This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

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 web map presents the Census 2020 Urbanized Areas (UA) and Urban Clusters (UC). For the 2020 Census, an urban area will comprise a densely settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000.This layer uses the US Census Bureau 2020 Urban Area source TIGER/Line data and corresponding List of 2020 Population Attributes.

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.

2020 Census data for the city of Boston, Boston neighborhoods, census tracts, block groups, and voting districts. In the 2020 Census, the U.S. Census Bureau divided Boston into 207 census tracts (~4,000 residents) made up of 581 smaller block groups. The Boston Planning and Development Agency uses the 2020 tracts to approximate Boston neighborhoods. The 2020 Census Redistricting data also identify Boston’s voting districts.

For analysis of Boston’s 2020 Census data including graphs and maps by the BPDA Research Division and Office of Digital Cartography and GIS, see 2020 Census Research Publications

For a complete official data dictionary, please go to 2020 Census State Redistricting Data (Public Law 94-171) Summary File, Chapter 6. Data Dictionary. 2020 Census State Redistricting Data (Public Law 94-171) Summary File

2020 Census Tracts In Boston

2020 Census Block Groups In Boston

Boston Neighborhood Boundaries Approximated By 2020 Census Tracts

Boston Voting District Boundaries

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows the distribution of population in what is now Canada circa 1851, 1871, 1901, 1921 and 1941. The five maps display the boundaries of the various colonies, provinces and territories for each date. Also shown on these five maps are the locations of principal cities and settlements. These places are shown on all of the maps for reference purposes even though they may not have been in existence in the earlier years. Each map is accompanied by a pie chart providing the percentage distribution of Canadian population by province and territory corresponding to the date the map is based on. It should be noted that the pie chart entitled Percentage Distribution of Total Population, 1851, refers to the whole of what was then British North America. The name Canada in this chart refers to the province of Canada which entered confederation in 1867 as Ontario and Quebec. The other pie charts, however, show only percentage distribution of population in what was Canada at the date indicated. Three additional graphs are included on this plate and show changes in the distribution of the population of Canada from 1867 to 1951, changes in the percentage distribution of the population of Canada by provinces and territories from 1867 to 1951 and elements in the growth of the population of Canada for each ten-year period from 1891 to 1951.

The Wildland-Urban Interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001) for the conterminous United States from 1990-2020. These data are useful within a GIS for mapping and analysis at national, state, and local levels. Data are available as a geodatabase and include information such as housing densities for 1990, 2000, 2010, and 2020; wildland vegetation percentages for 1992, 2001, 2011, and 2019; as well as WUI classes in 1990, 2000, 2010, and 2020.This WUI feature class is separate from the WUI datasets maintained by individual forest unites, and it is not the authoritative source data of WUI for forest units. This dataset shows change over time in the WUI data up to 2020.Metadata and Downloads

The North Carolina State Demographer data platform houses the latest data produced by the Office of the State Demographer. The platform allows users to create visualizations, download full (or partial) datasets, and create maps. Registered users can save their visualizations and be notified of dataset updates. This new platform is a subdomain of OSBM’s Log In to North Carolina (LINC) – a service containing over 900 data items including items pertaining to population, labor force, education, transportation, etc. LINC includes topline statistics from the State Demographer’s population estimates and projections while the North Carolina State Demographer data platform includes more detailed datasets for users requiring more detailed demographic information.

The interactive map of Lao PDR highlights the 12 districts in Oudomxai, Phongsaly, Xieng Khouang and Houaphan provinces, targeted by the Strategic Support for Food Security Project (SSFSNP) and located in the mountainous regions in the North of the country. The project is expecting to reduce extreme poverty and malnutrition in 400 food insecure villages and 34,000 poor smallholder households, with a predominantly non-Tai ethnic population. The map shows that according to the most recent reports the selected districts are located in provinces with more than 40% of the population living below the country poverty line.

Data Sources:

SSFSNP Locations:

Source: GAFSP Documents.

Poverty Incidence (Proportion of population below the poverty line) (2007): Proportion of the population living on less than Kip 92,959 (US$8.79) per person per month.

Source: Lao Statistics Bureau - World Bank. “Lao PDR Poverty Trends 1992/93-2002/03 (2004).”

Malnutrition (Proportion of underweight children under 5 years) (2011-12): Prevalence of severely underweight children is the percentage of children under age 5 whose weight-for-age is more than 3 standard deviations below the median for the international reference population ages 0-59 months.

Source: Measure DHS - Ministry of Health (MoH) and Lao Statistics Bureau (LSB). “Lao PDR Lao Social Indicator Survey (LSIS) 2011-12 (MULTIPLE INDICATOR CLUSTER SURVEY / DEMOGRAPHIC AND HEALTH SURVEY (2012).”

Total Population (2012): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.

Source: LAO Statistics Bureau (LSB). “Statistical Yearbook 2012 –Population Estimation and Density 2012.”

Population Density (2010): Population divided by land area in square kilometers.

Source: LAO Statistics Bureau (LSB). “Statistical Yearbook 2012 –Population Estimation and Density 2012.”

Total Population (2015): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.

Source: LAO Statistics Bureau (LSB). "The 4th Population and Housing Census 2015 (PHC) 2015."

Population Density (2015): Population divided by land area in square kilometers.

"The 4th Population and Housing Census 2015 (PHC) 2015."

Rice Harvested Area and Production: Harvested area in hectares by rice type and total production in tons by rice type 2012.

Source: Lao PDR Statistics Bureau (LBR) - Ministry of Agriculture and Forestry. “Statistical Yearbook 2012.”

The maps displayed on the GAFSP website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

The Geospatial Energy Mapper (GEM) provides mapping data and analysis tools for planning energy infrastructure in a geographic context. GEM is an interactive web-based decision support system that allows users to locate areas with high suitability for clean power generation and potential energy transmission corridors in the United States. Users can browse and download data layers, or create a custom suitability model to identify areas for energy development. GEM is built on the core data and capabilities of the Energy Zones Mapping Tool (EZMT). GEM features an improved user interface, updated data, and additional capabilities. Argonne National Laboratory hosts the tool with funding from the U.S. Department of Energy (DOE) Office of Electricity.This model is specific to land-based wind turbines (120 m). We modified the default model parameters to remove the habitat and protected areas elements. As a result, this layer represents the threat of energy development from a utility perspective and does not consider whether development is suitable from a conservation perspective. We considered a threat-only layer to be more complementary to the Southeast Blueprint, which already depicts conservation priorities. In addition, the source data for the habitat and protected areas elements were outdated and provided inconsistent coverage across the SECAS geography. We kept the default weights for all remaining model input layers as shown in the bulleted list below. The default and customizable models are downloadable from the GEM viewer. These data are provided for use in combination with the Blueprint and other data available on the SECAS Atlas. We chose the 120 m model (rather than the 80 m or 100 m models) based on the overall trend of increasing turbine size, and the reduction in wind shear and increase in available wind speed at higher altitudes (Department of Energy 2023).Distance in Meters to an Airport (weight = 2)Population Density (weight = 1)Distance (m) to Substation (≥ 345 kV) (weight = 3)Distance in Meters to Major Road (weight = 1)Land Cover (NLCD 2019) (weight = 1)Mean Annual Wind Speed (Land-based at 120 m) (weight = 5)For more information on these model parameters or to view and download this layer from its native mapper:Visit https://gem.anl.gov/toolSelect "Find suitable areas" from the sidebar on the leftChoose wind technologyChoose the Land-based wind turbine (120 m) modelView/download the default model or customize as described abovePlease direct any questions to gem@anl.gov.