The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole. The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context :

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole. Update frequency: Historic (none)

Dataset source

United States Census Bureau

Sample Query

SELECT zipcode, population FROM bigquery-public-data.census_bureau_usa.population_by_zip_2010 WHERE gender = '' ORDER BY population DESC LIMIT 10

Terms of use

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/us-census-data

This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the census data collector code were used as input features in the public supply delivery and water use machine learning models. This page includes the following file: census_data_collector.zip - a zip file containing the census data collector Python code used to retrieve data from the U.S. Census Bureau and a README file.

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

The Census of Agriculture is a complete count of U.S. farms and ranches and the people who operate them. Even small plots of land - whether rural or urban - growing fruit, vegetables or some food animals count if $1,000 or more of such products were raised and sold, or normally would have been sold, during the Census year. The Census of Agriculture, taken only once every five years, looks at land use and ownership, operator characteristics, production practices, income and expenditures. For America's farmers and ranchers, the Census of Agriculture is their voice, their future, and their opportunity. The Census Data Query Tool (CDQT) is a web-based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The data found via the CDQT may also be accessed in the NASS Quick Stats database. The CDQT is unique in that it automatically displays data from the past five Census of Agriculture publications. The CDQT is presented as a "2017 centric" view of the Census of Agriculture data. All data series that are present in the 2017 dataset are available within the CDQT, and any matching data series from prior Census years will also display (back to 1997). If a data series is not included in the 2017 dataset, then data cells will remain blank in the tool. For example, one of the data series had a label change from "Operator" to "Producer." This means that data from prior Census years labelled "Operator" will not show up where the label has changed to “Producer” for 2017. The new Census Data Query Tool application can be used to query Census data from 1997 through 2017. Data are searchable by Census table and are downloadable as CSV or PDF files. 2017 Census Ag Atlas Maps are also available for download. Resources in this dataset:Resource Title: 2017 Census of Agriculture - Census Data Query Tool (CDQT). File Name: Web Page, url: https://www.nass.usda.gov/Quick_Stats/CDQT/chapter/1/table/1 The Census Data Query Tool (CDQT) is a web based tool that is available to access and download table level data from the Census of Agriculture Volume 1 publication. The data found via the CDQT may also be accessed in the NASS Quick Stats database. The CDQT is unique in that it automatically displays data from the past five Census of Agriculture publications. The CDQT is presented as a "2017 centric" view of the Census of Agriculture data. All data series that are present in the 2017 dataset are available within the CDQT, and any matching data series from prior Census years will also display (back to 1997). If a data series is not included in the 2017 dataset, then data cells will remain blank in the tool. For example, one of the data series had a label change from "Operator" to "Producer." This means that data from prior Census years labelled "Operator" will not show up where the label has changed to "Producer" for 2017. Using CDQT:

Upon entering the CDQT, a data table is present. Changing the parameters at the top of the data table will retrieve different combinations of Census Chapter, Table, State, or County (when selecting Chapter 2). For the U.S., Volume 1, US/State Chapter 1 will include only U.S. data; Chapter 2 will include U.S. and State level data. For a State, Volume 1 US/State Level Data Chapter 1 will include only the State level data; Chapter 2 will include the State and county level data. Once a selection is made, press the “Update Grid” button to retrieve the new data table. Comma-separated values (CSV) download, compatible with most spreadsheet and database applications: to download a CSV file of the data as it is currently presented in the data grid, press the "CSV" button in the "Export Data" section of the toolbar. When CSV is chosen, data will be downloaded as numeric. To view the source PDF file for the data table, press the "View PDF" button in the toolbar.

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

This dataset presents statistics on: the number of establishments; sales, value of shipments, or revenue; annual payroll; number of employees; and response coverage of franchise inquiry, by franchise status for selected industries for the U.S. and states (only for sector 72). Includes only establishments of firms with paid employees.

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates. Note: The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

These are full-resolution boundary files, derived from TIGER/Line Shapefiles, the fully-supported, core geographic products from the US Census Bureau. They are extracts of selected geographic and cartographic information from the US Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database. These include information for the 50 states, the District of Columbia, Puerto Rico, and the Island areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the United States Virgin Islands). These include polygon boundaries of geographic and statistical areas, linear features including roads and hydrography, and point features. These files are converted and made available in BigQuery by the Cloud Public Datasets Program to support geospatial analysis through BigQuery GIS . The dataset describes the boundaries of the following US areas: States Counties 115th & 116th US Congressional Districts Metropolitan and Micropolitan Statistical Areas Urban Areas City Limits Combined Statistical Areas Coastline Populated Places National border For more details on each dataset, see the technical documentation published by the Census Bureau. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .</

Release Date: 2021-05-06.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Sales, value of shipments, or revenue of NAPCS products relating to this inquiry ($1,000).Percent of credit financing products income from interest (%).Percent of credit financing products income from fees (%).Percent of credit financing products income from other fees (%).Response coverage of type of credit financing products income inquiry (%)..Each record includes a code which represents a specific type of credit financing products category...Geography Coverage:.The data are shown for employer establishments at the U.S. level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown for selected 6- and 7-digit 2017 NAICS codes. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector52/EC1752CRFIN.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

The cartographic boundary files are simplified representations of selected geographic areas from the Census Bureau’s MAF/TIGER geographic database. These boundary files are specifically designed for small scale thematic mapping.

This feature class has been reprojected to Web Mercator Auxilary Sphere (WKID 3857) for use with this map service.

© US Census Bureau (2010) This layer is a component of US Census Tracts (California).

This cenus tract map services was created for the California Department of Alcoholic Beverage Control, License Query System.

© CSR# 139724

[Metadata] 2010 Census Public Use Microdata Areas (PUMA). Source: US Census Bureau.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/puma10.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for almost all geographic areas for which the Census Bureau tabulates data for both the 2010 Census and Census 2000. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

The intended dual purpose of MassGIS’ efforts in processing this data is to both create a geocoding resource that can supplement the addressing data available in its Master Address Database, and compile a comprehensive set of Census Bureau road features for map display, query, and analysis. Linear road features attributed with street names, address ranges, and other useful information have been generated from a combination of Census Bureau layers. A full list of the various Census 2020 TIGER/Line layers available for download can be found in this summary. See the datalayer metadata for full details.

Feature service also available.

In 2010, the US Census Bureau released data about the US population and demographics by tabulated census blocks. Shape files could be obtained through query by State. The files contained here are the tabulated census blocks for Puerto Rico, using the 2010 released version.

The original file can be found at the US Census bureau 2017 TIGER/LINE shape files. Please refer to US Census Bureau TIGER/Line for the query tool and for any necessary updates to the tabulated block information: https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2017&layergroup=Blocks+%282010%29

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

2010 US Census block boundaries attributed with a subset of the PL94-171 demographic data released March 2011. Please contact the Hennepin GIS Office if you require additional PL94-171 fields. Downloaded the 2010 US Census block boundary shapefile from the US Census Bureau at the following address: http://www.census.gov/cgi-bin/geo/shapefiles2010/main Downloaded the PL94-171 demographic data from the following address: http://www.census.gov/rdo/data/2010_census_redistricting_data_pl_94-171_summary_files.html Post processed the demographic data per instructions. Stored in Access database. Set up SQL Query to create a subset of the data based on feedback from HSPHD and the GIS Office. Database documentation on file at the GIS Office. Imported the shapefile and demographic data into a File geodatabase. Joined and saved the data sets. Performed error checking by comparing against State and Maptitude versions. The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

Link to Attribute Table Information: http://gis.hennepin.us/OpenData/Metadata/2010%20Census%20Blocks.pdf

Use Limitations: This data (i) is furnished "AS IS" with no representation as to completeness or accuracy; (ii) is furnished with no warranty of any kind; and (iii) is not suitable for legal, engineering or surveying purposes. Hennepin County shall not be liable for any damage, injury or loss resulting from this data.

© Hennepin County GIS Office This layer is a component of Datasets for Hennepin County AGOL and Hennepin County Open Data..

Release Date: 2021-04-22.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments and firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Capital expenditures for new construction ($1,000).Capital expenditures for new construction for work done by own employees ($1,000).Expenses for maintenance and repair ($1,000).Expenses for maintenance and repair for work done by own employees ($1,000).Response coverage of construction activity capital expenditures inquiry (%).Response coverage of construction activity expenses for maintenance & repair inquiry (%)..Geography Coverage.The data are shown for employer establishments at the US and State levels. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown for 2017 NAICS code 486. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists..Footnotes:.Not applicable..FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector48/EC1748CONACT.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.