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TwitterList of cities, zip codes and counties in Missouri
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Includes Maryland Zip Codes and their corresponding cities/towns, and counties.
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TwitterThis application shows comprehensive data for properties in the City of Winchester, Virginia. This data includes school district information, fire and rescue first due area, voting information, refuse and recycling and zoning information. It also shows the tax card information for each property queried.
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TwitterThis dataset contains a listing of incorporated places (cities and towns) and counties within the United States including the GNIS code, FIPS code, name, entity type and primary point (location) for the entity. The types of entities listed in this dataset are based on codes provided by the U.S. Census Bureau, and include the following: C1 - An active incorporated place that does not serve as a county subdivision equivalent; C2 - An active incorporated place legally coextensive with a county subdivision but treated as independent of any county subdivision; C3 - A consolidated city; C4 - An active incorporated place with an alternate official common name; C5 - An active incorporated place that is independent of any county subdivision and serves as a county subdivision equivalent; C6 - An active incorporated place that partially is independent of any county subdivision and serves as a county subdivision equivalent or partially coextensive with a county subdivision but treated as independent of any county subdivision; C7 - An incorporated place that is independent of any county; C8 - The balance of a consolidated city excluding the separately incorporated place(s) within that consolidated government; C9 - An inactive or nonfunctioning incorporated place; H1 - An active county or statistically equivalent entity; H4 - A legally defined inactive or nonfunctioning county or statistically equivalent entity; H5 - A census areas in Alaska, a statistical county equivalent entity; and H6 - A county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.
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TwitterThis data collection contains information about the population of each county, town, and city of the United States in 1850 and 1860. Specific variables include tabulations of white, black, and slave males and females, and aggregate population for each town. Foreign-born population, total population of each county, and centroid latitudes and longitudes of each county and state were also compiled. (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 -- https://doi.org/10.3886/ICPSR09424.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.
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The Green Book Online is a fully searchable database which gives New Yorkers the opportunity to search for the agencies, offices, boards and commissions that keep our City running. It includes listings for New York City, County, Courts, and New York State government offices.
This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!
This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.
Cover photo by Venveo on Unsplash
Unsplash Images are distributed under a unique Unsplash License.
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Abstract (en): This data collection is a compendium of data for all counties in the United States for the period 1944 to 1977. The data provide diverse information such as local government activities, population estimates and characteristics, and housing unit descriptors. Also included is information on local government revenues, property taxes, capital outlay, debts, expenditures on education, highways, public welfare, health and hospitals, and police, as well as information on births, deaths, schooling, labor force, employment, family income, family characteristics, electoral votes, and housing characteristics. Additional variables provide information on manufacturing, retail and wholesale trade, banking, mineral industries, farm population, agriculture, crime, and weather. Users may also be interested in the related data collection, COUNTY AND CITY DATA BOOK [UNITED STATES] CONSOLIDATED FILE: CITY DATA, 1944-1977 (ICPSR 7735). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Individual states, the District of Columbia, and counties or county equivalents for which data were published in the County and City Data Books, in the entire United States in the period 1947-1977. Smallest Geographic Unit: county 2012-09-18 The data have been checked and corrected for inconsistencies, and have been reformatted to one record per case. SAS, SPSS, and Stata setup files have been updated. SPSS and Stata system files and a SAS transport (CPORT) file have been added to the collection. The codebook has been updated.2008-04-01 SAS, SPSS, and Stata setup files have been added to this data collection. record abstractsThis data file includes both state and county records. Records for counties in each state are listed immediately following the state record. All records have the same structure, and the identifier for each record includes both state and the county codes. In the state records, the county code is listed as 000.
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TwitterThe Real Property Records Search allows the user to obtain ownership information as of January 1, value of improvements and land, photo of improvement, sales information for the last three (3) years, view the tax bill and GIS Mapping information associated with a parcel.Disclaimer: Data presented on the Tax Administration Records Search website is extracted from actual public records. Users are hereby notified that the aforementioned public primary information sources should be consulted for verification of the information contained on this website. While our department has made every effort to use the most current and accurate data, the County of Durham and software companies assume no legal responsibility for the information contained in the Tax Administration Records Search website. Please direct any questions or comments about the data displayed here to tax_assessor@dconc.gov.
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API URL: https://api.nlsc.gov.tw/idc/TextQueryRoad,Input parameters: /county code (land administration system code)/road keyword,Returned result: XML; includes complete road name, township code (obtained by ListTown1), township name,API syntax example: https://api.nlsc.gov.tw/idc/TextQueryRoad/B/
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Abstract (en): This collection presents in computer-readable form the data items used to produce the corresponding printed volume of the COUNTY AND CITY DATA BOOK, 1988. Included is a broad range of statistical information, made available by federal agencies and national associations, for counties, cities, and places. Information also is provided for the 50 states, the District of Columbia, and for the United States as a whole. The dataset is comprised of seven files: a county file, a city file, and a place file, with footnote files and data dictionaries for both the county and the city files. The county data file contains information on areas such as age, agriculture, banking, construction, crime, education, federal expenditures, personal income, population, and vital statistics. The city data file includes variables such as city government, climate, crime, housing, labor force and employment, manufactures, retail trade, and service industries. Included in the place data file are items on population and money income. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. The universe varies from item to item within the files, e.g., all persons, all housing units, all local governments, etc. 2009-05-26 SAS, SPSS, and Stata setups have been added to this data collection.2006-03-30 File CB9251.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Users are advised that the codebook that the Census Bureau has issued for use with this dataset is a preliminary one and does not include codes and definitions for states, counties, and cities. The codes and definitions may be listed off the tape or users may refer to other sources such as the printed version of the COUNTY AND CITY DATA BOOK, 1988. For each case in the Counties Data file, there are two 1,239-character records.
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TwitterODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
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This dataset contains FIPS (Federal Information Processing Standard), GNIS (Geographic Name Information System common) codes for identifying Washington state counties cities and towns. This is an official list from OFM (Office of Financial Management).
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The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf.
This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.
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TwitterWARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:
Purpose
County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.
Related Layers
This dataset is part of a grouping of many datasets:
Point of Contact
California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov
Field and Abbreviation Definitions
Accuracy
CDTFA"s source data notes the following about accuracy:
City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI =
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NEW!: Use the new Business Account Number lookup tool.
SUMMARY This dataset includes the locations of businesses that pay taxes to the City and County of San Francisco. Each registered business may have multiple locations and each location is a single row. The Treasurer & Tax Collector’s Office collects this data through business registration applications, account update/closure forms, and taxpayer filings. Business locations marked as “Administratively Closed” have not filed or communicated with TTX for 3 years, or were marked as closed following a notification from another City and County Department.
The data is collected to help enforce the Business and Tax Regulations Code including, but not limited to: Article 6, Article 12, Article 12-A, and Article 12-A-1. http://sftreasurer.org/registration.
HOW TO USE THIS DATASET
To learn more about using this dataset watch this video. To update your listing or look up your BAN see this FAQ: Registered Business Locations Explainer
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TwitterRetail Sales Tax Data by County and City
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TwitterThis layer contains information for locating past and present legal city boundaries within Los Angeles County. The Los Angeles County Department of Public Works provides the most current shapefiles representing city annexations and city boundaries on the Los Angeles County GIS Data Portal. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California. Numerous records are freely available at the Land Records Information website, hosted by the Department of Public Works.Principal Attributes:NO: The row number in the attribute table of the PDF Annexation Maps. (See Below)
ANNEX_No: These values are only used for the City of Los Angeles and Long Beach.
NAME: The official annexation name.
TYPE: Indicates the legal action.
A - represents an Annexation to that city. D - represents a Detachment from that city. V - is used to indicate the annexation was void or withdrawn before an effective date could be declared. 33 - Some older city annexation maps indicate a city boundary declared 'as of February 8, 1933'.
ANNEX_AREA: is the land area annexed or detached, in square miles, per the recorded legal description.
TOTAL_AREA: is the cumulative total land area for each city, arranged chronologically.
SHADE: is used by some of our cartographers to store the color used on printed maps.
INDEXNO: is a matching field used for retrieving documents from our department's document management system.
STATE (Secretary of State): Date filed with the Secretary of State. These are not available for earlier annexations and are Null.
COUNTY (County Recorder): Date filed with the County Recorder. These are not available for earlier annexations and are Null.
EFFECTIVE (Effective Date): The effective date of the annexation or detachment.
CITY: The city to which the annexation or detachment took place.
URL: This text field contains hyperlinks for viewing city annexation documents. See the ArcGIS Help for using the Hyperlink Tool.
FEAT_TYPE: contains the type of feature each polygon represents:
Land - Use this value for your definition query if you want to see only land features on your map. Pier - This value is used for polygons representing piers along the coastline. One example is the Santa Monica Pier. Breakwater - This value is used for polygons representing man-made barriers that protect the harbors. Water - This value is used for polygons representing navigable waters inside the harbors and marinas. 3NM Buffer - Per the Submerged Lands Act, the seaward boundaries of coastal cities and unincorporated county areas are three nautical miles from the coastline. (A nautical mile is 1,852 meters, or about 6,076 feet.) Annexation Maps by City (PDF)Large format, high quality wall maps are available for each of the 88 cities in Los Angeles County in PDF format.Agoura HillsHermosa BeachNorwalkAlhambraHidden HillsPalmdaleArcadiaHuntington ParkPalos Verdes EstatesArtesiaIndustryParamountAvalonInglewoodPasadenaAzusaIrwindalePico RiveraBaldwin ParkLa Canada FlintridgePomonaBellLa Habra HeightsRancho Palos VerdesBell GardensLa MiradaRedondo BeachBellflowerLa PuenteRolling HillsBeverly HillsLa VerneRolling Hills EstatesBradburyLakewoodRosemeadBurbankLancasterSan DimasCalabasasLawndaleSan FernandoCarsonLomitaSan GabrielCerritosLong BeachSan MarinoClaremontLos Angeles IndexSanta ClaritaCommerceLos Angeles Map 1Santa Fe SpringsComptonLos Angeles Map 2Santa MonicaCovinaLos Angeles Map 3Sierra MadreCudahyLos Angeles Map 4Signal HillCulver CityLos Angeles Map 5South El MonteDiamond BarLos Angeles Map 6South GateDowneyLos Angeles Map 7South PasadenaDuarteLos Angeles Map 8Temple CityEl MonteLynwoodTorranceEl SegundoMalibuVernonGardenaManhattan BeachWalnutGlendaleMaywoodWest CovinaGlendoraMonroviaWest HollywoodHawaiian GardensMontebelloWestlake VillageHawthorneMonterey ParkWhittier
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TwitterProperty currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.
The following columns were added 4/14/2023:
The following columns were added 3/19/2024:
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TwitterThis data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).
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Important Dataset Update 6/24/2020:Summit and Wasatch Counties updated.Important Dataset Update 6/12/2020:MAG area updated.Important Dataset Update 7/15/2019: This dataset now includes projections for all populated statewide traffic analysis zones (TAZs). Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.As with any dataset that presents projections into the future, it is important to have a full understanding of the data before using it. Before using this data, you are strongly encouraged to read the metadata description below and direct any questions or feedback about this data to analytics@wfrc.org. Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas. These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2019-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2015 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process. As these projections may be a valuable input to other analyses, this dataset is made available at http://data.wfrc.org/search?q=projections as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes. Wasatch Front Real Estate Market Model (REMM) ProjectionsWFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:Demographic data from the decennial census;County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature;Current employment locational patterns derived from the Utah Department of Workforce Services; Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff;Current land use and valuation GIS-based parcel data stewarded by County Assessors;Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations; andCalibration of model variables to balance the fit of current conditions and dynamics at the county and regional level.‘Traffic Analysis Zone’ ProjectionsThe annual projections are forecasted for each of the Wasatch Front’s 2,800+ Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres). ‘City Area’ ProjectionsThe TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.Summary Variables in the DatasetsAnnual projection counts are available for the following variables (please read Key Exclusions note below):DemographicsHousehold Population Count (excludes persons living in group quarters)Household Count (excludes group quarters)EmploymentTypical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)Retail Job Count (retail, food service, hotels, etc)Office Job Count (office, health care, government, education, etc)Industrial Job Count (manufacturing, wholesale, transport, etc)Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count.All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).* These variable includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.Key Exclusions from TAZ and ‘City Area’ ProjectionsAs the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.
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