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?

(by Joseph Kerski)This map is for use in the "What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysiscourse. The map contains population characteristics by country for 2013.These data come from the Population Reference Bureau's 2014 World Population Data Sheet.The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.PRB analyzes complex demographic data and research to provide the most objective, accurate, and up-to-date population information in a format that is easily understood by advocates, journalists, and decision makers alike.The 2014 year's data sheet has detailed information on 16 population, health, and environment indicators for more than 200 countries. For infant mortality, total fertility rate, and life expectancy, we have included data from 1970 and 2013 to show change over time. This year's special data column is on carbon emissions.For more information about how PRB compiles its data, see: https://www.prb.org/

The data is from:

https://simplemaps.com/data/world-cities

We're proud to offer a simple, accurate and up-to-date database of the world's cities and towns. We've built it from the ground up using authoritative sources such as the NGIA, US Geological Survey, US Census Bureau, and NASA.

Our database is:

• Up-to-date: It was last refreshed on May 11, 2025.
• Comprehensive: Over 4 million unique cities and towns from every country in the world (about 48 thousand in basic database).
• Accurate: Cleaned and aggregated from official sources. Includes latitude and longitude coordinates.
• Simple: A single CSV file, concise field names, only one entry per city.

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?

This dataset is about book subjects. It has 11 rows and is filtered where the books is The fourth part of the world : the race to the ends of the earth, and the epic story of the map that gave America its name. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

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.

The map data is derived from the United Nations Environment Programme (UNEP) for the years ranging from 1992 to 2006. The map shows the countries that participated in the Convention on Biological Diversity by year. From UNEP: "The Convention on Biological Diversity promotes conservation of biodiversity among nations through scientific and technological cooperation, access to financial and genetic resources, and transfer of ecologically sound technologies." Online resource: http://geodata.grid.unep.ch URL original source: http://www.biodiv.org/world/parties.asp

Official Technical Information * Distance: 25.8 miles * Start Elevation: 8,756 feet * Finish Elevation: 9,080 feet * Low Point: 8,745 feet * High Point: 9,945 feet * Total Climbing: 4,011 feet

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

The map data is derived from the United Nations Environment Programme (UNEP) for the years 1990, 1995, 2000, and 2005. The map shows the concentration of the population within 100 kilometers of coast by country measured in thousands of people. Online resource: http://geodata.grid.unep.ch URL original source: http://geodata.grid.unep.ch

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.

The map data is derived from the United Nations Environment Programme (UNEP) for the years ranging from 1958 to a projected 2012. The map shows the concentration dependency ratio within each country expressed as a percent of the total renewable water resources originating outside the country. 0% indicates that the country does not receive water from neighboring countries, or in the case of data that there is a lack of data for this country so that zero was put in to allow production of the heated map. 100% indicates that the country in fact receives all of its water from other countries. Online resource: http://geodata.grid.unep.ch URL original source: http://www.fao.org/ag/agl/aglw/aquastat/dbase/index.htm

This map is designed to be used as a base map by GIS professionals to overlay other thematic layers such as demographics or land cover. The base map features shaded relief imagery, bathymetry, and coastal water features designed to provide a neutral background for other data layers. The map was compiled from a variety of sources from several data providers, including the U.S. Geological Survey, NOAA, and Esri. The base map currently provides coverage for the world down to a scale of ~1:1m and coverage for the continental United States and Hawaii to a scale of ~1:70k. For more information on this map, including our terms of use, visit us online at http://goto.arcgisonline.com/maps/World_Terrain_Base

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.

Aim

To understand the representativeness and accuracy of expert range maps, and explore alternate methods for accurately mapping species distributions.

Location

Global

Time period

Contemporary

Major taxa studied

Terrestrial vertebrates, and Odonata

Methods

We analyzed the biases in 50,768 animal IUCN, GARD and BirdLife species maps, assessed the links between these maps and existing political and various non-ecological boundaries to assess their accuracy for certain types of analysis. We cross-referenced each species map with data from GBIF to assess if maps captured the whole range of a species, and what percentage of occurrence points fall within the species’ assessed ranges. In addition, we use a number of alternate methods to map diversity patterns and compare these to high resolution models of distribution patterns.

Results

On average 20-30% of species’ non-coastal range boundaries overlapped with administrative national boundaries. In total, 60% of areas with the highest spatial turnover in species (high densities of species range boundaries marking high levels of shift in the community of species present) occurred at political boundaries, especially commonly in Southeast Asia. Different biases existed for different taxa, with gridded analysis in reptiles, river-basins in Odonata (except the Americas) and county-boundaries for Amphibians in the US. On average, up to half (25-46%) species recorded range points fall outside their mapped distributions. Filtered Minimum-convex polygons performed better than expert range maps in reproducing modeled diversity patterns.

Main conclusions

Expert range maps showed high bias at administrative borders in all taxa, but this was highest at the transition from tropical to subtropical regions. Methods used were inconsistent across space, time and taxa, and ranges mapped did not match species distribution data. Alternate approaches can better reconstruct patterns of distribution than expert maps, and data driven approaches are needed to provide reliable alternatives to better understand species distributions.

Methods Materials and methods

We use a combination of approaches to explore the relationship between species range maps and geopolitical boundaries and a subset of geographic features. In some cases we used the density of species range boundaries to explore the relationship between these and various features (i.e. administrative boundaries, river basin boundaries etc.). Additionally, species richness and spatial turnover are used to explore changes in richness over short geographic distances. Analyses were conducted in R statistical software unless noted otherwise. All code scripts are available at https://github.com/qiaohj/iucn_fix. Workflows are shown in Figure S1a-c with associated scripts listed.

Species ranges and boundary density maps

ERMs (Expert range maps) were downloaded from the IUCN RedList website for mammals (5,709 species), odonates (2,239 species) and amphibians (6,684 species; https://www.iucnredlist.org/resources/grid/spatial-data). Shapefile maps for birds were downloaded from BirdLife (10,423 species, http://datazone.birdlife.org/species/requestdis), and for reptiles from the Global Assessment of Reptile Distributions (GARD) (10,064 species; Roll et al., 2017). Each species’ polygon boundaries were converted to a polylines to show the boundary of each species range (Figure S1a-II; codes are lines 7 – 18 in line2raster_xxxx.r ; xxxx varies based on the taxa). The associated shapefile was then split to produce independent polyline files for each species within each taxon (see Figure S1a-I, codes are lines 29 to 83 in the same file above.).

To generate species boundary density maps, species range boundaries were rasterized at 1km spatial resolution with an equal area projection (Eckert-IV), and stacked to form a single raster for each taxon (at the level of amphibians, odonates, etc.). This represented the number of species in each group and their overlapping range boundaries (Figure S1b-II, codes are in line2raster_all.r). Each cell value indicated the number of species whose distribution boundaries overlapped with each cell, enabling us to overlay this rasterized information with other features (i.e. administrative boundaries) so that the overlaps between them can be calculated in R. These species boundary density maps underlie most subsequent analyses. R code and caveats are given in the supplements, links are provided in text and Figure S1.

Geographic boundaries

Spatial exploration of species range boundaries in ArcGIS suggested that numerous geographic datasets (i.e. political and in few cases geographic features such as river basins) were used to delineate the species ranges for different regions and taxa (this is sometimes part of the methodology in developing ERMs as detailed by Ficetola et al., 2014). Thus in addition to analyzing the administrative bias and the percentage of occurrence records within each species’ ERM for all taxa, additional analyses were conducted when other biases were evident in any given taxa or region (detailed later in methods on a case-by-case basis).

For all taxa, we assessed the percentage of overlap between species range boundaries and national and provincial boundaries by digitizing each to 1km (equivalent to buffering thie polyline by 500m), both with and without coastal boundaries. An international map was used because international (Western) assessors use them, and does not necessarily denote agreed country boundaries (https://gadm.org/). The different buffers (500m, 1000m, 2500m, 5000m) were added to these administrative boundaries in ArcMap to account for potential, insignificant deviations from political boundaries (Figure S1b). An R script for the same function is provided in “country_line_buffer.r”.

To establish where multiple species shared range boundaries we reclassified the species range boundary density rasters for each taxa into richness classes using the ArcMap quartile function (Figure S1). From these ten classes the percentage of the top-two, and top-three quartiles of range densities within different buffers (500m, 1000m, 2500m, 5000m) was calculated per country to determine what percentage of highest range boundary density approximately followed administrative borders. This was done because people drawing ERMs may use detailed administrative maps or generalize near political borders, or may use political shapefiles that deviate slightly. It is consequently useful to include varying distances from administrative features to assess how range boundary densities vary in relation to administrative boundaries. Analyses of relationships between individual species range boundaries and administrative boundaries (coastal, non-coastal) were made in R and scripts provided (quantile_country_buffer_overlap.r).

Spatial turnover and administrative boundaries

Heatmaps of species richness were generated by summing entire sets of compiled species ranges for each taxon in polygonal form (Figure 1; Figure S1b-I). To assess abrupt diversity changes, standard deviations for 10km blocks were calculated using the block statistics function in ArcMap. Abrupt changes in diversity were signified by high standard deviations based on the cell statistics function in ArcGIS, which represented rapid changes in the number of species present. Maps were then classified into ten categories using the quartile function. Given the high variation in maximum diversity and taxonomic representation, only the top two –three richness categories were retained per taxon. This was then extracted using 1km buffers of national administrative boundaries to assess percentages of administrative boundaries overlapping turnover hotspots by assessing what proportion of political boundaries were covered by these turnover hotspots.

Taxon-specific analyses

Data exploration and mapping exposed taxon and regional-specific biases requiring additional analysis. Where other biases and irregularities were clear from visual inspection of the range boundary density maps for each taxa, the possible causes of biases were assessed by comparing range boundary density maps to high-resolution imagery and administrative maps via the ArcGIS server (AGOL). Standardized overlay of the taxon boundary sets with administrative or geophysical features from the image-server revealed three types of bias which were either spatially or taxonomically limited between: 1) amphibians with county borders in the United States, 2) dragonflies and river basins globally and 3) gridding of distributions of reptiles. In these cases, species boundary density maps were used as a basis to identify potential biases which were then explored empirically using appropriate methods.

For amphibians, counties in the United States (US) were digitized using a county map from the US (https://gadm.org/), then buffered by with 2.5km either side. Amphibian species range boundary density maps were reclassified showing where species range boundaries existed (with other non-range boundary areas reclassified as “no data,”) and all species boundaries numerically indicated (i.e. values of 1 indicates one species range boundary, values of 10 indicates ten species range boundaries). Percentages of species boundary areas falling on county and in the buffers, in addition to species range boundaries which did not overlap with county boundaries were calculated to give measures of what percentage of the species boundaries fell within 2.5km of county boundaries.

For Odonata, many species were mapped to river basin borders. We used river basins of levels 6-8 (sub-basin to basin) in the river hierarchy (https://hydrosheds.org) to assess the relationship between Odonata boundaries and river boundaries. Two IUCN datasets exist for Odonata; the IUCN Odonata specialist group spatial dataset

This statistic shows the share of ethnic groups in Australia in the total population. 33 percent of the total population of Australia are english.

Australia’s population

Australia’s ethnic diversity can be attributed to their history and location. The country’s colonization from Europeans is a significant reason for the majority of its population being Caucasian. Additionally, being that Australia is one of the most developed countries closest to Eastern Asia; its Asian population comes as no surprise.

Australia is one of the world’s most developed countries, often earning recognition as one of the world’s economical leaders. With a more recent economic boom, Australia has become an attractive country for students and workers alike, who seek an opportunity to improve their lifestyle. Over the past decade, Australia’s population has slowly increased and is expected to continue to do so over the next several years. A beautiful landscape, many work opportunities and a high quality of life helped play a role in the country’s development. In 2011, Australia was considered to have one of the highest life expectancies in the world, with the average Australian living to approximately 82 years of age.

From an employment standpoint, Australia has maintained a rather low employment rate compared to many other developed countries. After experiencing a significant jump in unemployment in 2009, primarily due to the world economic crisis, Australia has been able to remain stable and slightly increase employment year-over-year.

Route Description: Head up Vasquez Road to Arapaho Road west of town. Follow the dirt road at the end of Arapaho and look for the signs. Best to Go Up chickadee and bomb down Blue Sky Distance 14.7 miles Distance PRO 22.7 miles Start Elevation 9,075 feet Max Elevation 10,085 feet Max Elevation PRO 11,025 feet

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?

Phenotypic data: A total of 510 lines from the global durum panel (GDP) were evaluated for reaction to five isolates of the fungal pathogen Pyrenophora tritici-repentis. The five isolates were Pti2 (race 1), 86-124 (race 2), 331-9 (race 3), L13-192 (race 4), and DW5 (race 5). The GDP lines were inoculated in a greenhouse when plants were at the two- or three-leaf stage. Plants were grown in a randomized complete block design consisting of one replicate per experiment, and each experiment was repeated three times. Following inoculation, the plants were placed in misting chambers with 100% relative humidity at 21 °C for 24 h, and then placed in a growth chamber under a 12-h photoperiod at 21 °C. Disease reactions were scored 7 days after inoculation following the 1–5 scale where 1 was highly resistant and 5 was highly susceptible. Seedlings of the GDP were also infiltrated with cultures containing the necrotrophic effectors (NEs) Ptr ToxA and Ptr ToxB. Approximately 20 μl of NE-containing culture filtrate (CF) was infiltrated into two plants (1 leaf per plant) in each cone using a needleless 1-ml syringe. The infiltrated area (~3 cm) was marked with permanent marker on the leaves, and the plants were placed in a growth chamber (21 °C, 12-h photoperiod) for 5 days. The reactions were scored using a 0–3 scale where 0 indicated no reaction and 3 indicated severe necrosis or chlorosis. The expansion of chlorosis beyond the boundaries of Ptr ToxB infiltration to the distal end of the leaf was scored separately using 0 or 1 meaning the absence or presence of the symptom, respectively. This trait is noted as ‘ToxB_Spread’ in the data file. Genotypic data: The GDP lines were genotyped using the Illumina iSelect 90K SNP array. Markers were filtered using previously published genetic maps, and marker sequences were aligned to the Svevo.v1 reference genome. SNP imputation was performed, and the redundant markers were pruned based on genome wide linkage disequilibrium, which resulted in a data set with no missing SNP calls. The hapmap information of 855 genotypes and 13,373 SNPs developed by M. Maccaferri is located on the GrainGenes website (https://wheat.pw.usda.gov/GG3/global_durum_genomic_resources). The haplotype map of the 855 genotypes was reduced to 510 genotypes that were selected for phenotypic tests. The SNP data set was selected according to the set filtering for minor allele frequency (MAF) > 5% that resulted in a marker set containing 12,222 SNPs.