A Genetic Atlas of Human Admixture History

Research article by:

Garrett Hellenthal1,
George B. J. Busby2,
Gavin Band3,
James F. Wilson4,
Cristian Capelli2,
Daniel Falush5,*,
Simon Myers3,6,*,†


1UCL Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK.
2Department of Zoology, Oxford University, South Parks Road, Oxford OX1 3PS, UK.
3Wellcome Trust Centre for Human Genetics, Oxford University, Roosevelt Drive, Oxford OX3 7BN, UK.
4Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK.
5Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
6Department of Statistics, Oxford University, 1 South Parks Road, Oxford OX1 3TG, UK.
↵†Corresponding author. E-mail: myers{at}stats.ox.ac.uk


↵* These authors contributed equally to this work.



ABSTRACT
Modern genetic data combined with appropriate statistical methods have the potential to contribute substantially to our understanding of human history. We have developed an approach that exploits the genomic structure of admixed populations to date and characterize historical mixture events at fine scales. We used this to produce an atlas of worldwide human admixture history, constructed by using genetic data alone and encompassing over 100 events occurring over the past 4000 years. We identified events whose dates and participants suggest they describe genetic impacts of the Mongol empire, Arab slave trade, Bantu expansion, first millennium CE migrations in Eastern Europe, and European colonialism, as well as unrecorded events, revealing admixture to be an almost universal force shaping human populations.

The in-Laws Through History
Admixture, the result of previously distant populations meeting and breeding, leaves a genetic signal within the descendants' genomes. However, over time the signal decays and can be hard to trace. Hellenthal et al. (p. 747) describe a method, using a technique called chromosome painting, to follow the genetic traces of admixture back to the nearest extant population. The approach revealed details of worldwide human admixture history over the past 4000 years.

Received for publication 22 July 2013.
Accepted for publication 20 December 2013.

For full text, follow the link here.

For the genetic atlas of human admixture history website, click here.

How much of humanity is in you Hemisphere?

By Aleks Buczkowski

Interesting map by Bill Rankin. It shows what percentage of Earth’s population lives within 10,000 km of you. In order to compute that Bill had to calculate the percent of all humans that live on the half of the globe centered each point of the Earth.

Almost all of Eurasia is above 80 percent, and a majority of Europe is above 90 percent, with the center of the Human Hemisphere at 92.6% somewhere in western Switzerland. This map makes a lot of sense when we look at distribution of Earth’s population per latitudes and longitudes.

By looking at that graths we can say that roughly 88% of the world’s population lives in the northern hemisphere, and about half the world’s population lives north of 27°N. Looking at that high values in Europe make sense but it’s still striking.

source: Brilliant Maps and Geoawesomeness

“Calling Abidjan” – estimating population distribution through analysis of mobile phone call data records

Big data, big challenge? Together with Harald Sterly of the University of Cologne I presented a little piece of research in the Extended Spatial Analytics session of the German Geography Congress (Deutscher Kongress für Geographie) in Berlin. The project “Calling Abidjan” that we worked on with Kouassi Dongo of Université de Cocody-Abidjan was started after we successfully applied for participation of the D4D Challenge. According to the initiator Orange telecommunications ‘Data for Development’ is “an innovation challenge open on ICT Big Data for the purposes of societal development”. The project allowed us to work with anonymised mobile phone data from individual call records by Orange in the country of Côte d’Ivoire (Ivory Coast).
We were interested in investigating, what non-computer scientists with a social science and urban planning background can do with such data in a more contextual rather that technically driven way and therefore explored how mobile phone call records can be used to better estimate population distribution.

For our analysis we used anonymised call data records consisting of information about the base station, timestamp, and caller ID produced by the approximately 500.000 Orange Télecom users in the country. There were 1079 base stations at the time the data was generated and we were able to work with data covering 183 days. The dataset consisted of 13GB of raw data which some would perhaps call ‘Big Data’ (though I personally do not like this term for many reasons).
The following two (draft) maps give an insight into the results. The purple circles show the distribution and density of population estimates that we derived using only mobile phone call records dataset. To better see the correlation with what other population data tells us about where people live, we did not only produce a normal land area map (on the left, also displaying some basic idea of the topography in the country) but also showed the data on a gridded population cartogram which we generated from the LandScan population grid, the perhaps most detailed population dataset currently available on a globally consistent high-resolution basis:

(click for larger version)

The correlations that show up in these (admittedly quite drafty and basic) maps already gives an idea of our preliminary findings: Only using mobile phone call records we were able to reproduce a number of similar patterns, namely the higher population densities in the Southeast and the Centre, the lower densities in the Northeast and Northwest, and particularly the urban areas of Abidjan, Yamoussoukro, Bouke, Man and others (which especially stand out in the gridded population cartogram and show the details within these areas). However, aggregated on the 255 subprefectures our population dataset was not consistent in all parts. Considerable differences can be seen especially in the Western parts of the country and, most notable, in the Southwest.
Further analysis showed that the call data record analysis generally seems to overestimate population figures in the urban areas and to underestimate in rural areas or areas with little population density.
These could be explained with differentiated mobile phone subscription rates in urban and rural areas, but possibly also inaccuracies in the underlying census data and population models of the AfriPop dataset that we used to validate our approach.
To highlight one other notable difference: in the Southwest (region of Bas-Sassandra) the figures derived from mobile usage is significantly higher which we interpret as caused by a particularly positive economic development of the region (to a large extent related to the port of San-Pédro), resulting in both population growth as well as higher mobile subscription rates.

Our presentation at the conference was far from over-technical, but instead framed in a geographic context of the ongoing urbanisation processes on the African continent and the relevance of mobile phone communication technologies in this region (which I looked atfrom a health perspective with another colleague). To get an idea of the full context, take a look at the slides that we used during our talk available on Slideshare:

The content on this page has been created by Harald Sterly (University of Cologne), Benjamin Hennig and Kouassi Dongo (Université de Cocody-Abidjan) as part of a talk held at the session Extended Spatial Analytics at Deutscher Kongress für Geographie, 1 October 2015, Berlin (Germany). Please contact me for further details and terms of use.

Source: Views of the World

In Focus: Mapping Britain’s Super-rich

George Osborne’s autumn statement on the government’s budgetrekindled the ongoing debate about the fairness of the coalition’s spending cuts. How does it look like if you take a look at the richest and the poorest parts of society? In an article for the “In Focus” section ofPolitical Insight (December 2012, Volume 3, Issue 3) Danny Dorling and Iplotted the geography of the wealthiest of the wealthy in the United Kingdom in comparison to poverty.
The map that I created for this feature displays the distribution of the top 1% of the wealthiest 1% according to information published by the agency WealthInsight, one of the companies trying to gather information on this part of the publication that is a prime target for exclusive marketing. Displayed in the map are data on people with assets in excess of US$30 million and where they have their prime address registered in the UK. The extent of the data is very limited because WealthInsight releases data for only 20 UK cities and regions based on postcode areas (Northern Ireland is a single postcode area which is why we did not correlate that data with Belfast’s overall population). 

Here we have superimposed that data on a population cartogram of the country, drawing circles with an area in proportion to the numbers of super-rich (in red) over people living in each city (in blue). Where they overlap, the circles turn into a purple colour. Where there are more super-rich people than population alone would predict, there is an orange ring around a purple core, as shown around London. Where there are fewer super-rich than the population of a city might predict, there is a blue outer-ring, as around Birmingham. The underlying map shows the distribution of poverty in the UK in five shades of grey.
Cities such as Leeds, Birmingham and Nottingham have fewer super-rich than might be expected – partly because they are not especially affluent urban centres but also, most probably, because their postcode does not include nearby areas such as the North Yorkshire stockbroker belt or the Cotswolds. Aberdeen, in contrast, has some multimillionaires: beneficiaries of the oil boom with an Aberdeen postcode who live some distance from that city. With Manchester it is hard not to speculate that a few extra footballers may have tipped it over the limit.

Here are the bibliographic details:
Hennig, B. D. and Dorling, D. (2012). In Focus: Mapping Britain’s Super‑rich.Political Insight 3 (3): 42.
Article online (Wiley)

The content on this page has been created by Benjamin D. Hennig. You are free use the material under Creative Commons conditions (CC BY-NC-ND 3.0); please contact me for further details. I also appreciate a message if you used my maps somewhere else. High resolution and customized maps are available on request.

Source: Views of the World

The European Union – Politics and People

Starting with the electorate in the Netherlands and the United Kingdom today, voters all across the European Union are going to the polls to elect a new European Parliament(while most of the EU member states hold their vote on Sunday after which the results will be announced). Well, not all voters go to the polls: In 2009 the turnout at the European elections was at an all-time low of 43%. Whether the unusually passionate debate about this year’s election converts into a higher turnout remains to be seen. 

Undoubtedly these elections come at a critical point in the history of the European Union: With the financial crisis still having a high impact on many member states, and with far right and nationalist parties having gained ground at least in the public debates (and probably also in the forthcoming European Parliament), the few who do cast their vote will quite likely have a considerable impact onto the forthcoming European politics. The following series of cartograms shows Europe’s cartographic shapes in the current political climate. They were created for a contribution to the German Wirtschaftswoche magazine (see image above) in the buildup to the elections and show a mix of political, economic and population-related topics that form todays Europe:

(click for larger version)

The content on this page has been created by Benjamin Hennig. You are free to use the material under Creative Commons conditions (CC BY-NC-ND 3.0); please contact me for further details. I also appreciate a message if you used my maps somewhere else. High resolution and customized maps are available on request.

Source: Views of the World

Growing old: European Population Pyramids

“A population pyramid, also called an age pyramid or age picture diagram, is a graphical illustration that shows the distribution of various age groups in a population (typically that of a country or region of the world), which forms the shape of a pyramid when the population is growing.” (Wikipedia)

This population pyramid shows the distribution of the 503 million men and women in the European Union (based on the EU27 countries in 2012) by different age cohorts. The shape can be described as a ‘constrictive pyramid’, which is typical of developed societies with low fertility and mortality rates and with relatively older populations. The population aged 15–65 years is 335 million, whereas nearly one fifth of the total population is over 65 years old. There are only 78 million children aged 0–15. The male:female ratio in the EU is 0.95.
When comparing the population pyramid for the whole of the EU with similar diagrams for separate countries in Europe in- and outside the European Union, it can be seen that in many states the pyramids look similar to that of the overall EU. However, the pyramids for Albania and to a lesser extent Turkey have a more ‘pyramid-like’ shape, suggesting either relatively higher outmigration rates in the recent past and/or a lower life expectancy. Fertility in these countries is not much higher than the EU average. On the other hand, Germany, the Netherlands and Andorra seem to have higher than average elderly populations. Other significant extremes show that Andorra has the highest male:female ratio, while Latvia, Lithuania and Estonia have the lowest ratios. The following overview shows, how all European countries compare in their demographic structure:

(click for larger version)

The overall trend of demographic change is that of an ageing continent. The region with the largest percentage (27%) is Liguria in Italy. The elderly of Europe are also found in greater than normal proportions in northern Germany, and also along the Mediterranean coast and in the interior of France, in northern Spain and southwest England. A lot of these regions are typically attractive for retired people and may also be characterised by low fertility rates. The following map shows the regional distribution of the shares of elderly across Europe displayed on a gridded population cartogram where each of the grid cells is resized to the total number of people living there:

(click for larger version)

While Europe is getting older, there are the increasingly smaller numbers of children that will have to bear the burden of the slowly ageing societies. But there are stark geographical differences in their spatial distribution, as the following map of the proportion of children in an area demonstrates:

(click for larger version)

This gridded population cartogram of Europe shows that the region with the lowest proportion of children as a share of its total population is Principado de Asturias in northwest Spain, where 10.8% of the population is aged 0–15. Other areas with very small percentages (all below 12%) include the German regions of Schleswig-Holstein, Saarland and Thüringen, and the Italian region of Liguria. The region with the highest proportion (40.9%) of its population aged 0–15 is Mardin in Turkey (which is also the region with the lowest percentage of working-age population). Many of the regions with high child populations are in Turkey. The top 15 regions (including Mardin) are all in Turkey, all with over 25% of their populations being children. The next largest area that is not located in Turkey is the region of Border, Midlands and Western in the Republic of Ireland, where 22.4% of the population is aged 0–15.

If you want to find out more about how useful population pyramids as a visualisation tool can be, the following TED talk provides an interesting insight. “Population statistics“, as the related introduction explains, “are like crystal balls — when examined closely, they can help predict a country’s future (and give important clues about the past). Kim Preshoff explains how using a visual tool called a population pyramid helps policymakers and social scientists make sense of the statistics, using three different countries’ pyramids as examples.“



The Social Atlas of Europe
by Dimitris Ballas, Danny Dorling, Benjamin Hennig

Published by Policy Press
[Oder your copy here]

The content on this page has been created by Benjamin Hennig. You are free use to the material for non-commercial purposes under Creative Commons conditions (CC BY-NC-ND 3.0); please contact me for further details. I also appreciate a message if you used my maps somewhere else. High resolution and customized visualisations are available on request.

Source: Views of the World

Asylum seekers in Europe

2,500 people are believed to have died or gone missing on their way to Europe this year already, according to estimates by UNHCR. But it was the image of a young boy found dead on the shores of Turkey which changed the tone in the debate about the ongoing refugee crisis in Europe. While the response to the crisis varies strongly, Campaign groups are calling for a European-wide approach to the crisis. While Germany suspendedthe Dublin regulation to allow regugees into the country and claim asylum regardless of where they entered the European Union, the country also calls for a more equitable system of sharing refugees across the EU similar to Germany’s domestic approach of distributing refugees.
The following cartogram shows the current situation in Europe using Eurostat’s latest statistics about the number of asylum applicants in each country. The data covers the first half of 2015 (January to June) and adds up to 417,430 officially recorded claims in that period in the EU member states. The following map also includes those European countries which are not member of the European Union but part of the Schengen area and it shows each country resized according to the absolute number of asylum applications in that country from January to June 2015:

(click for larger version)

Also included in this cartogram is a reference map that shows the population distribution in Europe – which differs significantly from the main map. In 2014, Germany had the largest absolute number of asylum seekers (as also shown in the map below) in Europe (more than 200,000 compared to over 80,000 of second-ranked Sweden), while the relative distribution saw Sweden on top with 8,365 asylum applicants per 1 million population (Germany: 2,513), Hungary coming second with 4,337 asylum applicants per 1 million population not least due to its geographic location on some of the most frequently usedmigration routes into Europe. Other populous countries saw much lower figures, such as 972 asylum applicants per 1 million population in France and 494 asylum applicants per 1 million population in the UK.
Last year EU countries offered asylum to 184,665 refugees, while according to Eurostatmore than 570,000 migrants applied for asylum. This is a map the situation in 2014, showing the distribution of asylum applicants in Europe:

(click for larger version)

To put these numbers into perspective, the number of refugees heading for Europe is small compared to the global picture that UNHCR published in its Global refugee trendsearlier this year: Around the world, almost 60 million have been displaced by conflict and persecution last year. Nearly 20 million of them are refugees. Lebanon alone houses far more than a million Syrian refugees, a number that is higher than the whole number of refugees expected to arrive in all European nations put together.
While European leaders fail to find a joint – and humane – approach, citizen have started to take action, such as an internet platform dubbed as AirBNB for refugees in Germanyand a Facebook campaign in Iceland where “more than 11,000 families in Iceland have offered to open their homes to Syrian refugees in a bid to raise the government’s cap of just 50 asylum seekers a year”.

More migration-related maps from this website can be found here:

• Global refugee trends: showing countries or origin and destination as documented in the most recent UNHCR report
• Migrants at Sea: A look at where Mediterranean refugees arrived in Europe between 2006 and 2014
• Displaced lifes: Internally displaced people

The colours in the above maps are using a colour scheme developed for the Social Atlas of Europe. Each country shown has a unique colour which allows it to be identified in the differently distorted maps. Furthermore, all countries in these maps are shaded using a rainbow colour scheme, starting with shades of dark red to demarcate the countries with the most recent association with the EU and moving through to a shade of violet for the oldest member states.



The Social Atlas of Europe
by Dimitris Ballas, Danny Dorling, Benjamin Hennig

Published by Policy Press
[Oder your copy here]

The content on this page has been created by Benjamin Hennig using data by Eurostat. Please contact me for further details on the terms of use.

Source: Views of the World

Mapping County Demographic Data in R

BY ARI LAMSTEIN





Ari Lamstein, a technology consultant and author of the free email course, L​earn to Map Census Data in R, provides an introduction to mapping US demographic data using open source software R.

Today I will demonstrate how to map US County demographic data in R. Esri recently announced​ that it is adding additional support for R. This, in turn, has led to an increased interest in R from the GIS community. While R is not a full­fledged GIS program, its ability to import, manipulate and visualize data is phenomenal. Additionally, its packaging system makes it easy for users to create, package and share additional functionality.

We will use the c​horoplethr ​package to map our data. The name “choroplethr” is a play on the words “choropleth” and “R”. In addition to facilitating the creation of choropleth maps, choroplethr ships with demographic statistics from the US Census Bureau.

If you are new to R, you might want to take a quick primer (such as h​ere​ or h​ere)​ before continuing.

Step 1: Install and Load the Packages

As I mentioned above, we will be using the choroplethr package to generate our maps. We will also need the “choroplethrMaps” package. From the R command line, type the following commands. This will install and load the packages:

install.packages(c("choroplethr", "choroplethrMaps"))

library(choroplethr)

library(choroplethrMaps)

Step 2: Create a Simple Map

The choroplethr package comes with a data frame containing 2012 US County Population Estimates. The data frame is called d​f_pop_county.​ We can load it and see the first few elements like this:

data(df_pop_county) 

head(df_pop_county)

## region value 

##1 1001 54590 

##2 1003183226

##3 1005 27469 

##4 1007 22769 

##5 1009 57466 

##6 1011 10779


An important point is that the one column is named r​egion​and one column is named value.​ The regions are c​ounty FIPS codes.​

The function we will use to create county choropleth maps is called c​ounty_choropleth. ​It requires you to pass it a data frame with one column named r​egion ​and one column named v​alue.​

county_choropleth(df_pop_county)

Adding a title and legend is as simple as adding parameters to county_choropleth:​

county_choropleth (df_pop_county, 

                                title ="2012County Population Estimates",

                                legend = "Population")

Step 3: Experiment with the Colors

By default c​ounty_choropleth​ uses seven quantiles to display the color. That is, seven colors are used, and an equal number of regions have the same color. The number of quantiles can be changed with the n​um_colors​ parameter. For example, n​um_colors=2​ will show which counties are above and below the median:

county_choropleth (df_pop_county, 

                              title = "2012 State Population Estimates", 

                              legend = "Population", num_colors = 2)

Using one color will use a continuous scale. This is useful for seeing outliers in the data:

county_choropleth (df_pop_county, 

                               title = "2012 County Population Estimates", 

                               legend = "Population", num_colors = 1)

Los Angeles County (FIPS code 6037) has a population of almost 10 million, which is far larger than any other county in the US.

Step 4: More Demographics

Eight demographic statistics from 2013 are available in the data frame df_country_demographics:

data("df_county_demographics") 

colnames(df_county_demographics) 

##[1] "region"   "total_population" "percent_white" 

## [4] "percent_black" "percent_asian" "percent_hispanic" 

## [7] "per_capita_income" "median_rent" "median_age"


We can map any of them by creating a new column in the data frame called “value”, and setting it equal to the value we want to map:

df_county_demographics$value = df_county_demographics$percent_white county_choropleth (df_county_demographics, 

                               title = "2013 County Demographics\nPercent White", 

                               legend = "Percent White")

Summary

I hope that you have enjoyed this introduction to mapping county demographics in R. Similar functionality exists for mapping state demographics; see the function ?state_choropleth​ for details.

Source

EJSCREEN: Understanding the Connection Between the Environment and Demographics

BY ELIZABETH BORNEMAN



The Environmental Protection Agency has released a new mapping application that allows users to investigate environmental and demographic information about the places they live, work and play. The app, called EJSCREEN, intersects data sets from recent censuses and environmental maps to help users understand the relationship between the environment and demographics.

EJSCREEN utilizes 12 different environmental indicators, six demographic indicators, and 12 environmental justice (EJ) indexes which pair an environmental indicator with a demographic indicator. This allows users to obtain color coded mapping and reports for the specific locations they have selected to look at using the app. The app can then compare these specific reports to state data, regional data and national averages.

The app’s environmental indicators include areas of water pollution, air particulates, and can combine this data with the physical features of a city like factories that may be contributing to particular environmental problems. Demographic information using census data can indicate various economic, social, and ethnic divides in a city and pair them with environmental factors.

The motivation behind the creation of the app is environmental justice; the idea that everyone has the right to live in healthy and happy environments. Unfortunately, factors like income and socioeconomic status can expose people to higher levels of pollutants than those who live in wealthier areas of a city or state. The idea of environmental discrimination can now begin to be quantified after years of scientific speculation.

The app is used by scientists and policy makers as well as community members looking to improve the environmental quality of their communities. The app is the first consistent environmental tool for the Environmental Protection Agency to use that smaller environmental agencies can also utilize in their own conservation efforts. While far from perfect, the app is an important step in developing a greater understanding of the intersection between demographics and the environment in the United States.

References

EJSCREEN – Environmental Protection Agency

The EPA Has a New Tool For Mapping Where Pollution and Poverty Intersect (2015, July 14).

Source