West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

This dataset was created to monitor the status, condition and trend of national BLM resources in accordance with BLM policies. It focuses on the BLM terrestrial core indicators, which include measures of vegetation and soil condition such as plant species cover and composition, plant height, and soil stability. The BLM terrestrial core indicators and methods were identified through a multi-disciplinary process and are described in BLM Technical Note 440 (https://www.blm.gov/nstc/library/pdf/TN440.pdf). The Landscape Monitoring Framework (LMF) dataset was collect using the Natural Resource Conservation Services (NRCS) National Resource Inventory (NRI) methodology which mirrors the data collected by the BLM using the Monitoring Manual for Grassland, Shrubland, and Savannah Ecosystems (2nd edition; https://www.landscapetoolbox.org/manuals/monitoring-manual/). Specific instructions for data collectors each year the data were collected can be found at https://www.nrisurvey.org/nrcs/Grazingland/. Also see Interpreting Indicators of Rangeland Health (version 5; https://www.landscapetoolbox.org/manuals/iirhv5/). The monitoring locations were selected using spatially balanced, random sampling approaches and thus provide an unbiased representation of land conditions. However, these data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design. General Definitions Noxious: Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Each state’s noxious list can be found in tblStateSpecies Table, where the Noxious field is ‘YES’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Non-Noxious: Non-Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Non-Noxious status can be found in tblStateSpecies Table, where the Noxious field is ‘NO’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Sagebrush: Sagebrush species are designated for each BLM Administrative State using local botany expertise. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘Sagebrush’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Non-Sagebrush Shrub: Non Sagebrush Shrub species are designated for each BLM Administrative State as the plants determined to be shrubs that are not also Sagebrush. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘NonSagebrushShrub’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Tall Stature Perennial Grass: Tall Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘TallStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Short Stature Perennial Grass: Short Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘ShortStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Preferred Forb: Preferred forb for Sage Grouse status was determined for each state by Sage Grouse biologist and other local experts and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘PreferredForb’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Live: The NRI Methods measure Live vs Dead plant cover – i.e. if a pin drop hits a plant part and that plant part is dead (even if it’s on a living plant) that hit is considered a dead hit. Any occurrence of Live Sagebrush calculations indicates that the measurement is only hits that were live plant parts. If a pin hits both a live and a dead plant part in the same pin drop – that hit is considered live. Growth Habit: The form of a plant, in this dataset the options are Forb, Graminoid, Sedge, Succulent, Shrub, SubShrub, Tree, NonVascular. The most common growth habit for each state was determined by local botany expertise often after consulting the USDA plants database. The growth habit for each species is a state can be found in tblStateSpecies in the GrowthHabitSub field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc. Duration: The life length of a plant. In this dataset we consider plants to be either Perennial or Annual – Biennial plants are classified as Annuals. The most common duration for each state was determined by local botany expertise often after consulting the USDA plants database. The duration for each species is a state can be found in tblStateSpecies in the Duration field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc. tblStateSpecies: This table in the database contains the Species Lists for each state. In the instance where a species code does not have a Growth Habit, Growth Habit Sub, or Duration – any occurrence of that code will not be included in calculations that require that information – for example a code that has NonWoody Forb but no information about annual or perennial will not be included in any of the calculations that are perennial or annual forb calculations. Most codes with no information will have the following in the notes – indicating that the only calculation it will be included in is Total Foliar which doesn’t require any growth habit and duration information – “Not used for calculations except Total Foliar.”

This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2022 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Counties are ranked within their state on both health outcomes and health factors. Counties with a lower (better) health outcomes ranking than health factors ranking may see the health of their county decline in the future, as factors today can result in outcomes later. Conversely, counties with a lower (better) factors ranking than outcomes ranking may see the health of their county improve in the future.Some new variables in the 2022 Rankings data compared to previous versions:COVID-19 age-adjusted mortalitySchool segregationSchool funding adequacyGender pay gapChildcare cost burdenChildcare centersLiving wage (while the Living wage measure was introduced to the CHRR dataset in 2022 from the Living Wage Calculator, it is not available in the Living Atlas dataset and user’s interested in the most up to date living wage data can look that up on the Living Wage Calculator website).Data Processing Notes:Data downloaded April 2022Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.Ratios were set to null if negative to make them easier to work with in the map.For demographic variables, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.2010 US boundaries were used as the data contain 2010 US census geographies, for a total of 3,142 counties.

This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. It focuses on the BLM terrestrial core indicators, which include measures of vegetation and soil condition such as plant species cover and composition, plant height, and soil stability. The BLM terrestrial core indicators and methods were identified through a multi-disciplinary process and are described in BLM Technical Note 440 (https://ia800701.us.archive.org/6/items/blmcoreterrestri00mack/BlmCoreTerrestrialIndicatorsAndMethods_88072539.pdf). The Landscape Monitoring Framework (LMF) dataset was collect using the Natural Resource Conservation Services (NRCS) National Resource Inventory (NRI) methodology which mirrors the data collected by the BLM using the Monitoring Manual for Grassland, Shrubland, and Savannah Ecosystems (2nd edition; https://www.landscapetoolbox.org/manuals/monitoring-manual/). Specific instructions for data collectors each year the data were collected can be found at https://grazingland.cssm.iastate.edu/reference-materials. Also see Interpreting Indicators of Rangeland Health (version 5; https://www.landscapetoolbox.org/manuals/iirhv5/). The monitoring locations were selected using spatially balanced, random sampling approaches and thus provide an unbiased representation of land conditions. However, these data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design. General Definitions Noxious: Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Each state’s noxious list can be found in tblStateSpecies Table, where the Noxious field is ‘YES’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Non-Noxious: Non-Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Non-Noxious status can be found in tblStateSpecies Table, where the Noxious field is ‘NO’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Sagebrush: Sagebrush species are designated for each BLM Administrative State using local botany expertise. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘Sagebrush’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Non-Sagebrush Shrub: Non Sagebrush Shrub species are designated for each BLM Administrative State as the plants determined to be shrubs that are not also Sagebrush. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘NonSagebrushShrub’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Tall Stature Perennial Grass: Tall Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘TallStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Short Stature Perennial Grass: Short Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘ShortStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Preferred Forb: Preferred forb for Sage Grouse status was determined for each state by Sage Grouse biologist and other local experts and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘PreferredForb’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’). Live: The NRI Methods measure Live vs Dead plant cover – i.e. if a pin drop hits a plant part and that plant part is dead (even if it’s on a living plant) that hit is considered a dead hit. Any occurrence of Live Sagebrush calculations indicates that the measurement is only hits that were live plant parts. If a pin hits both a live and a dead plant part in the same pin drop – that hit is considered live. Growth Habit: The form of a plant, in this dataset the options are Forb, Graminoid, Sedge, Succulent, Shrub, SubShrub, Tree, NonVascular. The most common growth habit for each state was determined by local botany expertise often after consulting the USDA plants database. The growth habit for each species is a state can be found in tblStateSpecies in the GrowthHabitSub field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc. Duration: The life length of a plant. In this dataset we consider plants to be either Perennial or Annual – Biennial plants are classified as Annuals. The most common duration for each state was determined by local botany expertise often after consulting the USDA plants database. The duration for each species is a state can be found in tblStateSpecies in the Duration field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc. tblStateSpecies: This table in the database contains the Species Lists for each state. In the instance where a species code does not have a Growth Habit, Growth Habit Sub, or Duration – any occurrence of that code will not be included in calculations that require that information – for example a code that has NonWoody Forb but no information about annual or perennial will not be included in any of the calculations that are perennial or annual forb calculations. Most codes with no information will have the following in the notes – indicating that the only calculation it will be included in is Total Foliar which doesn’t require any growth habit and duration information – “Not used for calculations except Total Foliar.”

Consumer Price Index CPI in the United States increased to 323.98 points in August from 323.05 points in July of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset illustrates the cities with the largest wind speed differences. Also included are the city and state, the population, the speed differnce, the ranking, and the inverse ranking (to be used only for mapping purposes). Source: City-Data URL: http://www.city-data.com/top2/c466.html Date Accessed: November 9, 2007

This dataset graphically tracks Foreign Direct Investment in the United States. The dataset covers many types of investment, including manufacturing, trade, and financial aspects. This data covers 2006 figures, and shows which markets are heavily invested in by foreign nations. This data was collected from the Bureau of Economic Analysis : http://www.bea.gov/scb/pdf/2007/07%20July/0707_dip_article.pdf and credit is given to Marilyn Ibarra and Jennifer Koncz. The authors of : Direct Investment Positions for 2006 Country and Industry Detail The data was accessed on October 1, 2007. Statistics are quoted in the Millions.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in the USA. Power Plant emissions from all power plants in the United Staes were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, zip, county, metro area, lat/lon, and plant id for each individual power plant. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/202

This dataset displays the energy prices and expenditures for each of the 50 United States, plus the District of Columbia. Included in the dataset are figures on the prices on a scale with nominal dollars per million Btu. Expenditures in millions of nominal dollars. Expenditures per person in nominal dollars. Hawaii pays the highest in prices, with Texas paying the most in expenditures.

This dataset contains ranks and counts for the top 25 baby names by sex for live births that occurred in California (by occurrence) based on information entered on birth certificates.

FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic or targeted sampling design, structured implementation, standard core methods and indicators, electronic data capture and management, and integration with remote sensing. Each record represents a sample visit during which a suite of the BLM Riparian and Wetland AIM methods were applied, with the geometry marking the center of the plot as taken in the Plot Characterization form. Attributes are the BLM Riparian and Wetland AIM core indicators, which include plot-level measures of vegetation and soil condition such as plant species cover and composition, plant height, and woody structure. In addition, some plots may have some contingent and annual use indicators, including measures of hummock cover and characteristics, water quality, stubble height, soil alteration, and riparian woody use. Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (BLM AIM Wetland Database) at the BLM National Operations Center. Annual Use data (i.e., annual use indicators) are omitted from the public version of these data but can be made available upon request. General Definitions The species list used for data collection was originally developed from a full download of all species in USDA PLANTS shown as occurring in BLM-administered states. The state-level occurrence of species in this list have been adjusted over time as individual species were found to be missing from individual state lists. Traits used in indicator calculations for all species observed at a given monitoring plot can be found in the I_SpeciesIndicator feature service, where the traits are listed by plant. A full species list can also be provided by request by the National Riparian and Wetland AIM Team. Once finalized, it will be added to the WetlandAIM database, likely in spring of 2024. In general, traits are assigned at the species-level. Genera and family-level records were only given trait values if all species within that taxonomic group were considered to have one trait (e.g., all species of Tamarix are nonnative, so the genus level code is also considered nonnative). To assign Growth Habit and Duration to unknown plants, information recorded in the Unknown Plants form was used to fill in traits. For example, if a plant was identified as a Carex species (unknown code CAREX_01), the growth habit (graminoid) would be taken from the full species list since all Carex species are graminoids, and the duration would be taken from the plot-specific matching entry in Unknown Plants. Nativity Status: The nativity status of all species was taken from the USDA Plants Database and was ranked at a national scale. All plants identified to species are ‘Native’, ‘Nonnative’, or ‘Cryptogenic’. The term cryptogenic refers to species with both native and nonnative genotypes. Genera and family-level plants were only given a nativity status if all species within that taxonomic group were considered either native or nonnative (e.g. all species of Tamarix are nonnative, so the genus level code is also considered nonnative). Noxious: Noxious status are designated for each political state (i.e. StateCode) developed using the most recent state noxious list available online. Wetland Indicator Status: Wetland Indicator Status was taken from the U.S. Army Corps of Engineers’ National Wetland Plant List (NWPL 2020, version 3.5; https://wetland-plants.usace.army.mil/). Plants are ranked by ecoregion into one of the following rating categories based on an estimated frequency with which it is thought to occur in wetlands: obligate (OBL), facultative wetland (FACW), facultative (FAC), facultative upland (FACU), or upland (UPL), The five rating categories were first developed through an exhaustive review of the botanical literature and best professional judgement of national and regional experts, and has since undergone multiple rounds of revision by a national panel. C-Values: Coefficients of Conservatism (C-values) are assigned to species by a panel of experts, typically at a state level. C-values range from 0 to 10 and represent an estimated probability that a plant is likely to occur in a landscape relatively unaltered from pre-European settlement conditions (see table of C-Value Interpretation below). The Mean C-value is calculated at a plot level by averaging the C-values of all species in a given plot. Mean C-value is a stand-alone indicator of floristic quality, one of several indicators under the Floristic Quality Assessment (FQA) approach to assesses the degree of "naturalness" of an area. C-Value Interpretation 0 = Non-native species. Very prevalent in new ground or non-natural areas 1-3 = Commonly found in non-natural areas 4-6 = Equally found in natural and non-natural areas 7-9 = Obligate to natural areas but can sustain some habitat degradation 10 = Obligate to high-quality natural areas (relatively unaltered from pre-European settlement) C-values were compiled from several sources, listed below. CO = Smith, P., G. Doyle, and J. Lemly. 2020. Revision of Colorado’s Floristic Quality Assessment Indices. Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado. MT = Pipp, Andrea. 2017. Coefficient of Conservatism Rankings for the Flora of Montana: Part III. Report to the Montana Department of Environmental Quality, Helena, Montana. Prepared by the Montana Natural Heritage Program, Helena, Montana. 107 pp. WA = Rocchio, F.J, and R. Crawford. 2013. Floristic Quality Assessment for Washington Vegetation. Washington Natural Heritage Program, Washington Department of Natural Resources, Olympia, WA. (Values for Eastern Washington used). WY = Washkoviak L, B. Heidel, and G. Jones. 2017. Floristic Quality Assessment for Wyoming Flora: Developing Coefficients of Conservatism. Prepared for the U.S. Army Corps of Engineers. The Wyoming Natural Diversity Database, Laramie, Wyoming. 13 pp. plus appendices. AZ, CA, ID, NM, NV, OR, UT = Great Lakes Environmental Center (GLEC), Inc. and M.S. Fennessy. 2019. Project to Assign C-Values to Western State for use in the USEPA National Wetland Condition Assessment. Great Lakes Environmental Center, Traverse City, MI. Live: The Core Methods measure Live vs. Standing Dead plant cover, i.e., if a pin drop hits a dead plant part (even if it’s on a living plant), that hit is considered a dead hit. If a pin hits both a live and a dead plant part in the same pin drop, that hit is considered live. Growth Habit: The form of a plant. In this dataset, plants are either Forb, Graminoid, Shrub, Tree, and, in Alaska only, Liverwort, Moss, Hornwort, and Lichen. Growth habitat was derived from USDA PLANTS. If more than one growth habit was designated in USDA PLANTS, the most common growth habit was determined by consulting the USDA plants database and other literature and was applied across all states where it occurs. Graminoids include all grasses, rushes, sedges, arrow grasses, and quillworts (Poaceae, Cyperaceae, Juncaceae, Juncaginaceae, and Isoetes). Forbs include vascular, non-woody plants, but exclude graminoids. Shrubs are defined as perennial multi-stemmed woody plants usually less than 4-5 m in height. Trees are generally perennial woody plants with a single stem, normally greater than 4 to 5 m in height. Duration: The life length of a plant. In this dataset, plants are either Perennial or Annual. Biennial plants are classified as Annuals. Duration was derived from USDA PLANTS. If more than one duration was designated in USDA PLANTS, the most common duration for each state was determined by consulting the USDA plants database and applied across all administrative states where it occurs. Nonvasculars: Nonvascular species were not included in LPI data collection in the lower-48 except as generic “non-plant” codes. In Alaska, a full list of nonvascular species from the Alaska Vegetation Plots Database (https://akveg.uaa.alaska.edu/) including mosses, hornworts, liverworts, and lichens was used during data collection. In terms of indicator calculations, nonvasculars were not included in plot-level plant counts and cover (i.e. cover of various plant trait categories like nativity, duration, or growth habit), but were instead transferred into the simplified non-plant codes to be calculated into moss and lichen cover indicators. Cover by species of these nonvasculars can be found in the SpeciesIndicators table. Preferred Forbs: A set of specific forb species that are preferred by Sage Grouse birds. State preferred forb lists were developed by state botanists in collaboration with wildlife and sage-grouse experts and were based on a combination of peer reviewed literature and local knowledge. These lists were then combined to create one national list.