Source: https://www.census.gov/programs-surveys/ahs/data/2011/ahs-2011-public-use-file-puf/ahs-2011-national-public-use-file-puf.html Further explanation on columns: https://www.census.gov/data-tools/demo/codebook/ahs/ahsdict.html?s_keyword=&s_year=&sortby=

The AHS is sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The survey is the most comprehensive national housing survey in the United States.

Census 2020 blocks in King County with selected P.L. 94-171 redistricting data.

This workflow provides the prototype components of open dataset tools in KNIME Python-based Geospatial Extension, Users can acquire the data by easily defining the variable and geographic level. It contains 4 nodes: US2020 TIGER for US Basemap( Census Block, Block Group, Tract, and County), US2020 Census for Decennial Census P.L. 94-171 Redistricting Data US ACS-5: for the data of American Community Survey (ACS) 5 Years. GeoView: for geodata visualization Requirements: US Census API key:https://api.census.gov/data/key_signup.html KNIME Extension: KNIME Python Integration Python Package: geopandas, requests, matplotlib

Source: https://www.census.gov/programs-surveys/ahs/data/2011/ahs-2011-public-use-file-puf/ahs-2011-national-public-use-file-puf.html Further explanation on columns: https://www.census.gov/data-tools/demo/codebook/ahs/ahsdict.html?s_keyword=&s_year=&sortby=

The AHS is sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The survey is the most comprehensive national housing survey in the United States.

Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

The US government's 'Topologically Integrated Geographic Encoding and Referencing' system, usually referred to as TIGER, is based on an extensive database of US geographic information. It is county-level data that documents physical features like roads and rivers, as well as some administrative features such as Congressional districts. Data can be downloaded for each state and for each of the following: Puerto Rico, American Samoa, Guam, Northern Mariana Islands, Midway Island, and the US Virgin Islands.

The database does not contain demographic or topographic (terrain) data; the "topology" referenced in the name refers to how the database itself is designed.

Only recent versions are available online for free download. Note also that the Census Bureau is making substantial changes to how the TIGER system is formatted, which should allow for wider and more effective compatibility with GIS tools. A good overview can be seen at this page

This is the shapefile for 2010 Census Block Group Number. You can download it in the formats of Spreadsheet, KML, Shapefile in Zip file, or Full Dataset. It was created by the GIS Team in the Division of Information Technology Service at Texas Education Agency and data was based on the Census Bureau with website, https://www.census.gov/geo/maps-data/data/cbf/cbf_blkgrp.html with ESRI ArcGIS Tool (www.arcgis.com).

This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. This dataset is a preprocessed version of the American Community Survey (ACS) 2018 Public Use Microdata Sample (PUMS), provided by the U.S. Census Bureau. It is designed for machine learning research on domain generalization. The task is to predict individual income based on demographic and socioeconomic features, with age group used to define domain splits. Preprocessing was adapted from the Whyshift repository (https://github.com/namkoong-lab/whyshift), which builds on the Folktables project (https://github.com/socialfoundations/folktables). Folktables provides tools to extract and structure ACS PUMS data. The data remains governed by the U.S. Census Bureau terms of service, available at: https://www.census.gov/data/developers/about/terms-of-service.html.

This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. This dataset is a preprocessed version of the American Community Survey (ACS) 2018 Public Use Microdata Sample (PUMS), provided by the U.S. Census Bureau. It is designed for machine learning research on domain generalization. The task is to predict individual poverty ratio based on demographic and socioeconomic features, with age group used to define domain splits. Preprocessing was adapted from the Whyshift repository (https://github.com/namkoong-lab/whyshift), which builds on the Folktables project (https://github.com/socialfoundations/folktables). Folktables provides tools to extract and structure ACS PUMS data. The data remains governed by the U.S. Census Bureau terms of service, available at: https://www.census.gov/data/developers/about/terms-of-service.html.

Release Date: 2015-12-15.[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. 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.]..Table Name. . Statistics for All U.S. Firms by Industry, Ethnicity, and Receipts Size of Firm for the U.S. and States: 2012. ..Release Schedule. . This file was released in December 2015. Included are statistics for:. . Hispanic-Owned Firms (HISP). Company Summary (CS)-- Includes estimates for minority- and nonminority-owned firms. . ..Key Table Information. . This data supersedes all preliminary results released on August 18, 2015, and 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.. ..Geographic Coverage. . The data are shown for the United States at the national and state levels.. ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code levels.. ..Data Items and Other Identifying Records. . Statistics for All U.S. Firms by Industry, Ethnicity, and Receipts Size of Firm for the U.S. and States: 2012 contains data on:. . Numbers of all 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. . The data are shown for:. . All firms classifiable by gender, ethnicity, race, and veteran status. . Ethnicity. . Hispanic. . Mexican, Mexican American, Chicano. Puerto Rican. Cuban. Other Hispanic, Latino, or Spanish origin. . . Equally Hispanic/non-Hispanic. Non-Hispanic. . . Receipts size of firm. . All firms. Firms with sales/receipts of less than $5,000. Firms with sales/receipts of $5,000 to $9,999. Firms with sales/receipts of $10,000 to $24,999. Firms with sales/receipts of $25,000 to $49,999. Firms with sales/receipts of $50,000 to $99,999. Firms with sales/receipts of $100,000 to $249,999. Firms with sales/receipts of $250,000 to $499,999. Firms with sales/receipts of $500,000 to $999,999. Firms with sales/receipts of $1,000,000 or more. . . . . Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Ethnicity (ETH_GROUP). Receipts size of firm (RCPSZFI). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SB1200CSA06 table at: https://www2.census.gov/programs-surveys/sbo/data/2012/SB1200CSA06.zip. ..Contact Information. . To contact the Survey of Business Owners staff:. . Visit the website at www.census.gov/programs-surveys/sbo.html.. Email general, nonsecure, and unencrypted messages to ewd.survey.of.business.owners@census.gov.. Call 301.763.3316 between 7 a.m. and 5 p.m. (EST), Monday through Friday.. Write to:. U.S. Census Bureau. Survey of Business Owners. 4600 Silver Hill Road. Washington, DC 20233. . . ...Source: U.S. Census Bureau, 2012 Survey of Business Owners.Note: The data in...

Context

Dataset aims to further a county by county analysis of potential risk factors that could heighten Covid 19 transmission rates or deaths. The data has now been split between general population and over 60 estimates and converted to counts for ease of use.

Content

It includes a subset of county by county ACS estimates of:

The data includes information on:

1) County level indicators for over 60 populations including population density, race, poverty level, housing size, sources of income, employment status, whether living alone, language barriers, immigration status, and disability status.

2) County level indicators for the general population including race, poverty level, housing size, sources of income, employment status, whether living alone, language barriers, immigration status, and disability status, modes of transportation stats, and industry stats.

Acknowledgements

For traceability and recreation purposes, I published a kernel with the R code outlining the process used to produce the data set. https://www.kaggle.com/jtourkis/kernel3f7cd0a961

The information comes from 2018 5 Year estimates from the American Community Survey (ACS).

ACSST5Y2018.S0102 ACSST5Y2018.S0804 ACSST5Y2018.S2403

Note: ACS five year estimates are selections limited to counties with populations over 20,000. https://www.census.gov/programs-surveys/acs/guidance/estimates.html

Link to ACS/Census Tables:

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

It also includes 4/16 and 4/22 Daily Spread Estimates from John Hopkins and Population Density from the CDC Social Vulnerability Index.

Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. Social Vulnerability Index 2018 Database US. data-and-tools-download.html. Accessed on 4/16.

Inspiration

I hope this data will help bring people closer to understanding what economic factors correlate to or influence disease spread.

The Freight Analysis Framework (FAF), produced through a partnership between BTS and FHWA, integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. Starting with data from the 2012 Commodity Flow Survey (CFS) and international trade data from the Census Bureau, FAF incorporates data from agriculture, extraction, utility, construction, service, and other sectors.

FAF version 4 (FAF⁴) provides estimates for tonnage (in thousand tons) and value (in million dollars) by regions of origin and destination, commodity type, and mode. Data are available for the base year of 2012, the recent years of 2013 - 2015, and forecasts from 2020 through 2045 in 5-year intervals. Data may be accessed through the Data Extraction Tool, downloaded as a complete database, or in summary files.

Throughout 2016, releases of additional FAF4 products will provide state-to-state flows for 1997, 2002, and 2007; truck flows assigned to the highway network for 2012 and 2045; and domestic ton-miles and distance bands.

The D3 vacancy index was designed to provide a more nuanced assessment of structural vacancy than the "occupied/unoccupied/maybe" categories used in the Motor City Mapping windshield survey. The dataset includes several sources, including Motor City Mapping, utility data, and other resources to create a score evaluating the occupancy status of a parcel. These values are then coded as "Likely Occupied", "Potentially Vacant", "Likely Vacant", and "Very Likely Vacant", helping to show a spectrum of vacancy across Detroit. Because the vacancy index incorporates proprietary data sources, D3 is unable to release the raw values of the index to the public at the address level. To allow the public to obtain some of the benefit from this highly-effective tool, however, D3 aggregated this data to the Census Tract level. This file allows Detroit's policymakers and community members to track vacancy across the city using data that is as up-to-date as possible.Metadata associated with this file includes field description metadata and a narrative summary documenting the creation of the dataset.

This map service displays all air-related layers used in the USEPA Community/Tribal-Focused Exposure and Risk Screening Tool (C/T-FERST) mapping application (http://cfpub.epa.gov/cferst/index.cfm). The following data sources (and layers) are contained in this service: USEPA's 2005 National-Scale Air Toxic Assessment (NATA) data. Data are shown at the census tract level (2000 census tract boundaries, US Census Bureau) for Cumulative Cancer and Non-Cancer risks (Neurological and Respiratory) from 139 air toxics. In addition, individual pollutant estimates of Ambient Concentration, Exposure Concentration, Cancer, and Non-Cancer risks (Neurological and Respiratory) are provided for: Acetaldehyde, Acrolein, Arsenic, Benzene, 1,3-Butadiene, Chromium, Diesel PM, Formaldehyde, Lead, Naphthalene, and Polycyclic Aromatic Hydrocarbon (PAH). The original Access tables were downloaded from USEPA's Office of Air and Radiation (OAR) http://www.epa.gov/ttn/atw/nata2005/tables.html. The data classification (defined interval) for this map service was developed for USEPA's Office of Research and Development's (ORD) Community-Focused Exposure and Risk Screening Tool (C-FERST) per guidance provided by OAR. The 2005 NATA provides information on 177 of the 187 Clean Air Act air toxics (http://www.epa.gov/ttn/atw/nata2005/05pdf/2005polls.pdf) plus diesel particulate matter (diesel PM was assessed for non-cancer only). For additional information about NATA, go to http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf or contact Ted Palma, USEPA (palma.ted@epa.gov). NATA data disclaimer: USEPA strongly cautions that these modeling results are most meaningful when viewed at the state or national level, and should not be used to draw conclusions about local exposures or risks (e.g., to compare local areas, to identify the exact location of "hot spots", or to revise or design emission reduction programs). Substantial uncertainties with the input data for these models may cause the results to misrepresent actual risks, especially at the census tract level. However, we believe the census tract data and maps can provide a useful approximation of geographic patterns of variation in risk within counties. For example, a cluster of census tracts with higher estimated risks may suggest the existence of a "hot spot," although the specific tracts affected will be uncertain. More refined assessments based on additional data and analysis would be needed to better characterize such risks at the tract level. (http://www.epa.gov/ttn/atw/nata2005/countyxls/cancer_risk02_county_042009.xls). Note that these modeled estimates are derived from outdoor sources only; indoor sources are not included in these examples, but may be significant in some cases. The modeled exposure estimates are for a median individual in the geographic area shown. Note that in some cases the estimated relationship between human exposure and health effect may be calculated as a high end estimate, and thus may be more likely to overestimate than underestimate actual health effects for the median individual in the geographic area shown. Other limitations to consider when looking at the results are detailed on the EPA 2005 NATA website. For these reasons, the NATA maps included in C-FERST are provided for screening purposes only. See the 2005 National Air Toxic Assessment website for recommended usage and limitations on the estimated cancer and noncancer data provided above. USEPA's NonAttainment areas data. C-FERST displays Ozone for 8-hour Ozone based on the 1997 standard for reporting and Particulate Matter PM-2.5 based on the 2006 standard for reporting. These are areas of the country where air pollution levels consistently exceed the national ambient air quality standards. Details about the USEPA's NonAttainment data are available at http://www.epa.gov/airquality/greenbook/index.html. Center of Disease Control's (CDC) Environmental Public Health Tracking (EPHT) data. Averaged over three years (2004 - 2006). The USEPA's ORD calculated a three-year average (2004 - 2006) using the values for Ozone (number of days with the maximum 8-hour average above the National Ambient Air Quality Standards (NAAQS)) and PM 2.5 (annual ambient concentration). These data were extracted by the CDC from the USEPA's ambient air monitors and are displayed at the county level. USEPA received the Monitor and Modeled data from the CDC and calculated the three year average displayed in the web service. For more details about the CDC EPHT data, go to http://ephtracking.cdc.gov/showHome.action.

Excel Age-Range creator for 2001 and 2011 Census population figures.

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This Excel-based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error.

Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range.

This file uses the single year of age data from the 2011 Census released on 24 September 2012, which was available for all Local Authorities.

The ward data is currently modelled data for sex, based on single year of age data from Table qs103ew. The final data will be inserted into the tool when it is released in summer 2013.

Also included are the 2001 Census figures for comparison.

This tool was created by the GLA Intelligence Unit.

A seperate Custom Age-Range Tool for Census 2011 Workday population is available below. This is for local authorities and higher geographies only.

Download data from ONS website

Life expectancy at birth and age 65 by sex and ward, London borough, region, 1999/03 - 2008/12.

The population data used is revised 2002-2010 ONS mid year estimates (MYE) - revised post 2011 Census. Revised population estimates by single year of age for wards can also be found on the ONS website for 2002-2010, 2011, 2012, and 2013. These figures are consistent with the published revised mid-2002 to mid-2010 local authority estimates.

Rolling 5-year combined life expectancies are used for wards to reduce the effects of the variability in number of deaths in each year. The same method is applied to higher geographies to enable meaningful comparisons. However, 3-year combined expectancies are published separately on the Datastore for geographical areas that are local authority and above.

If the GLA publish revised 2002-2010 population data for wards then these life expectancy figures will also be revised to reflect them.

The ONS vital statistics mortality data breaks deaths into 10 year age bands. 5 year age band deaths were modelled using this data.

Vital Statistics: Population and Health Reference Tables are available on the ONS website http://www.ons.gov.uk/ons/rel/vsob1/vital-statistics--population-and-health-reference-tables/index.html">here.

The tool for calculating life expectancy is available from Public Health England.
The highest age band in the calculator is currently 85+. If the tool is updated with a higher upper age band (ie 90+), this data will be revised to reflect this change.

Healthy life expectancy and disability-free life expectancy (1999-2003) at birth have been calculated for wards in England and Wales. These can be found on the ONS website.

This data is also presented in the GLA ward profiles.

On a biennial basis, the Research and Innovative Technology Administration's (RITA's) Bureau of Transportation Statistics (BTS) conducts a census of all ferry operators operating in the United States and its territories. The information collected from the census is maintained in a national ferry database containing information regarding ferry systems including routes, vessels, passengers and vehicles carried, funding sources and other information. The numerous detailed data elements are provided in a relational database allowing access and analysis at various levels - operator, route segment, terminal, or vessel. The NCFO was first conducted in 2000 by the Volpe Center, another office within RITA. By legislative mandate (SAFETEA-LU), BTS assumed the role in 2006 and has subsequently conducted the NCFO in 2006, 2008 and 2010.