This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

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.

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.

Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).

This study measured the population dynamics of coyotes in the grasslands and creosote shrublands of McKenzie Flats, Sevilleta National Wildlife Refuge. The study was begun in January, 1992, and continued quarterly each year. Coyotes were sampled via scat counts along the roads of McKenzie Flats during winter, spring, summer, and fall of each year. The entire road transect was 21.5 miles in length. Scat counts over a week period (number of scats/mile/day) in each season along the roads were used to calculate the densities of coyotes (number of coyotes per square kilometer). Results from 1992 to 2002 indicated that autumn was the peak density period of the year, with generally steady declines through the year until the following autumn. Coyote populations appeared to fluctuate seasonally, but remained relatively stable at 0.27 +/- 0.03 (SE) coyotes per km2 during summer periods (this likely represents the "breeding pair" density, during which coyote pairs have set up territories and are raising young, but the pups have not as yet joined the parents in foraging activities).

This study measured the population dynamics of black-tail jackrabbits (Lepus californicus) and desert cottontail rabbits (Sylvilagus auduboni) in the grasslands and creosote shrublands of McKenzie Flats, Sevilleta National Wildlife Refuge. The study was begun in January, 1992, and continued quarterly each year. Rabbits were sampled via night-time spotlight transect sampling along the roads of McKenzie Flats during winter, spring, summer, and fall of each year. The entire road transect was 21.5 miles in length. Measurements of perpendicular distance of each rabbit from the center of the road were used to estimate densities (number of rabbits per square kilometer) via Program DISTANCE. Results from 1992 to 2002 indicated that spring was the peak density period of the year, with generally steady declines through the year until the following spring. Evidence of a long-term "cycle" (e.g., the 11 year cycle reported for rabbits in the Great Basin Desert) did not appear in the Sevilleta rabbit populations.

In 1800, the present-day region of Mexico had a population of just over six million people. Mexico gained its independence from the Spanish crown in 1821, and population growth remained steady for the next 85 years. Growth then halted with with the Panic of 1907, an American financial crisis whose ripple effects in Mexico would set the stage for the Mexican Revolution in 1910. This revolution would see population flatline at just over fifteen million between 1910 and 1920, as widespread conflict and result in the death of between 1.7 to 2.7 million over the decade, and the coinciding 1918 Spanish Flu epidemic would see the loss of another 300,000 in this time period. Following the end of both the Mexican Revolution and the Spanish Flu epidemic in 1920, the population of Mexico would begin to increase rapidly as modernization would see mortality rates fall and standards of living rise throughout the country. This growth has continued steadily into the 21st century, and in 2020, Mexico is estimated to have a population of just under 129 million.

Future county population was based on projections for 2100 from the Spatially Explicit Regional Growth Model (SERGoM; Theobald 2005). SERGoM simulates population based on existing patterns of growth by census block, groundwater well and road density, and transportation distance to urban areas, while constraining the pattern of development to areas outside of protected areas and urban areas (Theobald 2005). The dataset here is a projection for a “baseline” growth scenario that assumes a similar trajectory to that of current urban growth (Bierwagen et al. 2010). SERGoM accuracy is estimated as 79–99% when compared to 1990 and 2000 census data, with the accuracy varying by urban/exurban/rural categories and increasing slightly with coarser resolution (Theobald 2005). The accuracy of future model predictions with different economic scenarios is most sensitive to fertility rates, which are subject to cultural change, economic recessions, and the current pattern of lands protected from development (Bierwagen et al. 2010). Bierwagen, B. G., D. M. Theobald, C. R. Pyke, A. Choate, P. Groth, J. V. Thomas, and P. Morefield. 2010. National housing and impervious surface scenarios for integrated climate impact assessments. Proceedings of the National Academy of Sciences of the United States of America 107:20887-20892. Theobald, D. M. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10: article 32.

Detailed description of study areas and methods used to test predictions.

In Mexico, the number of practicing doctors amounted to **** professionals per 1,000 inhabitants in 2023, an increase compared to the figures reported a year earlier when there were **** practicing physicians per every thousand people. During 2022, the number of physicians in Mexico totaled approximately ******* professionals. Density of doctors worldwide In a global comparison, Mexico ranks in a middle category for density of medical doctors per 1,000 population, similar to Canada and Colombia. Among the countries in the upper bracket for highest density of doctors are Cuba, Sweden, Belgium, and Uruguay. Along with Mexico’s moderate density of doctors, over ** percent of the population was considered vulnerable due to lack of access to health services in Mexico as of 2022, up from around **** percent a decade earlier. Health care in Mexico Nearly ** ******* people in Mexico held public health insurance through Seguro Popular in 2020, which was replaced by a new institution at the beginning of that year, called INSABI (Instituto Nacional de Salud para el Bienestar). However, the IMSS (Instituto Mexicano del Seguro Social) led by a large margin as the largest provider of health insurance in the North American country.

The Bipartisan Infrastructure Law tasked NTIA with defining “high-cost areas” as part of the BEAD allocation formula. This task required NTIA to both define “high-cost" and define “area” (the size of the geographic area in which to assess whether 80% of the locations are unserved and to measure whether the cost is higher when compared to the average cost for all other similarly-defined unserved areas).NTIA defined “high cost” using a cost model that incorporates an area’s remoteness, population density, topography, and poverty levels, and measures costs over the life of the network. NTIA defined “area” to mean census block groups.Importantly, this definition of “high-cost area” also determines which households may be eligible for an increased ($75) benefit in the FCC’s ACP program. As a result, NTIA carefully weighed poverty in defining cost given ACP-eligibility and worked closely with the FCC to ensure that the high-cost area definition did not impede the administrability of that benefit. For full details on the methodology go to BEAD Allocation Methodology | Internet for All (internet4all.gov)

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of New Mexico. 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 New Mexico. 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 New Mexico. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F79S1P1F

This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sev/23/121705. The abstract below was extracted from the Level 0 data package and is included for context: This study explores the population dynamics of black-tail jackrabbits (Lepus californicus) and desert cottontail rabbits (Sylvilagus auduboni) in the grasslands and creosote shrublands of McKenzie Flats, Sevilleta National Wildlife Refuge. The study was initiated in January 1992, and continues quarterly each year. Rabbits are sampled via night-time spotlight transect sampling along the roads of McKenzie Flats once during winter, spring, summer, and fall. The route is 21.5 miles long. Measurements of perpendicular distance of each rabbit from the center of the road are used to estimate densities (number of rabbits per square kilometer) via Program DISTANCE. Results from January 1992 to May 2004 indicated that spring was the period of peak density period, with generally steady declines through the rest of the year until the following spring. Evidence of a long-term "cycle" (e.g., the 11-year-cycle reported for rabbits in the Great Basin Desert) does not appear in the Sevilleta rabbit populations.

Development of predictions for the impact of the landscape of fear for parallel effects between competing species.

In 2006, to obtain a measure of pinon and juniper biomass in the Cerro Montosa area, belt transects were superimposed on transects along which understory net primary producitivy (NPP) is sampled. All trees rooted within 5 m to the north of each belt were tagged, although some shorter belts exist on which all trees within 5 m to either the north or south were tagged. The height of each tagged tree was measured, as was the diameter-at root-crown (DRC). Crown diameters both parallel and perpendicular to the belt transect were also measured. Trees were re-measured in 2007, 2008, and 2009.

This site contains data files and model code for a dynamic nesting territory occupance model and 2-sex integrated population model for Cooper's hawks in Albuquerque, New Mexico, USA, 2011 - 2020.

Map prepared by Srini Vasan; data for liquor store locations supplied by Tom Scharman

Genetic founder effects are often expected when animals colonize restored habitat in fragmented landscapes, but empirical data on genetic responses to restoration are limited. We examined the genetic response of banner-tailed kangaroo rats (Dipodomys spectabilis) to landscape-scale grassland restoration in the Chihuahuan Desert of New Mexico, USA. D. spectabilis is a grassland specialist and keystone species. At sites treated with herbicide to remove shrubs, colonization by D. spectabilis is slow and populations persist at low density for ≥10 yrs (≥6 generations). Persistence at low density and low gene flow may cause strong founder effects. We compared genetic structure of D. spectabilis populations between treated sites and remnant grasslands, and we examined how the genetic response to restoration depended on treatment age, area, and connectivity to source populations. Allelic richness and heterozygosity were similar between treated sites and remnant grasslands. Allelic richness at treated sites was greatest early in the restoration trajectory, and genetic divergence did not differ between recently colonized and established populations. These results indicated that founder effects during colonization of treated sites were weak or absent. Moreover, our results suggested founder effects were not mitigated by treatment area or connectivity. Dispersal is negatively density-dependent in D. spectabilis, and we hypothesize that high gene flow may occur early in the restoration trajectory when density is low. Our study shows genetic diversity can be recovered more rapidly than demographic components of populations after habitat restoration, and that founder effects are not inevitable for animals colonizing restored habitat in fragmented landscapes.

The endangered Rio Grande silvery minnow persists as a remnant population in a highly fragmented and regulated arid-land river system. The species is subject to dramatic fluctuations in density. Since 2003, the wild population has been supplemented by hatchery-reared fish. We report on a 12-year (1999 – 2010) monitoring study of genetic diversity and effective population size (Ne) of wild and hatchery stocks. Our goals were to evaluate how genetic metrics responded to changes in wild fish density and whether they corresponded to the number and levels of diversity of hatchery-reared repatriates. Genetic diversity and all measures of Ne in the wild population did not correlate with wild fish density until hatchery supplementation began in earnest. Estimates of variance and inbreeding effective size were not correlated. Our results suggest source-sink dynamics where captive stocks form a genetically diverse source and the wild population behaves as a sink. Nevertheless, overall genetic diversity of silvery minnow has been maintained over the last decade and we attribute this to a well designed and executed propagation management plan. When multiple factors like environmental fluctuation and hatchery supplementation act simultaneously on a population, interpretation of genetic monitoring data may be equally complex and require considerable ecological data.

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 subpopulations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn subpopulations declined in abundance. Sixteen subpopulations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn subpopulations would experience increased temperatures, while the direction and magnitude of precipitation had high subpopulation-specific variation. Models predicted that nine subpopulations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn subpopulations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere. Long-term data from annual aerial surveys of pronghorn subpopulations in Utah, Arizona, New Mexico, and western Texas were used to calculate annual rates of population growth (λ). When subpopulation-specific harvest and translocation data were available, population estimates for calculating λ were adjusted according to the following equation: λt = Nt/(Nt-1 - h - r + a), where λt is population change from time t-1 to t, Nt and Nt-1 are population estimates from current and previous surveys, respectively, h is number of pronghorn harvested, and r and a are number of individuals removed from and released into the population, respectively, through translocations. Only population estimates from surveys conducted in consecutive years were used to calculate λ. If λ = 2, the associated surveys were removed from analyses because λ would be considered to be derived from unreliable or unstandardized population estimates, resulting in biologically unrealistic population growth rates. Monthly climate data (precipitation [mm/day] and mean temperature [degrees C]) were from 14 x 14 km cells from pronghorn range in each subpopulation in Utah, Arizona, New Mexico, and western Texas. Means across grids were calculated to obtain monthly values of precipitation and temperature. Two realistic future global climate scenarios were compared; a lower (Representative Concentrations Pathways 4.5) and a high (Representative Concentrations Pathways 8.5) atmospheric CO2 concentration scenario. Standardized precipitation index for 3-, 6-, 12-, and 24-month periods were calculated from all available monthly precipitation data using program SPI SL 6 (National Drought Mitigation Center 2014). Monthly mean temperature, total precipitation, and mean SPI (3-, 6-, and 12-month periods) were summarized by important periods in an adult female pronghorn's annual reproductive cycle relative to peak fawning (i.e., early, mid-, and late gestation [3 months each] and lactation [4 months]). Mean temperature and total precipitation were also calculated for 12 and 24 months preceding each population survey. Historic pronghorn population trends in relation to temperature and precipitation were assessed using integrated Bayesian population models. All models included a covariate for density effect (i.e., population in the previous year). Precipitation and temperature model comparison sets were run separately, and each model set included a null model (i.e., only density covariate, no climate covariates). These top individual precipitation and temperature covariates were then combined in models (i.e., one precipitation and temperature covariate per model), and these combined models were run including a term for the interaction between precipitation and temperature using the following equation: ln(λt) = Alpha + Beta1XN[t-1] + Beta2Xprec + Beta3Xtemp + Beta4Xprec*temp. Projected climate data for each pronghorn subpopulation was used to predict λt for each year to 2090. An integrated modeling approach was used, whereby the best performing model climatic predictors from historic population trends for each pronghorn subpopulation was embedded in that subpopulation pronghorn population projection model.