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?”.
Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.
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
April 20, 2020
April 29, 2020
September 1st, 2020
February 12, 2021
new_deaths
column.February 16, 2021
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.
Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic
Filter cases by state here
Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac
Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true
Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.
Pull the 100 counties with the highest per-capita confirmed cases here
Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.
The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.
@(https://datawrapper.dwcdn.net/nRyaf/15/)
<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>
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
This data should be credited to Johns Hopkins University COVID-19 tracking project
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Key information about Malaysia Household Income per Capita
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
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:
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.
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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?”.