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?

This dot map shows three kinds of urban transitions. First, there are indeed areas where changes take place at very precise boundaries — such as between Lawndale and the Little Village, or Austin and Oak Park — and Chicago has more of these stark borders than most cities in the world. But transitions also take place through gradients and gaps as well, especially in the northwest and southeast. Using graphic conventions which allow these other possibilities to appear takes much more data, and requires more nuance in the way we talk about urban geography, but a cartography without boundaries can also make simplistic policy or urban design more difficult — in a good way.

This ethnicity dataset (GREG) is a digital version of the paper Soviet Narodov Mira atlas created in 1964. In 2010 the GREG (Geo-referencing of ethnic groups) project, used maps and data drawn from the Narodov Mira atlas to create a GIS (Geographic Information Systems) version of the atlas (2010). ETH ZurichFirst developed by G.P. Murdock in the 1940s, is an ethnographic classification system on human behavior, social life and customs, material culture, and human-ecological environments (2003). University of California

The dataset contains information relating to the race route during Race 7 of the 2013 Cycling World Championships, held on 09/25/2013.

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.

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In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

This map shows child poverty in the US by county, with an emphasis on the Black children living in poverty.The darkest colors in the map highlight where there are a higher percentage of Black children living in poverty. The symbol size shows the count of all children living in poverty. The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here. To explore other child poverty patterns, visit the following maps:Where is Black child poverty higher than total child poverty?Which race has the highest rate of child poverty?

Participants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground. Upon completion, students will be able to: Access web resources that provide near real-time updates on active wildfires, Navigate databases of remote sensing imagery and data, Analyze geospatial data to detect fire hot spots, map burn areas, and assess severity, Process image and GIS data in open source tools like QGIS and Google Earth Engine.

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

Climate-GIS Västernorrland is intended to be a map service where the County Administrative Board publishes and visualises different types of climate-related planning documents regarding Västernorrland County. The documentation consists of the results of various analyses carried out by national actors and by the County Administrative Board in Västernorrland. With the help of this map service, you can find information for a specific location in the county. The service also includes tools for drawing, sharing and printing your own maps. As new material is produced, it is published here and information about the material is posted on the County Administrative Board’s website. In addition, information is sent to contact persons for climate change adaptation in the county. The map service contains the following geodata: The low points and runways of the Skyfall mapping per municipality within the county. Flood mapping for specific watercourses. SGU Prerequisites for screed in fine-grained soils (WMS service) SGU Skreddatabas(slide, race and other earth movements(WMS service) SMHI, Climate series, hydrology SMHI, Climate, Meteorology LST SMHI Climate series, meteorology expanded snow indicators SGU Earth species Earthslides and ravines (WMS service) SGU Earth species 1:25 000-1:100,000 (WMS service) SGU Earth species 1:200,000, Medelpad (WMS service) SGU Earth species 1:1 million (WMS service) Note that the limits of the map layers you see in the map service may differ from what it looks like in reality. To get more information about the boundaries of each map layer, we would like to refer to the descriptive information (metadata) available for each map layer. Metadata can be found in the County Administrative Boards’ geodata directory, search the map warehouse name.

This map compares homeownership rates between households with a non-Hispanic White householder and households with a Black or African American householder. This map shows us where there is a disparity in home ownership based on race/ethnicity. The pattern is shown at the state, county, and tract levels. Zoom or pan around the map to explore the map. You can also search for your city and explore the pattern in your local area. If you zoom out, you can see the nationwide pattern. The data comes from the most current release of American Community Survey (ACS) estimates from the U.S. Census Bureau. The layer being used in this map can be found here, and also within ArcGIS Living Atlas of the World. Click here to find more ACS layers within Living Atlas. Note: areas where there are no Black or White householders, no symbol is shown. This map compares areas where there are both White and Black householders.

This map highlights child poverty in the US by which race has the highest percentage of children in poverty. The pattern is shown by county, and the popup provides a breakdown of child poverty rates by race (where available). Note that not all counties have data for all races, so the map will show the predominant value based on the data available.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here. To explore other child poverty patterns, visit the following maps:Where is Black child poverty higher than total child poverty?Black Children in Poverty in the US

Do Black households make as much as the typical household in the US? This map shows that this doesn't seem to be the case. This map compares the median household income of households with Black householders compared to the 2020 US median household income: $67,340. If the Black households in a county make as much as a "typical" household, the county is shown in turquoise. If Black households in a county make less than the US median income, it is shown in orange. The size of the symbol highlights where there are the highest counts of Black population in the US.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.

Do Black households make as much as the "typical" household in their county? This map shows that this doesn't seem to be the case. This map compares the median household income of households with Black householders compared to the median household income of that county. If the Black households in a county make as much as a "typical" household in their county, the county is shown in turquoise. If Black households in a county make less than the median income of their county, it is shown in orange. The size of the symbol highlights where there are the highest counts of black population in the US.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.

This map compares two different rates of child poverty:The percent of all children who live below the poverty lineThe percent of Black children who live below the poverty lineThe higher rate between the two is shown by the associated color. The size of the symbol shows the count of all children living in poverty. The pattern is shown by US counties.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here. To explore other child poverty patterns, visit the following maps:Black Children in Poverty in the USWhich race has the highest rate of child poverty?

Prior to the civil war in the 1990’s ethnic tension caused many rivalries between groups. This was common between the Temne, with their allies the Limba, and the Mende, with their allies the Sherbro, Kissi, and Gola groups. Even with this history of ethnic conflict it does not appear to be a significant factor that contributed to the civil war as the war focused on control of diamond mines. With the civil war over for more than a decade the country is relatively peaceful. There are no serious ethnic conflicts or rivalries. Limba – Limba populations are found in other West African countries although 90% reside in Sierra Leone. The majority are Muslim, having been introduced to Islam in the late nineteenth century. This is much later than their neighbors. To prevent too much Westernization, the Limba often send their children to Islamic schools. Mande – The Mande are a large ethnic group in West Africa that is comprised of many smaller groups. The Mande people speak a variety of Mande languages. Most practice agriculture, animal husbandry, and trade. They practice a patrilineal society having the eldest male serve as lineage head. With so many Mande groups spread over West Africa there is much variation among language and culture. Mel – The Mel within Sierra Leone are comprised of the Gola and the Kissi. Similar to other West Africa groups, the Gola participate in secret societies. The most important occurs around the age of puberty and these societies seek to socialize youth with Gola culture. The Kissi are increasingly becoming culturally influenced by the Mende people. Soso - The Soso were introduced to Islam in the seventeenth century and they are now overwhelmingly Sunni Muslim, of the Maliki School. Many still perform ritual ceremonies from indigenous religions. They are often influenced by neighboring groups. Temne – The Temne are one of the largest ethnic groups in the country. While the capital of Freetown is home to many groups, the largest number of people belong to the Temne ethnicity. The majority are Muslim, having been introduced to Islam in the seventeenth century. Some Temne still practice indigenous religions or incorporate them into their practice of Islam. Similar to other groups in the country, the Temne also have secret socieites. The Temne use these socieites to learn about the Temne culture. Although many have convertered to Islam or Christianity, it is common to incorporate indigenous religious beliefs. Attribute Table Field DescriptionsISO3-International Organization for Standardization 3-digit country codeADM0_NAME-Administration level zero identification / namePEOPLEGP_1-People Group level 1PEOPLEGP_2-People Group level 2PEOPLEGP_3-People Group level 3PEOPLEGP_4-People Group level 4PEOPLEGP_5-People Group level 5ALT_NAMES-Alternative names or spellings for a people groupCOMMENTS-Comments or notes regarding the people groupSOURCE_DT-Source one creation dateSOURCE-Source oneSOURCE2_DT-Source two creation dateSOURCE2-Source twoCollectionThis feature class was constructed by referencing and combining information from Murdock’s Map of Africa (1959) with other anthropological literature pertaining to Sierra Leone ethnicity. The information was then processed through DigitalGlobe’s AnthropMapper program to generate more accurate ethnic coverage boundaries. Anthromapper uses geographical terrain features, combined with a watershed model, to predict the likely extent of ethnic and linguistic influence.Metadata and data pertaining to the feature class was collected from the review of Murdock’s Map of Africa (1959) in conjunction with information from anthropological research pertaining to ethnicity in northern Africa. While efforts were made to secure the accuracy of the geographic location of existing ethnicities, many are transient in nature and continue to migrate. Further, it should be stressed that ethnic groups listed represent the prominent people groups in Sierra Leone; however, numerous subgroups may exist below this tier. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Anthromapper. DigitalGlobe, September 2014.Gonen, Amiram. The Encyclopedia of the Peoples of the World. New York: Henry Holt and Company, 1993.Levinson, David. Encyclopedia of World Cultures: Africa and the Middle East. Boston: G.K. Hall and Co., 1995.Murdock, George Peter. Tribal Map of Africa from Africa: Its Peoples and Their Culture History. New York: McGraw-Hill Book Co., January 1959.Olson, James S. The Peoples of Africa: An Ethnohistorical Dictionary. Westport: Greenworod Press, 1996.The Diagram Group. Encyclopedia African Peoples. London: Diagram Visual Information, 2000.Yakan, Mohamad Z. Almanac of African Peoples and Nations. New Brunswick: Transaction Publishers, 1999.Sources (Metadata)Gonen, Amiram. The Encyclopedia of the Peoples of the World. New York: Henry Holt and Company, 1993.Levinson, David. Encyclopedia of World Cultures: Africa and the Middle East. Boston: G.K. Hall and Co., 1995.Murdock, George Peter. Tribal Map of Africa from Africa: Its Peoples and Their Culture History. New York: McGraw-Hill Book Co., January 1959.Notholt, Stuart A. Fields of Fire: An atlas of ethnic conflict. London: Stuart Notholt Communications Ltd, 2008.Olson, James S. The Peoples of Africa: An Ethnohistorical Dictionary. Westport: Greenworod Press, 1996.The Diagram Group. Encyclopedia African Peoples. London: Diagram Visual Information, 2000.University of Iowa Museum of Art, “Sierra Leone; Gola or Vai peoples, Lansana Ngumoi”. January 2006. Accessed December 2014. http://uima.uiowa.edu.Yakan, Mohamad Z. Almanac of African Peoples and Nations. New Brunswick: Transaction Publishers, 1999.

GIS maps are windows into a database. Learn how to access the data connected to map features to answer questions about the real world.GoalsExplore patterns with GIS maps.Create GIS maps.Display map labels.Use a table to select features on a map.

This layer shares SEDAC's population projections for U.S. counties for 2020-2100 in increments of 5 years, for each of five population projection scenarios known as Shared Socioeconomic Pathways (SSPs). This layer supports mapping, data visualizations, analysis and data exports.Before using this layer, read:The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview by Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni. Global Environmental Change, Volume 42, 2017, Pages 153-168, ISSN 0959-3780, https://doi.org/10.1016/j.gloenvcha.2016.05.009.From the 2017 paper: "The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature."According to SEDAC, the purpose of this data is:"To provide subnational (county) population projection scenarios for the United States essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications."According to Francesco Bassetti of Foresight, "The SSP’s baseline worlds are useful because they allow us to see how different socioeconomic factors impact climate change. They include: a world of sustainability-focused growth and equality (SSP1); a “middle of the road” world where trends broadly follow their historical patterns (SSP2); a fragmented world of “resurgent nationalism” (SSP3); a world of ever-increasing inequality (SSP4);a world of rapid and unconstrained growth in economic output and energy use (SSP5).There are seven sublayers, each with county boundaries and an identical set of attribute fields containing projections for these seven groupings across the five SSPs and nine decades.Total PopulationBlack Non-Hispanic PopulationWhite Non-Hispanic PopulationOther Non-Hispanic PopulationHispanic PopulationMale PopulationFemale PopulationMethodology: Documentation for the Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Data currency: This layer was created from a shapefile downloaded April 18, 2023 from SEDAC's Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Enhancements found in this layer: Every field was given a field alias and field description created from SEDAC's Data Dictionary downloaded April 18, 2023. Citation: Hauer, M., and Center for International Earth Science Information Network - CIESIN - Columbia University. 2021. Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/dv72-s254. Accessed 18 April 2023.Hauer, M. E. 2019. Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. https://doi.org/10.1038/sdata.2019.5.Distribution Liability: CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-465-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

The map shows the broadband score from census block to state level using Federal Communications Commission (FCC) Form 477 data. The broadband score is an index based on the FCC’s minimum standard of broadband of 25 megabits per second (Mbps) download and 3 Mbps upload. A geography with speeds of 25/3 Mbps is awarded 100 points. The map uses blending with the World Population Footprint to highlight areas where people actually live within the community.Vintage of data: June 2021When zoomed in to a local level, the Census populated places layer is enabled. The pop-up for each feature calculates, on the fly, the population weighted broadband score for the area, population served by speed, and breakdown by race/ethnicity. All Arcade calculations are done using block level data. Zoom to your local neighborhood and select an area to begin exploring.Use this application for a simple, immersive view.Additional ResourcesFCC Staff Block EstimatesFCC Speed Values Fixed Broadband Deployment Data from FCC Form 477Digital Divide: Broadband Pricing by State, ZIP Code, and Income Level (BroadbandUSA)Open Internet Measurement (M-Lab)Eligibility Area Map Datasets (USDA)