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.

This map of human habitation was developed, following a modification of Schumacher et al. (2000), by incorporating 2000 U.S Census Data and land ownership. The 2000 U.S. Census Block data and ownership map of the western United States were used to correct the population density for uninhabited public lands. All census blocks in the western United States were merged into one shapefile which was then clipped to contain only those areas found on private or indian reservation lands because human habitation on federal land is negligible. The area (ha) for each corrected polygon was calculated and the 2000 census block data table was joined to the shapefile. In a new field, population density (individuals/ha) corrected for public land in census blocks was calculated . SHAPEGRID in ARC/INFO was used to convert population density values to grid with 90m resolution.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

This is a map of populated areas with population density greater than or equal to 1 individual/ ha (i.e., rural/exurban but including suburban and urban as defined by Marzluff et al. 2001) as determined from U.S. Census data corrected for public lands.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Glacier Lakes Ecosystems Experiments Site (GLA) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Shortgrass Steppe (SGS) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The average American was responsible for emitting 13.8 metric tons of carbon dioxide (tCO₂) in 2023. U.S. per capita fossil CO₂ emissions have fallen by more than 30 percent since 1990. Global per capita emission comparisons Despite per capita emissions in the U.S. falling notably in recent decades, they remain roughly three times above global average per capita CO₂ emissions. In fact, the average American emits more CO₂ in one day than the average Somalian does throughout the entire year. Additionally, while China is now the world’s biggest emitter, the average Chinese citizen’s annual carbon footprint is roughly half the average American’s. Which U.S. state has the largest carbon footprint? Per capita energy-related CO₂ emissions in the U.S. vary greatly by state. Wyoming was the biggest CO₂ emitter per capita in 2022, with 97 tCO₂ per person. The least-populated state’s high per capita emissions are mainly due to its heavily polluting coal industry. In contrast, New Yorkers had the one of the smallest carbon footprints in 2022, at less than nine tCO₂ per person.

1Mean well density in 2009 within a 20 km radius of northeast Wyoming leks.2Maximum well density in 2009 within a 20 km radius of northeast Wyoming leks.3Permitting levels for well density on United States federal land.

Idaho had one of the largest per capita uses of the public water supply in the United States, totaling 184 gallons per day, followed by Utah with 169 gallons and Wyoming at 156 gallons. The public supply of water refers to water that is withdrawn by both public and private suppliers and is delivered to domestic, commercial, thermoelectric, irrigation, and industrial users. Overall, the most populous states tend to be the largest consumers of water. Sources of public supply water can include desalinated seawater and treated brackish groundwater. California and Texas withdrew 5.15 billion gallons and 2.89 billion gallons per day, respectively, for public supply in 2015. Almost 90 percent of the U.S. population relies on public water supplies.

U.S. Water Consumption Water withdrawal in the United States has increased over the last decades, reaching 322 billion gallons per day in 2015. The U.S. is one of the largest per capita consumers water in the world, in addition to being one of the largest absolute consumers of water. The average U.S. family uses some 400 gallons of water per day. However, a large share of water is lost or wasted through leaky pipes or just evaporation and over-watering landscapes. Minor changes such as fixing a leaky faucet, using a dishwasher, upgrading to a water-efficient toilet, or taking showers instead of baths can help save conserve water.

The distribution of spatial genetic variation across a region can shape evolutionary dynamics and impact population persistence. Local population dynamics and among-population dispersal rates are strong drivers of this spatial genetic variation, yet for many species we lack a clear understanding of how these population processes interact in space to shape within-species genetic variation. Here, we used extensive genetic and demographic data from 10 subpopulations of greater sage-grouse to parameterize a simulated approximate Bayesian computation (ABC) model and (i) test for regional differences in population density and dispersal rates for greater sage-grouse subpopulations in Wyoming, and (ii) quantify how these differences impact subpopulation regional influence on genetic variation. We found a close match between observed and simulated data under our parameterized model and strong variation in density and dispersal rates across Wyoming. Sensitivity analyses suggested that changes in ...

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Glacier Lakes Ecosystems Experiments Site (GLA) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Glacier Lakes Ecosystems Experiments Site (GLA) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

NER draft CCP goals call for maintenance of denning wolves on NER to facilitate elk population reduction objectives. The State of Wyoming assumed wolf management authority as of 10/1/12, and wolf hunting now occurs immediately adjacent to NER. Demographic monitoring of NER wolves will help ensure that CCP goals and objectives are met.

Currently there is no information on the effects of wolves on elk aggregation and density patterns in a feedground environment, and this study will provide important information on these effects. Elk aggregation and density data will also be used to support ongoing disease monitoring and disease contingency planning efforts identified in the 2007 Bison and Elk Management Plan and EIS.

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Wyoming. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Wyoming. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Wyoming. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7VM499V; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Wyoming. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Wyoming. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Wyoming. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7VM499V

This CSV file contains cumulative fish habitat condition index (HCI) scores generated for river reaches of the conterminous United States as well as indices generated specifically for four spatial units including local and network catchments and 90 m local and network buffers of river reaches. Note that the cumulative HCI score is determined from limiting index scores generated for the four spatial units listed above. Detailed methods for calculating cumulative fish habitat condition index scores as well as the indices for each spatial extent can be found on the following website: http://assessment.fishhabitat.org/: The variables used to create indices in catchments vs. buffers differ due to differences in resolution of datasets. The following anthropogenic disturbance variables were used to create local and network catchment indices: Percent of urban land use, percent of impervious surface, human population density, road density, percent of pasture/hay, percent of cultivated crops, density of point source pollution sites (National Pollution Discharge Elimination, Toxic Inventory Release and National Superfund), nutrient and sediment loading to watersheds, habitat fragmentation metrics (density of dams and road crossings), density of mines and water withdrawals. The following anthropogenic disturbance variables were analyzed to create the local and network buffer indices: percent of urban land use, percent of agriculture, percent of pasture/hay and percent of impervious surface. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment and buffer units. Also included in this CSV file are the most limiting and severe disturbances to stream reaches operating within each of the four spatial extents. Limiting disturbances are defined as those disturbances that result in a stream reach not being in the best available condition determined for the region. Severe disturbances are a subset of limiting disturbances that are associated with stream reaches in a given region that were scored as having high or very high risk of habitat degradation (red and orange color groups). In this data set, indices as well as limiting and severe disturbances are linked to the stream reaches, catchments and buffers created for the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. It is important to recognize that these broadly-defined disturbance variables often act together with other measured or unmeasured threats to degrade habitat. Thus, while we may identify “urbanization” as a major threat to fish habitat in some regions, “urbanization” represents an umbrella term that describes many facets of urban development that could cause degradation to habitats. Fields in this dataset that begin with the "L_" prefix represent the local catchment whereas network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix. Like the catchment variables the buffer variables are labeled using a "LB_" and "NB_" prefix for local buffer and network buffer variables, respectively. More information about the processes used to create scores can be found in the processes section. Version 2.0 includes the addition of severe disturbances for each spatial scale and fixes errors documented in the change log.