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) Database (MTDB). The MTDB 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 as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) 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 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. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The urban areas created by the US Census Bureau "represent densely developed territory, and encompass residential, commercial, and other nonresidential urban land uses. Each urban area must encompass at least 2,000 housing units or at least 5,000 people." They were created by the Bureau following the 2020 census based on the population and housing unit density of individual blocks. The 2020 Census increased the urban population and housing unit count requirements for the first time since 1910, resulting in differences between the 2010 and 2020 boundaries beyond those resulting from population change. In addition, they also stopped distinguishing between "urbanized areas" and "urban clusters" and renamed areas as needed to reflect the updated boundaries. Please see the US Census Bureau website for more information.

Last Update: 02/2021This datasets was was downloaded from the 2020 Census Redistricting Data (P.L. 94-171) page. All 2020 census boundaries are current to January 1, 2020. The Census Bureau will release the first set of corresponding demographic data in September 2021 (the 2020 Census Redistricting P.L. 94-171 Summary Files). Following that release, AGRC will append the demographic data to the existing 2020 geographies served on this page.Census tracts are small, relatively permanent subdivisions of a county designed to present and compare statistical data for areas of roughly equal population. Census tracts generally contain between 1,200 and 8,000 people, with an optimum population of 4,000. A census tract is spatially smaller in a higher-density area and larger in a more sparsely populated area. In higher-density areas, tracts can be considered approximately “neighborhood” sized.Visit the SGID 2020 Census data pagefor more information.

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.

700 (People per square kilometer) in 2011. Notes: a. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. b. For working out the density of India and Jammu & Kashmir the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account. c. India figures include estimated figures for those of the three sub-divisions viz. Mao Maram, Paomata and Purul of Senapati district of Manipur as population census 2001 in these three subdivisions were cancelled due to technical and administrative reasons although a population census was carried out in this sub-division as per schedule.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

This datalayer displays the Urbanized Areas (UAs) for the state based on a January 1, 1990 ground condition. Note that the Census Bureau made significant changes in Urban/Rural designations for the Census 2000 data layers. Some of these delineations and definitions are explained below. 1990 Urban/Rural The U.S. Census Bureau defined urban for the 1990 census as consisting of all territory and population in urbanized areas (UAs) and in the urban portion of places with 2,500 or more people located outside of the UAs. The 1990 urban and rural classification applied to the 50 states, the District of Columbia, and Puerto Rico. 1990 Urbanized Areas A 1990 urbanized area (UA) consisted of at least one central place and the adjacent densely settled surrounding territory that together had a minimum population of 50,000 people. The densely settled surrounding territory generally consisted of an area with continuous residential development and a general overall population density of at least 1,000 people per square mile. 1990 Extended Cities For the 1990 census, the U.S. Census Bureau distinguished the urban and rural population within incorporated places whose boundaries contained large, sparsely populated, or even unpopulated area. Under the 1990 criteria, an extended city had to contain either 25 percent of the total land area or at least 25 square miles with an overall population density lower than 100 people per square mile. Such pieces of territory had to cover at least 5 square miles. This low-density area was classified as rural and the other, more densely settled portion of the incorporated place was classified as urban. Unlike previous censuses where the U.S. Census Bureau defined extended cities only within UAs, for the 1990 census the U.S. Census Bureau applied the extended city criteria to qualifying incorporated places located outside UAs. 1990 Urbanized Area Codes Each 1990 UA was assigned a 4-digit numeric census code in alphabetical sequence on a nationwide basis based on the metropolitan area codes. Note that in Record Type C, the 1990 UA 4-digit numeric census code an d Census 2000 UA 5-digit numeric census code share a 5-character field. Because of this, the 1990 4-digit UA code, in Record Type C only, appears with a trailing blank. For Census 2000 the U.S. Census Bureau classifies as urban all territory, population, and housing units located within urbanized areas (UAs) and urban clusters (UCs). It delineates UA and UC boundaries to encompass densely settled territory, which generally consists of: - A cluster of one or more block groups or census blocks each of which has a population density of at least 1,000 people per square mile at the time - Surrounding block groups and census blocks each of which has a population density of at least 500 people per square mile at the time, and - Less densely settled blocks that form enclaves or indentations, or are used to connect discontiguous areas with qualifying densities. Rural consists of all territory, population, and housing units located outside of UAs and UCs. For Census 2000 this urban and rural classification applies to the 50 states, the District of Columbia, Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, and the Virgin Islands of the United States. Urbanized Areas (UAs) An urbanized area consists of densely settled territory that contains 50,000 or more people. The U.S. Census Bureau delineates UAs to provide a better separation of urban and rural territory, population, and housing in the vicinity of large places. For Census 2000, the UA criteria were extensively revised and the delineations were performed using a zero-based approach. Because of more stringent density requirements, some territory that was classified as urbanized for the 1990 census has been reclassified as rural. (Area that was part of a 1990 UA has not been automatically grandfathered into the 2000 UA.) In addition, some areas that were identified as UAs for the 1990 census have been reclassified as urban clusters. Urban Clusters (UCs) An urban cluster consists of densely settled territory that has at least 2,500 people but fewer than 50,000 people. The U.S. Census Bureau introduced the UC for Census 2000 to provide a more consistent and accurate measure of the population concentration in and around places. UCs are defined using the same criteria that are used to define UAs. UCs replace the provision in the 1990 and previous censuses that defined as urban only those places with 2,500 or more people located outside of urbanized areas. Urban Area Title and Code The title of each UA and UC may contain up to three incorporated place names, and will include the two-letter U.S. Postal Service abbreviation for each state into which the UA or UC extends. However, if the UA or UC does not contain an incorporated place, the urban area title will include the single name of a census designated place (CDP), minor civil division, or populated place recognized by the U.S. Geological Survey's Geographic Names Information System. Each UC and UA is assigned a 5-digit numeric code, based on a national alphabetical sequence of all urban area names. For the 1990 census, the U.S. Census Bureau assigned as four-digit UA code based on the metropolitan area codes. Urban Area Central Places A central place functions as the dominant center of an urban area. The U.S. Census Bureau identifies one or more central places for each UA or UC that contains a place. Any incorporated place or census designated place (CDP) that is in the title of the urban area is a central place of that UA or UC. In addition, any other incorporated place or CDP that has an urban population of 50,000 or an urban population of at least 2,500 people and is at least 2/3 the size of the largest place within the urban area also is a central place. Extended Places As a result of the UA and UC delineations, an incorporated place or census designated place (CDP) may be partially within and partially outside of a UA or UC. Any place that is split by a UA or UC is referred to as an extended place.

description: Completion report for a study of muskrat population dynamics and vegetation utilization, being led by Utah State University for a doctorate dissertation. The study objectives are as follows: (1) Estimate population parameters for the muskrats in the study area (i.e. size, fecundity, and age and sex structure), (2) Determine and evaluate the degree of utilization of the marsh vegetation by a known population of muskrats, and (3) Provide a management plan for muskrats and marsh habitat based on population and utilization parameters. In 1980 a commercial trapping program for muskrats (Ondatra zibethicus) was instituted on the Fish Springs National Wildlife Refuge. The "Muskrat Inventory Procedure No. 911 for Fish Springs charges the refuge personnel with the responsibility of determining the population status of the muskrat population in order to properly manage the harvest. Because a consistent and reliable index of the muskrat density is necessary for determining trapping quotas, 4 indexing techniques were employed during September 1981 in an attempt to ascertain which was the most efficient and accurate method. In addition to evaluating the procedures, the simultaneous implementation of the techniques provided an opportunity to more reliably estimate the muskrat population status for the 1981 trapping season. Both direct and indirect methods were utilized to determine density. Four management units on the refuge were used for collecting the data: 1) Harrison, 2) Mallard, 3) Shoveler, and 4) Curlew (Figure 1). Results determined from these units can be used to extrapolate to the entire refuge.; abstract: Completion report for a study of muskrat population dynamics and vegetation utilization, being led by Utah State University for a doctorate dissertation. The study objectives are as follows: (1) Estimate population parameters for the muskrats in the study area (i.e. size, fecundity, and age and sex structure), (2) Determine and evaluate the degree of utilization of the marsh vegetation by a known population of muskrats, and (3) Provide a management plan for muskrats and marsh habitat based on population and utilization parameters. In 1980 a commercial trapping program for muskrats (Ondatra zibethicus) was instituted on the Fish Springs National Wildlife Refuge. The "Muskrat Inventory Procedure No. 911 for Fish Springs charges the refuge personnel with the responsibility of determining the population status of the muskrat population in order to properly manage the harvest. Because a consistent and reliable index of the muskrat density is necessary for determining trapping quotas, 4 indexing techniques were employed during September 1981 in an attempt to ascertain which was the most efficient and accurate method. In addition to evaluating the procedures, the simultaneous implementation of the techniques provided an opportunity to more reliably estimate the muskrat population status for the 1981 trapping season. Both direct and indirect methods were utilized to determine density. Four management units on the refuge were used for collecting the data: 1) Harrison, 2) Mallard, 3) Shoveler, and 4) Curlew (Figure 1). Results determined from these units can be used to extrapolate to the entire refuge.

Resource created for Assignments 7 and 8 of CEEn 534. The Resource includes discharge data for the Provo River from http://data.cuahsi.org/#. This resource also includes a shapefile of Utah Lake, an image of the USGS site, a stock photo image of a dam, and an audio file consisting of the song "America the Beautiful" performed by Larry Green, guitar instructor at BYU. The resource has had a polyline shapefile of the major streams that flow through Utah County and a raster of the projected Population density in Utah county for the year 2050. the idea would be to use the stream flow data to calculate if the river will provide enough water for the populations immediately around it. SLD files have been included to help with visualization.

Abstract from data: Discharge cubic feet per second data collected from 2020-01-01T00:00:00 to 2020-12-31T23:59:59 created on Wed Feb 24 2021 12:36:43 GMT-0700 (Mountain Standard Time) from the following site: PROVO RIVER AT PROVO UT. Data created by CUAHSI HydroClient: http://data.cuahsi.org/#.8

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Utah. 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 Utah. 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 Utah. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7N29TZ3; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Utah. 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 Utah. 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 Utah. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7N29TZ3

Data on prairie dog densities, flea abundance on prairie dogs, and plague epizootics in Montana and Utah, USA, 2003-2005. Prairie dog species (PDspecies in the data file) included black-tailed prairie dogs (PDs) (BTPD, Cynomys ludovicianus) in north-central Montana, white-tailed PDs (WTPD, Cynomys leucurus) in eastern Utah, and Utah PDs (UPD, Cynomys parvidens) in southwestern Utah. Field research was completed by the U.S. Geological Survey, Fort Collins Science Center, and colleagues. We used summertime visual counts as an index to PD densities (Pddensity in the data file). For each plot, we counted PDs using binoculars and/or spotting scopes from a single location outside the plot that gave the best view of the entire plot and repeated these counts on three (usually consecutive) days. We began counts just after sunrise and continued to conduct repeated systematic scans of the plot until the counts declined to about half the peak number (usually by late morning as PDs went below ground for their typical mid-day break). We converted the counts to density estimates (counts per hectare [ha]).The estimate we used to calculate density was the highest count obtained from a plot for the 3 days within a given year. We analyzed data from colonies experiencing a plague epizootic during this particular study (with an epizootic defined as greater than or equal to 90% decline in PD density). We indexed annual population change (PDpopchgProportion in the data file) by subtracting the count density estimate of the year before a plague epizootic (t1) from the density estimate during an epizootic (t2) for each plot, and dividing that by the density estimate from t1 to summarize population change as a proportionate change. We evaluated the correlation between PD population change and PD density in year t1, because negative plague-effects and the intensity of population decline may be greatest when PD densities are high in year t1 (a potential "density dependent" phenomenon discussed in a wide range of literature on disease ecology). We also evaluated the correlation between PD population change and flea abundance in year t1, because rates of plague transmission and, therefore, PD mortality are expected to increase with increasing flea densities. To assess flea abundance (PDfleas in the data file), we combed live-trapped PDs and counted the number of fleas on each PD. The PDs were live-trapped, individually marked with ear tags, and combed as thoroughly as possible for 30 seconds (s) to collect fleas. Prairie dogs were allowed to recover from anesthesia and released at their trapping locations. For each plot and year, we used the average value of flea counts (defined as flea abundance).

The 2019 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

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.

The Department of Justice launched Operation Weed and Seed in 1991 as a means of mobilizing a large and varied array of resources in a comprehensive, coordinated effort to control crime and drug problems and improve the quality of life in targeted high-crime neighborhoods. In the long term, Weed and Seed programs are intended to reduce levels of crime, violence, drug trafficking, and fear of crime, and to create new jobs, improve housing, enhance the quality of neighborhood life, and reduce alcohol and drug use. This baseline data collection effort is the initial step toward assessing the achievement of the long-term objectives. The evaluation was conducted using a quasi-experimental design, matching households in comparison neighborhoods with the Weed and Seed target neighborhoods. Comparison neighborhoods were chosen to match Weed and Seed target neighborhoods on the basis of crime rates, population demographics, housing characteristics, and size and density. Neighborhoods in eight sites were selected: Akron, OH, Bradenton (North Manatee), FL, Hartford, CT, Las Vegas, NV, Pittsburgh, PA, Salt Lake City, UT, Seattle, WA, and Shreveport, LA. The "neighborhood" in Hartford, CT, was actually a public housing development, which is part of the reason for the smaller number of interviews at this site. Baseline data collection tasks included the completion of in-person surveys with residents in the target and matched comparison neighborhoods, and the provision of guidance to the sites in the collection of important process data on a routine uniform basis. The survey questions can be broadly divided into these areas: (1) respondent demographics, (2) household size and income, (3) perceptions of the neighborhood, and (4) perceptions of city services. Questions addressed in the course of gathering the baseline data include: Are the target and comparison areas sufficiently well-matched that analytic contrasts between the areas over time are valid? Is there evidence that the survey measures are accurate and valid measures of the dependent variables of interest -- fear of crime, victimization, etc.? Are the sample sizes and response rates sufficient to provide ample statistical power for later analyses? Variables cover respondents' perceptions of the neighborhood, safety and observed security measures, police effectiveness, and city services, as well as their ratings of neighborhood crime, disorder, and other problems. Other items included respondents' experiences with victimization, calls/contacts with police and satisfaction with police response, and involvement in community meetings and events. Demographic information on respondents includes year of birth, gender, ethnicity, household income, and employment status.