/statistical area description of the urban area.
• UACE10: A unique 5-character numeric census code that identifies each urban area.
• FUNCSTAT10: The functional status code for the urban area.
• ALAND10: The land area of the urban area in square meters.
• AWATER10: The water area of the urbareaan in square meters.
• INTPTLAT10: The latitude coordinate of an interior point within the geographic extent of an individual block group building footprint.(Numeric) -**INTPTLON150 longitude coordinate of an interior point within markset tabulation block group или Tract_LP tificatlpublic Land Survey System™ (PLSS)-based apices location

Understanding these attributes will allow you to gain insights into each specific type.

3. Analyzing Land and Water Area

By focusing on ALAND10 and AWATER10, you can explore the land and water areas of each urban area. This information could be valuable for understanding urban sprawl, planning infrastructure projects, or conducting environmental studies.

4. Exploring Functional Status

FUNCSTAT10 provides information about the functional status of an urban area. This attribute can help you identify whether an area serves as a single principal city or represents part of a larger metropolitan region.

5. Utilizing the Geographic Coordinates

INTPTLAT10 and INTPTLON10 provide latitude and longitude coordinates for each urban area's interior point. You can leverage this data to plot locations on maps

Research Ideas

• Urban Planning Analysis: This dataset can be used for urban planning analysis, such as identifying the land area and water area of different urban areas. It can help city officials and planners understand the spatial distribution of urban areas, assess population density, and make informed decisions regarding infrastructure development and resource allocation.
• Market Research: The dataset can be utilized for market research purposes by identifying different types of urban areas (UAs or UCs) based on their po...

Urban lakes are heavily impacted by human activities and climate variability, and they provide many ecosystem services to residents. The MSP LTER program is studying long term changes in urban lake water quality, ecology and management as part of our long term studies of urban environments. The goal of this dataset is to understand how land-use change, management, and climate have impacted urban lake biogeochemistry over time. This dataset includes parameters characterizing the long term (> 5 years) surface water quality and chemistry of 294 lakes and ponds in the Minneapolis-Saint Paul Seven County Metropolitan Area, Minnesota, USA. The dataset draws from data publicly available through the Minnesota Pollution Control Agency and data provided by individual agencies, park districts and cities. The dataset is distinct from other lake datasets because it is curated to only report a single value per lake x date x parameter, minimizing the amount of data manipulation needed before use in statistical analyses. All data come from the top two meters of the water column. In the case of multiple spatial measurements on a single lake or multiple agencies sampling the same lake on the same day, chemistry data were averaged to generate a single value. For Secchi data, the deepest reported observation on a given lake x date was used. Parameters: total phosphorus, total nitrogen, total Kjeldahl nitrogen, nitrate, nitrite, nitrate + nitrite (NOx), ammonium, chlorophyll a (corrected and not corrected for pheophytin), specific conductivity, chloride, and Secchi depth. These waterbodies are identified by their DNR Division of Water (DOW) number with minor alterations for subbasin identification. This dataset does not comprehensively represent all lentic waterbodies that have substantial water quality data in the metro area, and some included waterbodies may be considered wetlands according to state classifications. The data brought together in this database has undergone QAQC by the organizations that originally collected it, as well as a screening process during data harmonization. While we believe that the resulting dataset is robust, we cannot guarantee that it is free of errors or inaccuracies.

This is the Boston Housing Dataset, copied from: https://www.kaggle.com/datasets/vikrishnan/boston-house-prices

Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. The attributes are defined as follows (taken from the UCI Machine Learning Repository1): CRIM: per capita crime rate by town

CRIM per capita crime rate by town ZN proportion of residential land zoned for lots over 25,000 sq.ft. INDUS proportion of non-retail business acres per town CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) NOX nitric oxides concentration (parts per 10 million) RM average number of rooms per dwelling AGE proportion of owner-occupied units built prior to 1940 DIS weighted distances to five Boston employment centres RAD index of accessibility to radial highways TAX full-value property-tax rate per 10 000 USD PTRATIO pupil-teacher ratio by town B 1000 (Bk - 0.63)^2 where Bk is the proportion of black people by town LSTAT % lower status of the population MEDV Median value of owner-occupied homes in $1000's Missing values: None

Duplicate entries: None

This is a copy of UCI ML housing dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/

It has then been amended to include multiple different correlations:

Directly Derived Features - New features created by applying direct transformations to existing features. For example a scaled version of another (e.g., CRIM_dup_2 = CRIM * 2), or adding some noise to an existing feature (e.g., RM_noisy = RM + random_noise).

Linear Combinations - Combining existing features linearly. For instance, a feature that is a weighted sum of several other features (e.g., weighted_feature = 0.5 * CRIM + 0.3 * NOX + 0.2 * RM).

Polynomial Features - Creating polynomial transformations of existing features. For example, square or cube a feature (e.g., AGE_squared = AGE^2). These will have a predictable correlation with their original feature.

Interaction Terms - Generating features that are the product of two existing features. Revealing interactions between variables (e.g., TAX_RAD_interaction = TAX * RAD).

Duplicate Features with Variations: Duplicate some existing features and add small variations. For example, copy a feature and add a random small value to each entry (e.g., LSTAT_varied = LSTAT + small_random_value).

These have been done by taking the dataset in python and transforming it, for example:

``import pandas as pd import random import numpy as np

List of original column names

original_columns = ["CRIM", "ZN", "INDUS", "CHAS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "B", "LSTAT"]

Generate features

for col_name in original_columns: # Linear Combinations other_cols = random.sample([c for c in original_columns if c != col_name], 2) df[f"{col_name}_linear_combo"] = 0.5 * df[col_name] + 0.3 * df[other_cols[0]] + 0.2 * df[other_cols[1]]

# Polynomial Features
df[f"{col_name}_squared"] = df[col_name] ** 2

# Interaction Terms
other_col = random.choice([c for c in original_columns if c != col_name])
df[f"{col_name}_{other_col}_interaction"] = df[col_name] * df[other_col]

# Duplicate Features with Variations
df[f"{col_name}_varied"] = df[col_name] + (np.random.rand(df.shape[0]) * 0.05)

Display the DataFrame

print(df) ``

This data collection contains information from samples of housing units in 11 Metropolitan Statistical Areas (MSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial or medical establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air-conditioning equipment. Questions concerning quality of housing include condition of walls and floors, adequacy of heat in winter, availability of electrical outlets, basement and roof water leakage, and exterminator service for mice or rats. Data on housing expenses include amount of mortgage or rent payments and costs of utilities, fuel, garbage collection, property insurance, and real estate taxes. Respondents who had moved recently were questioned about characteristics of the previous residence and reasons for moving. Residents were also asked to evaluate the quality of their neighborhoods with respect to issues such as crime, street noise, quality of roads, commercial activities, presence of trash, litter, abandoned structures, or offensive odors, and adequacy of services such as police protection, shopping facilities, and schools. In addition to housing characteristics, some demographic information is provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data are available on the householder, including years of school completed, Spanish origin, and length of residence. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09853.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.