The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

This is a dataset of 2020 U.S. Census Block Groups within the City of Buffalo. Block Groups are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used to present data and control block numbering. A block group consists of clusters of blocks within the same census tract that have the same first digit of their four-digit census block number. For example, blocks 3001, 3002, 3003, . . ., 3999 in census tract 1210.02 belong to BG 3 in that census tract.

Context

This dataset expands on my earlier New York City Census Data dataset. It includes data from the entire country instead of just New York City. The expanded data will allow for much more interesting analyses and will also be much more useful at supporting other data sets.

Content

The data here are taken from the DP03 and DP05 tables of the 2015 American Community Survey 5-year estimates. The full datasets and much more can be found at the American Factfinder website. Currently, I include two data files:

1. acs2015_census_tract_data.csv: Data for each census tract in the US, including DC and Puerto Rico.
2. acs2015_county_data.csv: Data for each county or county equivalent in the US, including DC and Puerto Rico.

The two files have the same structure, with just a small difference in the name of the id column. Counties are political subdivisions, and the boundaries of some have been set for centuries. Census tracts, however, are defined by the census bureau and will have a much more consistent size. A typical census tract has around 5000 or so residents.

The Census Bureau updates the estimates approximately every year. At least some of the 2016 data is already available, so I will likely update this in the near future.

Acknowledgements

The data here were collected by the US Census Bureau. As a product of the US federal government, this is not subject to copyright within the US.

Inspiration

There are many questions that we could try to answer with the data here. Can we predict things such as the state (classification) or household income (regression)? What kinds of clusters can we find in the data? What other datasets can be improved by the addition of census data?

Context

There are a number of Kaggle datasets that provide spatial data around New York City. For many of these, it may be quite interesting to relate the data to the demographic and economic characteristics of nearby neighborhoods. I hope this data set will allow for making these comparisons without too much difficulty.

Exploring the data and making maps could be quite interesting as well.

Content

This dataset contains two CSV files:

1. nyc_census_tracts.csv

   This file contains a selection of census data taken from the ACS DP03 and DP05 tables. Things like total population, racial/ethnic demographic information, employment and commuting characteristics, and more are contained here. There is a great deal of additional data in the raw tables retrieved from the US Census Bureau website, so I could easily add more fields if there is enough interest.

   I obtained data for individual census tracts, which typically contain several thousand residents.

2. census_block_loc.csv

   For this file, I used an online FCC census block lookup tool to retrieve the census block code for a 200 x 200 grid containing New York City and a bit of the surrounding area. This file contains the coordinates and associated census block codes along
   with the state and county names to make things a bit more readable to users.

   Each census tract is split into a number of blocks, so one must extract the census tract code from the block code.

Acknowledgements

The data here was taken from the American Community Survey 2015 5-year estimates (https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml).

The census block coordinate data was taken from the FCC Census Block Conversions API (https://www.fcc.gov/general/census-block-conversions-api)

As public data from the US government, this is not subject to copyright within the US and should be considered public domain.

Big “p” policy changes at the state and federal level are certainly important to health equity, such as eligibility for and generosity of Medicaid benefits. Medicaid expansion has significantly expanded the number of people who are eligible for Medicaid and the creation of the health insurance exchanges (Marketplace) under the Affordable Care Act created a very visible avenue through which people can learn that they are eligible. Although many applications are now submitted online, physical access to state, county, and tribal government Medicaid offices still plays a critical role in understanding eligibility, getting help in applying, and navigating required documentation for both initial enrollment and redetermination of eligibility. However, as more government functions have moved online, in-person office locations and/or staff may have been cut to reduce costs, and gentrification has shifted where minoritized, marginalized, and/or low-income populations live, it is unclear if this key local connection point between residents and Medicaid has been maintained. Our objective was to identify and geocode all Medicaid offices in the United States for pairing with other spatial data (e.g., demographics, Medicaid participation, health care use, health outcomes) to investigate policy-relevant research questions. Three coders identified Medicaid office addresses in all 50 states and the District of Columbia by searching state government websites (e.g., Department of Health and Human Services or analogous state agency) during late 2021 and early 2022 for the appropriate Medicaid agency and its office locations, which were then reviewed for accuracy by a fourth coder. Our corpus of Medicaid office addresses was then geocoded using the Census Geocoder from the US Census Bureau (https://geocoding.geo.census.gov/geocoder/) with unresolved addresses investigated and/or manually geocoded using Google Maps. The corpus was updated in August through December 2023 following the end of the COVID-19 public health emergency by a fifth coder as several states closed and/or combined offices during the pandemic. After deduplication (e.g., where multiple counties share a single office) and removal of mailing addresses (e.g., PO Boxes), our dataset includes 3,027 Medicaid office locations. 1 (December 19, 2023) – original version 2 (January 25, 2024) – added related publication (Data in Brief), corrected two records that were missing negative signs in longitude 3 (February 6, 2024) – corrected latitude and longitude for one office (1340 State Route 9, Lake George, NY 12845) 4 (March 4, 2024) – added one office for Vermont after contacting relevant state agency (280 State Road, Waterbury, VT 05671)

2020 Census Tracts from the US Census for New York City. These boundary files are derived from the US Census Bureau's TIGER data products and have been geographically modified to fit the New York City base map. All previously released versions of this data are available at BYTES of the BIG APPLE- Archive.

This CT Counties layer consists of individual polygons representing each of the 8 counties that make up the state of Connecticut.

This feature layer is directly derived from the CTDOT Municipalities feature layer geometry, created by CT Department of Transportation. The municipalities are dissolved into their associated counties.

This feature layer includes US Census Federal Information Processing Standards (FIPS) codes that are associated with each municipality. This was included based on information from 'https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes/2020.html' rel='nofollow ugc'>Connecticut County to County Subdivision Crosswalk from the US Census.

The 9 Planning Regions in Connecticut have replaced the counties for statistical and administrative functions, so these 8 counties are used only in legacy geography.

Connecticut’s 9 planning regions provide a geographic framework within which municipalities can jointly address common interests and coordinate such interests with state plans and programs. CGS Section 16a-4a authorizes the Secretary of the Office of Policy and Management (OPM) to designate or redesignate the boundaries of logical planning regions. CGS Section 4-124j authorizes the member municipalities of each planning region to establish a formal regional governance structure known as a council of governments (COG).

For more information see:

• 'https://portal.ct.gov/opm/igpp/org/planning-regions/planning-regions---overview' rel='nofollow ugc'>CT Office of Policy and Management, Regional Councils of Governments in Connecticut
• 'https://www2.census.gov/geo/pdfs/reference/ct_county_equiv_change.pdf' rel='nofollow ugc'>Final Change to County Equivalents in Connecticut [PDF]
• 'https://www.federalregister.gov/documents/2022/06/06/2022-12063/change-to-county-equivalents-in-the-state-of-connecticut' rel='nofollow ugc'>Final Federal Register Notice
• 'https://portal.ct.gov/sots/register-manual/section-vii/regional-councils-of-governments' rel='nofollow ugc'>CT Office of the Secretary of the State, Regional Councils of Governments

CT Municipalities

CT Counties

CT Councils of Governments

CT Planning Regions

2010 Census Tract polygons for all of Pierce County, realigned to Pierce County roads, parcels, hydro, railroads, powerlines, etc. Population figures for race, ethnicity, median age, household size, household count, etc were downloaded from the US Census website and joined to this layer. Please read metadata for additional information. Any data download constitutes acceptance of the Terms of Use.Abstract:Census tracts are small, relatively permanent statistical subdivisions of a county delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The U.S. Census Bureau delineated census tracts in situations where no local participant existed or where local or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. This is the first decennial census for which the entire United States is covered by census tracts. For the 1990 census, some counties had census tracts and others had block numbering areas (BNAs). In preparation for Census 2000, all BNAs were replaced by census tracts, which may or may not cover the same areas. The original 2010 census data was downloaded via ftp from Census. The lines were were redrawn with the best available data; such as roads, parcels, hydro, railroads, powerlines. Pierce County requires that the Census layers to be used with county data layers. The population figures were downloaded from the US Census website and joined to this this theme. For more information read 2010 Census Summary File 1 (Issued June 2011). The pdf document is located at: https://www.census.gov/prod/cen2010/doc/sf1.pdfPurpose:The US Census Tracts are used by variety of departments for analysis, planning, and mapping purposes.DATA LINKED FROM PIERCE COUNTY OPEN DATA PORTALSee the metadata and original layer here

Census tracts as of 2018."Census Tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity that are updated by local participants prior to each decennial census as part of the Census Bureau's Participant Statistical Areas Program. The Census Bureau delineates census tracts in situations where no local participant existed or where state, local, or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of statistical data.Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census. Census tracts occasionally are split due to population growth or merged as a result of substantial population decline.Census tract boundaries generally follow visible and identifiable features. They may follow nonvisible legal boundaries, such as minor civil division (MCD) or incorporated place boundaries in some states and situations, to allow for census-tract-to-governmental-unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. Tribal census tracts are a unique geographic entity defined within federally recognized American Indian reservations and off-reservation trust lands and can cross state and county boundaries. Tribal census tracts may be completely different from the census tracts and block groups defined by state and county.Census Tract Codes and Numbers—Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively."- US Census Bureau For information about US Census Bureau geographies, click here. USE CONSTRAINTS: The Alaska Department of Commerce, Community, and Economic Development (DCCED) provides the data in this application as a service to the public. DCCED makes no warranty, representation, or guarantee as to the content, accuracy, timeliness, or completeness of any of the data provided on this site. DCCED shall not be liable to the user for damages of any kind arising out of the use of data or information provided. DCCED is not the authoritative source for American Community Survey data, and any data or information provided by DCCED is provided "as is". Data or information provided by DCCED shall be used and relied upon only at the user's sole risk.

Release Date: 2016-02-23.[NOTE: Includes firms with paid employees and firms with no paid employees. Data are based on the 2012 Economic Census, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2012 Survey of Business Owners. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Respondent firms include all firms that responded to the characteristic(s) tabulated in this dataset and reported gender, ethnicity, race, or veteran status or that were publicly held or not classifiable by gender, ethnicity, race, and veteran status. Percentages are for respondent firms only and are not recalculated when the dataset is resorted. Percentages are always based on total reporting (defined above) within a gender, ethnicity, race, veteran status, and/or industry group for the characteristics tabulated in this dataset. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.].Data User Notice posted on April 12, 2016: Census Bureau staff identified a processing error that affects selected data from the 2012 Survey of Business Owners and Self-Employed Persons (SBO). As a result, we revised 2012 estimates for the following categories: Business loan from federal, state, or local government; Government-guaranteed business loan from a bank or financial institution; and Business loan from a bank or financial institution for all geographies and sectors. This processing error did not affect other categories in this table...Table Name. . Statistics for All U.S. Firms by Sources Used to Finance Expansion or Capital Improvements by Industry, Gender, Ethnicity, Race, and Veteran Status for the U.S.: 2012. ..Release Schedule. . The data in this file was released in February 2016.. ..Key Table Information. . This data is related to all other 2012 SBO files.. Refer to the Methodology section of the Survey of Business Owners website for additional information.. ..Universe. . The universe for the 2012 Survey of Business Owners (SBO) includes all U.S. firms operating during 2012 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. In this file, "respondent firms" refers to all firms that reported gender, ethnicity, race, or veteran status for at least one owner or returned a survey form with at least one item completed and were publicly held or not classifiable by gender, ethnicity, race, and veteran status.. ..Geographic Coverage. . The data are shown at the U.S. level only.. ..Industry Coverage. . The data are shown for the total of all sectors (NAICS 00) and at the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms by Sources Used to Finance Expansion or Capital Improvements by Industry, Gender, Ethnicity, Race, and Veteran Status for the U.S.: 2012 contains data on:. . Number of firms, firms with paid employees, and firms with no paid employees. Sales and receipts for all firms, firms with paid employees, and firms with no paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. Percent of all respondent firms, respondent firms with paid employees, and respondent firms with no paid employees. Percent of sales and receipts of all respondent firms, respondent firms with paid employees, and respondent firms with no paid employees. Percent of number of employees of respondent firms with paid employees. Percent of annual payroll of respondent firms with paid employees. . The data are published by sources used to finance expansion or capital improvements in 2012 and by gender, ethnicity, race, and veteran status.. ..Sort Order. . Data are presented in ascending levels by:. . NAICS code (NAICS2012). Gender, ethnicity, race, and veteran status (CBGROUP). Sources used to finance expansion or capital improvements in 2012 (EXPSRCE). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSCB28 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSCB28.zip. ..Contact Information...

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

Listing of all active businesses currently registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly. NAICS Codes are from 2007 NAICS: https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007