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 and beyond, 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.

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.

Population density in 2010 within the boundaries of the Narragansett Bay watershed, the Southwest Coastal Ponds watershed, and the Little Narragansett Bay watershed. The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 2010 10m cell population density raster was produced using Rhode Island (2011) state land use data, Massachusetts (2005) state land use, Connecticut (2011) NLCD land use data, and U.S. Census data (2010). To generate a population estimate (number of persons) for any given area within the boundaries of this raster, use the Zonal Statistics as Table tool to sum the 10m cell density values within your zone dataset (e.g., watershed polygon layer). For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

This excel contains results from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 4: "Population." The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990, 2000, and 2010 10m cell population density rasters were produced using Rhode Island state land use data, Massachusetts state land use, Connecticut NLCD land use data, and U.S. Census data. To generate a population estimate (number of persons) for any given area within the boundaries of this raster, NBEP used the the Zonal Statistics as Table tool to sum the 10m cell density values within a given zone dataset (e.g., watershed polygon layer). Results presented include population estimates (1990, 2000, 2010) as well as calculation of percent change (1990-2000;2000-2010;1990-2010).

This graph shows the population density of the United States of America from 1790 to 2019. In 2019, the population density was approximately 92.9 residents per square mile of land area. Population density in the United States Population density has been tracked for over two hundred years in the United States. Over the last two centuries, the number of people living in the United States per square mile has grown from 4.5 in 1790 to 87.4 in 2010. After examining the data in detail, it becomes clear that a major population increase started around 1870. Population density was roughly 11 at the time and has doubled in the last century. Since then, population density grew by about 16 percent each decade. Population density doubled in 1900, and grew in total by around 800 percent until 2010.

The population density of the United States varies from state to state. The most densely populated state is New Jersey, with 1,208 people per square mile living there. Rhode Island is the second most densely populated state, with slightly over 1,000 inhabitants per square mile. A number of New England states follow at the top of the ranking, making the northeastern region of the United States the most densely populated region of the country.

The least populated U.S. state is the vast territory of Alaska. Only 1.3 inhabitants per square mile reside in the largest state of the U.S.

Compared to other countries around the world, the United States does not rank within the top 50, in terms of population density. Most of the leading countries and territories are city states. However, the U.S. is one of the most populous countries in the world, with a total population of over 327 million inhabitants, as of 2018.

An archive of the fish and inveterate data from the Rhode Island Department of Environmental Management (RIDEM) juvenile fin-fish survey across 6 Rhode Island salt ponds from 2010-2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Fish and invertebrate surveys:
The Rhode Island Department of Environmental Management (RIDEM) provided us with the fish and invertebrate survey data for this study. Beginning in 2010, RIDEM established permanent monitoring stations throughout all six coastal ponds (points on Fig. 1A of Yeager et al., 2020). Communities were sampled monthly from May to October each year via 150 ft beach seine net. Fish and macroinvertebrate individuals were counted, measured, and identified to species. Communities were composed of fishes and invertebrates from multiple trophic levels. Across all ponds, species richness ranged from and 11 to 39 and trophic level ranged from 2.01 \u2013 4.65 (Appendix S1 of Yeager et al., 2020). For the community analysis, we examined species and their abundances averaged across sampling stations to account for dependence within each pond and averaged across months to account for seasonal differences of species presence. To ensure even community sampling across ponds which varied in number of sampling stations, we conducted a species rarefaction test, examining both the rarefaction curves as well as the relationship between the rarefied and observed number of species (Appendix S2 of Yeager et al., 2020). awards_0_award_nid=709941 awards_0_award_number=OCE-1652320 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1652320 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=RI Community Data PI: Mallarie Yeager (Northeastern University) Co-PI: A. Randall Hughes (Northeastern University)
Version date: 03-March-2020 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.805252.1 Easternmost_Easting=-71.5056 geospatial_lat_max=41.3998 geospatial_lat_min=41.3311 geospatial_lat_units=degrees_north geospatial_lon_max=-71.5056 geospatial_lon_min=-71.7689 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/805252 institution=BCO-DMO instruments_0_dataset_instrument_nid=805256 instruments_0_description=A seine net is a very long net, with or without a bag in the centre, which is set either from the shore or from a boat for surrounding a certain area and is operated with two (long) ropes fixed to its ends (for hauling and herding the fish).

Seine nets are operated both in inland and in marine waters. The surrounded and catching area depends on the length of the seine and of the hauling lines.

(definition from: fao.org) instruments_0_instrument_name=Seine Net instruments_0_instrument_nid=716403 metadata_source=https://www.bco-dmo.org/api/dataset/805252 Northernmost_Northing=41.3998 param_mapping={'805252': {'Latitude': 'flag - latitude', 'Longitude': 'flag - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/805252/parameters people_0_affiliation=Northeastern University people_0_affiliation_acronym=NEU people_0_person_name=Mallarie Yeager people_0_person_nid=805269 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Northeastern University people_1_affiliation_acronym=NEU people_1_person_name=A. Randall Hughes people_1_person_nid=522929 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Shannon Rauch people_2_person_nid=51498 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=Seagrass and Oyster Ecosystems projects_0_acronym=Seagrass and Oyster Ecosystems projects_0_description=NSF Award Abstract: Disease outbreaks in the ocean are increasing, causing losses of ecologically important marine species, but the factors contributing to these outbreaks are not well understood. This 5-year CAREER project will study disease prevalence and intensity in two marine foundation species - the seagrass Zostera marina and the Eastern oyster Crassostrea virginica. More specifically, host-disease relationships will be explored to understand how genetic diversity and population density of the host species impacts disease transmission and risk. This work will pair large-scale experimental restorations and smaller-scale field experiments to examine disease-host relationships across multiple spatial scales. Comparisons of patterns and mechanisms across the two coastal systems will provide an important first step towards identifying generalities in the diversity-density-disease relationship. To enhance the broader impacts and utility of this work, the experiments will be conducted in collaboration with restoration practitioners and guided by knowledge ascertained from key stakeholder groups. The project will support the development of an early career female researcher and multiple graduate and undergraduate students. Students will be trained in state-of-the-art molecular techniques to quantify oyster and seagrass parasites. Key findings from the surveys and experimental work will be incorporated into undergraduate courses focused on Conservation Biology, Marine Biology, and Disease Ecology. Finally, students in these courses will help develop social-ecological surveys and mutual learning games to stimulate knowledge transfer with stakeholders through a series of workshops. The relationship between host genetic diversity and disease dynamics is complex. In some cases, known as a dilution effect, diversity reduces disease transmission and risk. However, the opposite relationship, known as the amplification effect, can also occur when diversity increases the risk of infection. Even if diversity directly reduces disease risk, simultaneous positive effects of diversity on host density could lead to amplification by increasing disease transmission between infected and uninfected individuals. Large-scale field restorations of seagrasses (Zostera marina) and oysters (Crassostrea virginica) will be utilized to test the effects of host genetic diversity on host population density and disease prevalence/intensity. Additional field experiments independently manipulating host genetic diversity and density will examine the mechanisms leading to dilution or amplification. Conducting similar manipulations in two marine foundation species - one a clonal plant and the other a non-clonal animal - will help identify commonalities in the diversity-density-disease relationship. Further, collaborations among project scientists, students, and stakeholders will enhance interdisciplinary training and help facilitate the exchange of information to improve management and restoration efforts. As part of these efforts, targeted surveys will be used to document the perceptions and attitudes of managers and restoration practitioners regarding genetic diversity and its role in ecological resilience and restoration. projects_0_end_date=2022-01 projects_0_geolocation=Coastal New England projects_0_name=CAREER: Linking genetic diversity, population density, and disease prevalence in seagrass and oyster ecosystems projects_0_project_nid=709942 projects_0_start_date=2017-02 sourceUrl=(local files) Southernmost_Northing=41.3311 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-71.7689 xml_source=osprey2erddap.update_xml() v1.3

This hosted feature layer has been published in RI State Plane Feet NAD 83.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 2015 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 boundaries for counties and equivalent entities are as of January 1, 2010.

Redistricting Census 2000 TIGER/Line was used to divide Rhode Island into Tract, Block Group and Block levels. The TIGER/Line data set was joined with Census 2000 Redistricting Data to calculate the population density per square mile. These data are tabular and Geographic US Census 2000 information for general GIS use.

Population density in 2000 within the boundaries of the Narragansett Bay watershed, the Southwest Coastal Ponds watershed, and the Little Narragansett Bay watershed. The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 2000 population density (persons per acre) raster was produced using Rhode Island (2003-2004) state land use data, Massachusetts (1999) state land use, Connecticut (2001) NLCD land use data, and U.S. Census data (2000). This raster is appropriate for mapping purposes, as raster values have been converted to persons per acre. To generate population estimates (number of persons), use the 10m cell population rasters. For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Rhode Island. 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 Rhode Island. 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 Rhode Island. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F78S4MWN

Population density in 1990 within the boundaries of the Narragansett Bay watershed, the Southwest Coastal Ponds watershed, and the Little Narragansett Bay watershed. The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990 10m cell population density raster was produced using Rhode Island 1988 state land use data, Massachusetts 1985 state land use, Connecticut 1992 NLCD land use data, and U.S. Census data (1990). To generate a population estimate (number of persons) for any given area within the boundaries of this raster, use the Zonal Statistics as Table tool to sum the 10m cell density values within your zone dataset (e.g., watershed polygon layer). For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

Knowledge of how roads affect forest biodiversity can be improved by measuring the responses of indicator species to complex environmental gradients caused by these infrastructures. We studied litter invertebrate species responses to road edges in laurel and pine forests in Tenerife, Canary Islands. We sampled invertebrates from litter and assessed the environmental variation related to road proximity. We also assessed the effect of relevant environmental predictors on a diverse array of potential indicator species. We applied canonical ordination and non-parametric regression (Lowess) to classify invertebrate species responses to roads and their associated gradients. Three types of responses to road edge proximity were defined for the most common invertebrate taxa: edge-preferring or edge specialists, interior-preferring or edge-avoiders, and edge-indifferent or neutral species. Those species appearing most frequently and with higher population density between 1 and 20 m from the edge (commonly peaking at 10 m from the road) were categorized as edge-preferring. We classified taxa attaining peak population densities at or beyond 60 m from the edge (and most commonly 100 m) as interior species. Edge-neutral species were those without an evident pattern of stabilization in abundance along the gradient and with peaks in abundance at varying distance intervals. These edge litter communities contain a high native and endemic diversity but also a significant density of alien fauna. The specific patterns of penetration of road edge effects on invertebrate species should be seen as having a pervasive and cumulative impact considering the exceptionally large number of roads in these forests and the high population densities of alien invertebrates. Future management plans for forest conservation on the Canary Islands should include the highly altered but valuable litter communities along road edges.

This geodatabase contains data from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 4: "Population." Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990, 2000, and 2010 10m cell population density rasters were produced using Rhode Island state land use data, Massachusetts state land use, Connecticut NLCD land use data, and U.S. Census data. Summary tables catalog population estimates (1990, 2000, 2010) for each geoscale as well as calculation of acres of developed lands per 100 persons and percent change in estimated population (1990-2000; 2000-2010; 1990-2010).