Reference Layer: Bureau of Labor Statistics Monthly Unemployment (latest 14 months)_This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: August 2022 (preliminary values at the county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: October 21, 2022Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2021 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.To better understand the different labor force statistics included in this map, see the diagram below from BLS:

This map shows how unemployment has changed in the US over the past month. This can be seen by counties within this map. The map shows if a county's unemployment has increased or decreased, and by how much. The map always represents the most current figures offered by BLS, and updates automatically. To see the most current month offered by BLS, find the CurrentMonth attribute in the data table, or visit the metadata for the Living Atlas layer used in this map.Bureau of Labor Statistics (BLS):https://www.bls.gov/Local Area Unemployment Statistics (LAUS):https://www.bls.gov/lau/Click here to view a map showing the change in unemployment rate.

The Occupational Employment Statistics (OES) program conducts a semiannual survey designed to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. The Bureau of Labor Statistics produces occupational employment and wage estimates for approximately 415 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and selected 5- and 6-digit North American Industry Classification System (NAICS) industrial groups. The OES program surveys approximately 200,000 establishments per panel (every six months), taking three years to fully collect the sample of 1.2 million establishments. To reduce respondent burden, the collection is on a three-year survey cycle that ensures that establishments are surveyed at most once every three years. The estimates for occupations in nonfarm establishments are based on OES data collected for the reference months of May and November. The OES survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OES survey make these estimates possible. The OES features several arts-related occupations, particularly in the Arts, Design, Entertainment, Sports, and Media Occupations group (Standard Occupational Classification (SOC) code 27-0000). Several featured occupation groups include the following: Art and Design Workers (SOC 27-1000) Art Directors Fine Artists, including Painters, Sculptors, and Illustrators Multimedia Artists and Animators Fashion Designers Graphic Designers Set and Exhibit Designers Entertainers and Performers, Sports and Related Workers (SOC 27-2000) Actors Producers and Directors Athletes Coaches and Scouts Dancers Choreographers Music Directors and Composers Musicians and Singers Media and Communication Workers (SOC 27-3000) Radio and Television Announcers Reports and Correspondents Public Relations Specialists Writers and Authors Data for years 1997 through the latest release and can be found on the OES Data page. Also, see OES News Releases sections for current estimates and news releases. Users can analyze the data for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. As well, OES Charts are available. Users may also explore data using OES Maps. If preferred, data can also be accessed via the Multi-Screen Data Search or Text Files using the OES Databases page.

This map shows three things: which counties are experiencing high unemployment (indicated by the sad faces)whether unemployment is worsening or getting better since a year ago (indicated by color)the number of unemployed people in the most recent month (indicated by size)The map always represents the most current figures offered by BLS, and updates automatically twice a month. To see the most current month offered by BLS, find the CurrentMonth attribute in the data table, or visit the metadata for the Living Atlas layer used in this map.Click on any county to see full details on the percent unemployed and count of unemployed people over the past 14 months. The pop-up's Arcade expressions compare the most recent month's data to that from 12 months prior. This helps with any seasonal variation that might occur within a year.Bureau of Labor Statistics (BLS):https://www.bls.gov/Local Area Unemployment Statistics (LAUS):https://www.bls.gov/lau/

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

This multi-scale map contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The unemployment rate is the ratio of individuals who are unemployed (those not working and actively looking for work in the past 4 weeks, and are currently ready and available to start work) to individuals in the labor force (employed + unemployed).The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The map always represents the most current figures offered by BLS, and updates automatically. To see the most current month offered by BLS, click on a pop-up. You can also find the CurrentMonth attribute in the layer's data table, or visit the the Living Atlas layer used in this map.Bureau of Labor Statistics (BLS):https://www.bls.gov/Local Area Unemployment Statistics (LAUS):https://www.bls.gov/lau/

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic.Most current month: October 2024 (preliminary values at the county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from theU.S. Bureau of Labor Statistics.Data downloaded: December 20, 2024Local Area Unemployment Statistics table download:https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact.The data is joined to 2021TIGER boundariesfrom theU.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.To better understand the different labor force statistics included in this map, see the diagram belowfrom BLS:

Extract from the Quarterly Census of Emploment and Wages (QCEW). More info available at: https://www.bls.gov/cew/downloadable-data-files.htm

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: July 2025 (preliminary values at the state and county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: August 27, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and County NationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova. As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

This web map powers the Public Safety Coverage webmap application that enables users to search various GIS data. Information is updated on a weekly basis. Please contact gisadmin@co.crawford.pa.us for any questions, edits, or issues with this application.Additional maps can be found at our GIS landing page.

This map shows the most current unemployment figures in the USA by states and counties. This is seen in two ways:The darkest red areas in the map have the highest unemployment rate (% of labor force)The LARGEST SYMBOLS have the highest count of unemployed peopleThe color pattern is reinforced behind the centroid layer in order to show the pattern by the boundaries. The figures shown are the most recent monthly figures provided by the Bureau of Labor Statistics (BLS), and the data updates behind the scenes automatically each month.The map uses the Bureau of Labor Statistics Monthly Unemployment layer from ArcGIS Living Atlas of the World. To find other web maps that show how unemployment looks in the US right now, check out the collection of maps created from this layer.

A three-dimensional (3D) map of the Cooper Basin region has been produced from 3D inversions of Bouguer gravity data using geological data to constrain the inversions. The 3D map delineates regions of low density within the basement of the Cooper/Eromanga Basins that are inferred to be granitic bodies. This 3D data release constitutes the second version of the 3D map of the Cooper Basin region. It builds on Version 1 of the Cooper Basin Region Geological map, released in 2009.

The Cooper Basin region is coincident with a prominent geothermal anomaly and forms part of a broad area of anomalously high heat flow. High-heat-producing granites, including granodiorite of the Big Lake Suite (BLS) at the base of the Cooper and Eromanga Basins sequences combined with thick Cooper/Eromanga sedimentary sequences that provide a thermal blanketing effect, result in temperatures as high as 270° C at depths <5 km. The location and characteristics of other granitic bodies are poorly understood and accurately identifying them is an important first step towards future geothermal exploration in this region.

3D Bouguer gravity field inversion modelling was carried out using the UBC inversion software. An initial gravity inversion was performed using seismic horizons to constrain the 3D distribution of the Cooper/Eromanga Basin sediments. Densities, derived from seismic velocities from a refraction seismic survey in the region, were assigned to the Cooper/Eromanga sediments in order to constrain their gravity contribution. A series of Iso-surfaces were generated, enclosing low density lobes within the basement of the initial sediment-constrained inversion model. Gravity 'worms' were used to pick the iso-surfaces that approximate the lateral sub-sediment extent of potential granites within the basement. A series of subsequent granite-constrained inversions were generated by assigning different maximum cut-off depths to the lobes. The inversion model that produced the most 'neutral' result had a maximum cut-off depth of 10 km.

The 3D map was then used to predict temperatures throughout the volume of the map. Thermal properties were sourced from the literature and from direct measurements. Forward predictions of temperatures were carried out using the Simulator for HEat and MAss Transport (SHEMAT) software package. Thermal properties were iteratively updated until a satisfactory match was achieved between the model and temperature measurements. The resulting temperature distribution gives strongly elevated temperatures over the BLS, as well as broader regions of elevated temperature in the northwest of the study area toward Mt Isa, under the Adavale Basin in the north-east of the study area, and south-east of the BLS.

Uncertainty was analysed using a stochastic modelling technique. A sensitivity analysis was first performed to select the parameters which, when varied, had the greatest effect on the predicted temperatures. These parameters are: thermal conductivity of the basin sediments, heat production of the basement and granite units, and basal heat flux. Stochastic models were then run, giving the standard deviation of the temperature at each point in the model. The resulting standard deviation distribution shows that areas of highest predicted temperature are also areas of highest error. However, when the standard deviation values are converted to percentage error, a different pattern emerges: Highest error values are observed where the Cooper Basin sediments are thickest. Lower error values are observed over the BLS and in the southeast of the model area.

This layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC).

This layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24021Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

Graph and download economic data for Income Before Taxes: Income Before Taxes by Highest Education: College Graduate: Total (CXUINCBEFTXLB1407M) from 2012 to 2023 about tertiary schooling, education, tax, income, and USA.

This dataset contains Basic Life Support (BLS) Response Zones (service areas) polygon geometry and attributes, for Chester County, Pennsylvania.

This map service was created to support the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management’s (OCM) Coastal Flood Exposure Mapper. The purpose of the online mapping tool is to provide coastal managers, planners, and stakeholders a preliminary look at exposures to coastal flooding hazards. The Mapper is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help communities initiate resilience planning efforts. As with all remotely sensed data, all features should be verified with a site visit. The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes. For more information, visit the Coastal Flood Exposure Mapper (https://coast.noaa.gov/floodexposure).Send questions or comments to the NOAA Office for Coastal Management (coastal.info@noaa.gov).

This app contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: March 2021 (preliminary values at the state and county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2019 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.To better understand the different labor force statistics included in this map, see the diagram below from BLS:Esri's U.S. Updated Demographic Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Data Note: The median household income value divides the distribution of household income into two equal parts. Pareto interpolation is used if the median falls in an income interval other than the first or last. For the lowest interval, <$10,000, linear interpolation is used. If the median falls in the upper income interval of $500,000+, it is represented by the value of $500,001. Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer shows figures of quit rates and quit levels by the US, BLS regions, and states. Data is from the Bureau of Labor Statistics (BLS) and was released October and November of 2021. The layer default symbology highlights to September 2021 quit rate in comparison to the national figure of 3.0%.According to the October 2021 News Release by BLS:"The number of quits increased in August to 4.3 million (+242,000). The quits rate increased to a series high of 2.9 percent. Quits increased in accommodation and food services (+157,000); wholesale trade (+26,000); and state and local government education (+25,000). Quits decreased in real estate and rental and leasing (-23,000). The number of quits increased in the South and Midwest regions."In the following November News Release:"In September, quits rates increased in 15 states and decreased in 10 states. The largest increases in quits rates occurred in Hawaii (+3.8 percentage points), Montana (+1.5 points), as well as Nevada and New Hampshire (+1.1 points each). The largest decreases in quits rates occurred in Kentucky (-1.1 percentage points), Iowa (-1.0 point), and South Dakota (-0.7 point). Over the month, the national quits rate increased (+0.1 percentage point)."Quit rates: The quits rate is the number of quits during the entire month as a percent of total employment.Quit levels: Quits are the number of quits during the entire month.State and US figures: Table 4. Quits levels and rates by industry and region, seasonally adjustedRegion figures: Table 4. Quits levels and rates by industry and region, seasonally adjustedThis data was obtained in October and November 2021, and the months of data from BLS are as follows:August 2020September 2020April 2021 (only offered for Regions)May 2021June 2021July 2021August 2021September 2021 (preliminary values)For the full data release, click here.The states (including the District of Columbia) that comprise the regions are: Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and VermontSouth: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West VirginiaMidwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and WisconsinWest: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.