5 datasets found
  1. a

    Wildfire Resilience Census Tracts

    • livingatlas-dcdev.opendata.arcgis.com
    Updated Dec 7, 2021
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    Climate Solutions (2021). Wildfire Resilience Census Tracts [Dataset]. https://livingatlas-dcdev.opendata.arcgis.com/maps/climatesolutions::wildfire-resilience-census-tracts
    Explore at:
    Dataset updated
    Dec 7, 2021
    Dataset authored and provided by
    Climate Solutions
    Area covered
    Description

    This is a dataset of over 70,000 United States Census tracts enriched with over 25 demographic and environmental variables. These tracts cover the conterminous United States. The tract-level data were used to calculate and map climate resiliency indices.Data SourcesThis data product were first published in January 2022.United States (US) Census Bureau: American Community Survey (ACS) layers for all demographic and housing variables, TIGER/Line Shapefiles USA 2021 for national roads,US Centers for Disease Control and Prevention (CDC) for Daily Census Tract-Level PM2.5 Concentrations, 2016,US CDC PLACES: Local Data for Better Health for Current Asthma Prevalence,US Forest Service Wildfire Risk to Communities layers for Average Wildfire Exposure Type, Average Wildfire Risk to Homes, Average Housing Density, and Wildfire Hazard Potential.Processing NotesThe polygon features underwent several processing steps as part of the enrichment process. The tools used were dependent on the type of input data.All table joins used the attribute GEOID as a unique identifier for tracts.PM2.5 Concentrations were provided as coordinates for census tract centroids as well as census tract FIPS which was joined to polygon GEOIDs.The Zonal Statistics as Table geoprocessing tool was used on raster data types including Wildfire Exposure Type, Risk to Potential Structures, and Wildfire Hazard Potential inputs. Mean values for these inputs was calculated using the census tract as the zone and the raster as the value. Output was then joined back to the features.The Join Field geoprocessing tool was used with ACS input variables.The Egress Score was derived by intersecting TIGER/Line roads with tract boundaries. Roads were first filtered to include only Primary, Secondary, and Local roads. The number of intersections per tract was counted and normalized by the area of the tract. The inverse of this measure is called "Egress Score" and is used as a proxy for ranking tracts based on the number of routes into or out of each tract.*Note: This measure is intended for planning purposes only and should not be used for tactical decision making.Process OverviewFor every census tract, a Z-score was calculated that compares the value of each variable for the tract to the mean value for all tracts in the same county and is expressed as standard deviation from that mean. The Z-scores were than standardized into breaks ranging from 1 to 5 and averaged to create an overall wildfire resiliency index (WRI) for each tract. The WRIs and methodology were developed in collaboration with partners at the Centers for Disease Control and Prevention, UC Davis Department of Public Health, and the US Forest Service's Fire Lab.The tract Egress Score was derived by intersecting US Census Bureau TIGER/Line feature data with census tract polygon features to generate multipoint features. Because the TIGER/Line data may contain multiple coincident road segments that represent different road names, the multipoint features were dissolved using the unique GEOID and generated as point features. This result was summarized on GEOID and counted. The intersection point counts were joined back to the original tract features using GEOID. The counts were normalized by the area of the tracts and the reciprocal was calculated to get the Egress Score for the tract, higher Egress Score means fewer roads intersecting the tract and greater benefit from the intervention.Related WRI maps include “Where Will Better Air Filtration Improve Wildfire Resilience?”, “Where Will Home Hardening Improve Wildfire Resilience?”, and “Where Will Better Evacuation Routes Improve Wildfire Resilience?”.

  2. Income of individuals by age group, sex and income source, Canada, provinces...

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated May 1, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas [Dataset]. http://doi.org/10.25318/1110023901-eng
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    Dataset updated
    May 1, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

  3. d

    Johns Hopkins COVID-19 Case Tracker

    • data.world
    csv, zip
    Updated Jul 2, 2025
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    The Associated Press (2025). Johns Hopkins COVID-19 Case Tracker [Dataset]. https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker
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    zip, csvAvailable download formats
    Dataset updated
    Jul 2, 2025
    Authors
    The Associated Press
    Time period covered
    Jan 22, 2020 - Mar 9, 2023
    Area covered
    Description

    Updates

    • Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

    • April 9, 2020

      • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.
    • April 20, 2020

      • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.
    • April 29, 2020

      • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.
    • September 1st, 2020

      • Johns Hopkins is now providing counts for the five New York City counties individually.
    • February 12, 2021

      • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
      • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.
    • February 16, 2021

      - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

      Overview

    The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

    The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

    This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

    The AP is updating this dataset hourly at 45 minutes past the hour.

    To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

    Queries

    Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

    Interactive

    The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

    @(https://datawrapper.dwcdn.net/nRyaf/15/)

    Interactive Embed Code

    <iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>
    

    Caveats

    • This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
    • In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
    • In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
    • This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
    • Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
    • Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
    • The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

    Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

    Attribution

    This data should be credited to Johns Hopkins University COVID-19 tracking project

  4. Malaysia Household Income per Capita

    • ceicdata.com
    Updated Mar 15, 2019
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    CEICdata.com (2019). Malaysia Household Income per Capita [Dataset]. https://www.ceicdata.com/en/indicator/malaysia/annual-household-income-per-capita
    Explore at:
    Dataset updated
    Mar 15, 2019
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2002 - Dec 1, 2022
    Area covered
    Malaysia
    Description

    Key information about Malaysia Household Income per Capita

    • Malaysia Annual Household Income per Capita reached 5,731.680 USD in Dec 2022, compared with the previous value of 5,761.586 USD in Dec 2019.
    • Malaysia Annual Household Income per Capita data is updated yearly, available from Dec 2002 to Dec 2022, with an averaged value of 4,426.922 USD.
    • The data reached an all-time high of 5,761.586 USD in Dec 2019 and a record low of 2,100.510 USD in Dec 2002.
    • In the latest reports, Retail Sales of Malaysia grew 12.890 % YoY in Apr 2023.

    CEIC calculates Annnual Household Income per Capita from Monthly Average Household Income multiplied by 12, Number of Households and Total Population and converts it into USD. The Department of Statistics provides Average Household Income in local currency and Total Population based on 2020 Census. Malaysian Communications and Multimedia Commission provides Number of Households. Federal Reserve Board average market exchange rate is used for currency conversions. Population prior to 2020 is based on 2010 Census. Population prior to 2010 is based on 2000 Census. Annual Household Income per Capita prior to 2004 is calculated from Number of Household based on 2000 Census.

  5. England and Wales Census 2021 - RM202: Household size by number of rooms

    • statistics.ukdataservice.ac.uk
    csv, json, xlsx
    Updated Jun 10, 2024
    + more versions
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    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service. (2024). England and Wales Census 2021 - RM202: Household size by number of rooms [Dataset]. https://statistics.ukdataservice.ac.uk/dataset/england-and-wales-census-2021-rm202-household-size-by-number-of-rooms
    Explore at:
    xlsx, json, csvAvailable download formats
    Dataset updated
    Jun 10, 2024
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    UK Data Servicehttps://ukdataservice.ac.uk/
    Northern Ireland Statistics and Research Agency
    Authors
    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service.
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    England, Wales
    Description

    This dataset provides Census 2021 estimates that classify households in England and Wales by household size and by number of rooms. The estimates are as at Census Day, 21 March 2021.

    It is inappropriate to measure change in number of rooms from 2011 to 2021, as Census 2021 used Valuation Office Agency data for this variable. Instead use Census 2021 estimates for number of bedrooms for comparisons over time. Read more about this quality notice.

    Area type

    Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

    For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

    Coverage

    Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

    • country - for example, Wales
    • region - for example, London
    • local authority - for example, Cornwall
    • health area – for example, Clinical Commissioning Group
    • statistical area - for example, MSOA or LSOA

    Number of rooms (Valuation Office Agency)

    A room can be any room in a dwelling apart from bathrooms, toilets, halls or landings, kitchens, conservatories or utility rooms. All other rooms, for example, living rooms, studies, bedrooms, separate dining rooms and rooms that can only be used for storage are included. If two rooms have been converted into one, they are counted as one room.

    The number of rooms is recorded by address, this means that for households living in a shared dwelling the number of rooms are counted for the whole dwelling and not the individual household.

    This definition is based on the Valuation Office Agency’s (VOA) definition.

    Household size

    The number of people in the household.

    Visitors staying at an address do not count to that household’s size.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Climate Solutions (2021). Wildfire Resilience Census Tracts [Dataset]. https://livingatlas-dcdev.opendata.arcgis.com/maps/climatesolutions::wildfire-resilience-census-tracts

Wildfire Resilience Census Tracts

Explore at:
Dataset updated
Dec 7, 2021
Dataset authored and provided by
Climate Solutions
Area covered
Description

This is a dataset of over 70,000 United States Census tracts enriched with over 25 demographic and environmental variables. These tracts cover the conterminous United States. The tract-level data were used to calculate and map climate resiliency indices.Data SourcesThis data product were first published in January 2022.United States (US) Census Bureau: American Community Survey (ACS) layers for all demographic and housing variables, TIGER/Line Shapefiles USA 2021 for national roads,US Centers for Disease Control and Prevention (CDC) for Daily Census Tract-Level PM2.5 Concentrations, 2016,US CDC PLACES: Local Data for Better Health for Current Asthma Prevalence,US Forest Service Wildfire Risk to Communities layers for Average Wildfire Exposure Type, Average Wildfire Risk to Homes, Average Housing Density, and Wildfire Hazard Potential.Processing NotesThe polygon features underwent several processing steps as part of the enrichment process. The tools used were dependent on the type of input data.All table joins used the attribute GEOID as a unique identifier for tracts.PM2.5 Concentrations were provided as coordinates for census tract centroids as well as census tract FIPS which was joined to polygon GEOIDs.The Zonal Statistics as Table geoprocessing tool was used on raster data types including Wildfire Exposure Type, Risk to Potential Structures, and Wildfire Hazard Potential inputs. Mean values for these inputs was calculated using the census tract as the zone and the raster as the value. Output was then joined back to the features.The Join Field geoprocessing tool was used with ACS input variables.The Egress Score was derived by intersecting TIGER/Line roads with tract boundaries. Roads were first filtered to include only Primary, Secondary, and Local roads. The number of intersections per tract was counted and normalized by the area of the tract. The inverse of this measure is called "Egress Score" and is used as a proxy for ranking tracts based on the number of routes into or out of each tract.*Note: This measure is intended for planning purposes only and should not be used for tactical decision making.Process OverviewFor every census tract, a Z-score was calculated that compares the value of each variable for the tract to the mean value for all tracts in the same county and is expressed as standard deviation from that mean. The Z-scores were than standardized into breaks ranging from 1 to 5 and averaged to create an overall wildfire resiliency index (WRI) for each tract. The WRIs and methodology were developed in collaboration with partners at the Centers for Disease Control and Prevention, UC Davis Department of Public Health, and the US Forest Service's Fire Lab.The tract Egress Score was derived by intersecting US Census Bureau TIGER/Line feature data with census tract polygon features to generate multipoint features. Because the TIGER/Line data may contain multiple coincident road segments that represent different road names, the multipoint features were dissolved using the unique GEOID and generated as point features. This result was summarized on GEOID and counted. The intersection point counts were joined back to the original tract features using GEOID. The counts were normalized by the area of the tracts and the reciprocal was calculated to get the Egress Score for the tract, higher Egress Score means fewer roads intersecting the tract and greater benefit from the intervention.Related WRI maps include “Where Will Better Air Filtration Improve Wildfire Resilience?”, “Where Will Home Hardening Improve Wildfire Resilience?”, and “Where Will Better Evacuation Routes Improve Wildfire Resilience?”.

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