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

The 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.

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer will be updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico.Census tracts with no population are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

A dataset listing New York counties by population for 2024.

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.

Introduction

Climate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.

The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.

This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.

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This map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).

Areas using lidar-based elevation data: U.S. coastal states except Alaska
Elevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)).

Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago.

CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.

The 2016 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.

Remotely-sensed and bottom-up survey information were compiled on eight variables measuring the direct and indirect human pressures on the environment globally in 1993 and 2009. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. A validation analysis using scored pressures from 3114×1 km2 random sample plots revealed strong agreement with the Human Footprint maps. We anticipate that the Human Footprint maps will find a range of uses as proxies for human disturbance of natural systems. The updated maps should provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application.

A dataset listing New Jersey counties by population for 2024.

Map of gray wolf and red wolf current and historic range and suitable habitat across the U.S. and Mexico. Produced by Defenders of Wildlife (2021). All data sources listed below:Gray Wolf:Historic Range: The historic range for the gray wolf was delineated with the help of peer reviewed sources: Rutledge et al. 2010. Genetic and morphometric analysis of sixteenth century Canis skull fragments: implications for historic eastern and gray wolf distribution in North America.Current Range: Range delineation was based on range data from IUCN and USFWS, expert knowledge, and personal communications from Defenders of Wildlife field teams, academia, and federal agencies. Details of delineations focused mostly on the United States and Mexico as ranges north of that couldn’t be confirmed due to controversies.Suitable Habitat:Bennett, L.E. 1994. Colorado Gray Wolf Recovery: A biological feasibility study. Final Report. U.S. Fish and Wildlife Service and University of Wyoming Fish and Wildlife Cooperative research unit, Laramie, Wyoming, USA. Available at: https://babel.hathitrust.org/cgi/pt?id=umn.31951p00672031a;view=1up;seq=146California Department of Fish and Wildlife. 2016b. Potential Suitable Habitat in California. Pages 153-160 in Conservation Plan for Gray Wolves in California Part 2. Carroll, C., Phillips, M.K., Lopez-Gonzalez, C.A., and Schumaker, N.H. 2006. Defining Recovery Goals and Strategies for Endangered Species: The Wolf as a Case Study. BioScience 56(1): 25–37, https://doi.org/10.1641/0006-3568(2006)056[0025:DRGASF]2.0.CO;2Carroll, C. 2003. Impacts of Landscape Change on Wolf Viability in the Northeastern U.S. and Southeastern Canada. Wildlands Project Special Paper No. 5, available at https://www.klamathconservation.org/docs/wolfviabilitypaper.pdf.Carroll, C. 2007. Application of habitat models to wolf recovery planning in Washington. Unpublished report.Defendersof Wildlife. 2006. Places for Wolves: A Blueprint for Restoration and Recovery in the Lower 48 StatesDefenders of Wildlife. 2013. Places for WolvesHarrison, D. J., and T. G. Chapin. 1998. An assessment of potential habitat for eastern timber wolves in the northeastern United States and connectivity with occupied habitat in southeastern Canada. Wildlife Conservation Society, Working Paper Number 7.Harrison, D. J., and T. G. Chapin. 1998. Extent and connectivity of habitat for wolves in eastern North America. Wildlife Society Bulletin 26: 767-775, available at https://wolfology1.tripod.com/id207.htmHearne D., Lewis K., Martin M., Mitton E., and Rocklen C. 2003. Assessing the Landscape: Toward a Viable Gray Wolf Population in Michigan and Wisconsin. Hendricks, S.A., Schweizer, R.M., Harrigan, R.J., Pollinger, J.P., Paquet, P.C., Darimont, C.T., Adams, J.R., Waits, L.P., vonHoldt, B.M., Hohenlohe1, P.A. and R.K. Wayne. 2018. Natural recolonization and admixture of wolves (Canis lupus) in the US Pacific Northwest: challenges for the protection and management of rare and endangered taxa. The Genetics Society. Heredity. https://doi.org/10.1038/s41437-018-0094-x.Jimenez, M.D. et al. 2017. Wolf Dispersal in the Rocky Mountains, Western United States: 1993–2008. The Journal of Wildlife Management 81(4):581–592.Larson, T. and W.J. Ripple. 2006. Modeling Gray Wolf (Canis lupus) habitat in the Pacific Northwest, U.S.A. Journal of Conservation Planning 2:17-33.Maletzke, B.T. and R.B. Wielgus. 2011. Development of wolf population models for RAMAS© analysis by the Washington Department of Fish and Wildlife.Martinez-Meyer E., Gonzalez-Bernal A., Velasco J.A., Swetnam T.L., Gonzalez-Saucedo Z.Y., Servin J., Lopez-Gonzalez C.A., Oakleaf, J.A., Liley S., and Heffelfinger J.R. 2020. Rangewide habitat suitability analysis for the Mexican wolf (Canis lupus baileyi) to identify recovery areas in its historical distribution. Diversity and Distributions 00:1-13.McNab, W.H., Cleland, D.T., Freeouf, J.A., Keys, Jr., J.E., Nowacki, G.J., Carpenter, C.A., comps. 2007. Description of ecological subregions: sections of the conterminous United States [CD-ROM]. Gen. Tech. Report WO-76B. Washington, DC: U.S. Department of Agriculture, Forest Service. 80 p.McNab, W.H. and P.E. Avers. 1995. Ecological subregions of the United States. Washington, DC: U.S. Department of Agriculture, Forest Service, available at https://www.fs.fed.us/land/pubs/ecoregions/.Mladenoff, D.J., Sickley, T.A., Haight, R.G. and Wydeven, A.P. 1995. A Regional Landscape Analysis and Prediction of Favorable Gray Wolf Habitat in the Northern Great Lakes RegionMladenoff, D.J. and T.A. Sickley. 1998. Assessing Potential Gray Wolf Restoration in the Northeastern United States: A Spatial Source. Journal of Wildlife Management 62(1): 1-10.Minnesota Dept. of Natural Resources. 2001. Minnesota Wolf Management Plan. Minnesota Dept. Natural Resources. 2017a. Gray Wolf, available at https://www.dnr.state.mn.us/mammals/wolves/mgmt.html.Montana Fish Wildlife & Parks. 2004. Montana Gray Wolf Conservation and Management Plan.Montana Fish,Wildlife & Parks. 2018. Montana Annual Report 2018: Wolf Conservation and Management.Oakleaf J.K., Murray D.L., Oakleaf J.R., Bangs E.E., Mack C.M., Smith D.W., Fontaine J.A., Jimenez M.D., Meier T.J., and C.C. Niemeyer. 2006. Habitat Selection by Recolonizing Wolves in the Northern Rocky Mountains of the United States. Journal of Wildlife Management 70(2):554-563.Oregon Department of Fish and Wildlife. 2015. Updated mapping potential gray wolf range in Oregon.Potvin M.J., Drummer T.D., Vucetich J.A., Beyer E. Jr., and J.H. Hammill. 2005. Monitoring and Habitat Analysis for Wolves in Upper Michigan. Journal of Wildlife Management 69(4):1660-1669.Treves A., Martin K.A., Wiedenhoeft J.E., Wydeven A.P. (2009) Dispersal of Gray Wolves in the Great Lakes Region. In: Wydeven A.P., Van Deelen T.R., Heske E.J. (eds) Recovery of Gray Wolves in the Great Lakes Region of the United States. Springer, New York, NY. https://doi.org/10.1007/978-0-387-85952-1_12USGS Gap Analysis Project Species Range and Predicted Habitat: Gray wolf: https://gapanalysis.usgs.gov/apps/species-data-download/Washington Dept. of Fish and Wildlife (WDFW). 2017. Washington Gray Wolf Conservation and Management 2017 Annual Report.Wiles, G. J., H. L. Allen, and G. E. Hayes. 2011. Wolf conservation and management plan for Washington. Washington Department of Fish and Wildlife, Olympia, Washington. 297 pp.Red Wolf:Historic Range:Red wolf historic range established by USFWS based on information provided by the 2016 Wildlife Management Institute report [ Wildlife Management Institute: A Review and Evaluation of the Red Wolf (Canis rufus) Historic Range, Final Report – 5/25/2016]. The historic range layer is a combination of the following Level II EPA Ecoregions: 1) Mississippi Alluvial and Southeast USA Coastal Plains, 2) Ozark/Ouachita-Appalachian Forests, 3) South Central Semi-Arid Prairies, 4) Southeastern USA Plains, and 5) Texas-Louisiana Coastal PlainsCurrent Range (Recovery Area):Red wolf recovery area adapted from the USFWS current range information.Suitable Habitat:Toivonen L.K. (2018) Assessing red wolf conservation based on analyses of habitat suitability and human perception of carnivores.Karlin M., Vaclavik T., Chadwick J., and R. Meentemeyer. (2016) Habitat use by adult red wolves, Canis rufus, in an agricultural landscape, North Carolina, USA. Mammal Study 41:87-95.