Via NBCNews: First Americans May Have Been Stuck in Beringia for Millennia

First Americans May Have Been Stuck in Beringia for Millennia
BY ALAN BOYLE
WILLIAM MANLEY / IAAR / UNIV. OF COLO.
20140306-131539.jpg

This map shows the outlines of modern Siberia (left) and Alaska (right) with dashed lines. The broader area in a darker shade of green, which is now covered by ocean, represents the Bering land bridge as it existed about 18,000 years ago.
Anthropologists say that the ancestors of Native Americans started making their way from Siberia to the Americas 25,000 years ago over a land bridge that once spanned the Bering Sea — but there are gaps in that story: Why didn’t those migrants leave behind any archaeological traces until 10,000 years later?

Now scientists are homing in on an explanation: During all those millennia, the first Americans were isolated on the land bridge itself. When the land bridge vanished, so did the evidence of that Beringian culture.

The “Beringian Standstill” hypothesis was first proposed by Latin American geneticists in 1997, as a way to explain the genetic evidence indicating that Native Americans started diverging from Siberians 25,000 years ago. In contrast, the archaeological evidence for the first Americans goes back only 15,000 years, to the end of the ice age known as the Last Glacial Maximum.

In this week’s issue of the journal Science, three researchers report new clues that support the claims for Beringia’s lost world. They say fossilized insects, plants and pollen extracted from Bering Sea sediment cores show that central Beringia was once covered by shrub tundra. That would have made it one of the few regions in the Arctic where wood was available for fuel.

Thousands of Siberian migrants might have found refuge in central Beringia until the climate warmed up enough for glaciers to recede, letting them continue their movement into the Americas, the researchers say. “This work fills in a 10,000-year missing link in the story of the peopling of the New World,” Scott Elias, a geography professor at Royal Holloway, University of London, said in a news release.

NANCY BIGELOW / UNIV. OF ALASKA FAIRBANKS
A photo of Alaska’s shrub tundra environment today shows birch shrubs in the foreground and spruce trees scattered around Eight Mile Lake in the foothills of the Alaska Range.
In addition to Elias, the authors of “Out of Beringia?” include lead author John Hoffecker and Dennis O’Rourke. For more about the “Beringian Standstill” concept, check out the reports from the University of Utah and the University of Colorado, plus this online animation and PDF presentation. For an alternate explanation of the spread of the first Americans, check out this archived story.

First published February 27th 2014, 1:37 pm

ALAN BOYLE
Alan Boyle is the science editor for NBC News Digital. He joined MSNBC.com at its inception in July 1996

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Interactive: Racial Dot Maps via: University of Va.

The Racial Dot Map

One Dot Per Person for the Entire United States

Created by Dustin Cable, July 2013

Access and Use Policy


Link to Full Screen Map

Download a High Resolution Image of the U.S. Racial Dot Map (33 MB)

Please read the Access and Use Policy, which describes how this map can be used and how it should be cited.

NEW: You can see the new Congressional Dot Map project with election results here.

The Map

This map is an American snapshot; it provides an accessible visualization of geographic distribution, population density, and racial diversity of the American people in every neighborhood in the entire country. The map displays 308,745,538 dots, one for each person residing in the United States at the location they were counted during the 2010 Census. Each dot is color-coded by the individual’s race and ethnicity. The map is presented in both black and white and full color versions. In the color version, each dot is color-coded by race.

All of the data displayed on the map are from the U.S. Census Bureau’s 2010 Summary File 1 dataset made publicly available through the National Historical Geographic Information System. The data is based on the “census block,” the smallest area of geography for which data is collected (roughly equivalent to a city block in an urban area).

The map was created by Dustin Cable, a former demographic researcher at the University of Virginia’s Weldon Cooper Center for Public Service. Brandon Martin-Anderson from the MIT Media Lab deserves credit for the original inspiration for the project. This map builds on his work by adding the Census Bureau’s racial data, and by correcting for mapping errors.

The Dots

Each of the 308 million dots are smaller than a pixel on your computer screen at most zoom levels. Therefore, the “smudges” you see at the national and regional levels are actually aggregations of many individual dots. The dots themselves are only resolvable at the city and neighborhood zoom levels.

Each dot on the map is also color-coded by race and ethnicity. Whites are coded as blue; African-Americans, green; Asians, red; Hispanics, orange; and all other racial categories are coded as brown.

Shades of Purple, Teal, and Other Colors

Since dots are smaller than one pixel at most zoom levels, colors are assigned to a pixel depending on the number of colored dots within that pixel. For example, if a pixel contains a number of White (blue dots) and Asian (red dots) residents, the pixel will be colored a particular shade of purple according to the proportion of each within that pixel.

Different shades of purple, teal, and other colors can therefore be a measure of racial integration in a particular area. However, a place that may seem racially integrated at wider zoom levels may obscure racial segregation at the city or neighborhood level.

Take the Minneapolis-St. Paul metro area as an example:

While Minneapolis and St. Paul may appear purple and racially integrated when zoomed out at the state level, a closer look reveals a greater degree of segregation between different neighborhoods in both cities. While some areas remain relatively integrated, there are clear delineations between Asian, black, and white neighborhoods.

Lightly Populated Areas

Toggling between color-coded and non-color-coded map views in lightly populated areas provides more contrast to see differences in population density. Take North and South Dakota as illustrative examples:

In the black and white version, it is easier to see the smaller towns and low-density areas than in the color-coded version. Different monitor settings and configurations may make it harder or easier to see color variations in lightly populated areas, but the non-color-coded map should always show differences in population density fairly well.

Dots Located in Parks, Cemeteries, and Lakes

The locations of the dots do not represent actual addresses. The most detailed geographic identifier in Census Bureau data is the census block. Individual dots are randomly located within a particular census block to match aggregate population totals for that block. As a result, dots in some census blocks may be located in the middle of parks, cemeteries, lakes, or other clearly non-residential areas within that census block. No greater geographic resolution for the 2010 Census data is publicly available (and for good reason).

A more accurate portrayal of the geographic distribution of residents is possible if data is available on the location of parks, buildings, and/or physical addresses. Individual dots could therefore be conditionally placed based on this data.

The following is an example of using additional data to improve the dot density map for the City of Charlottesville, Virginia:

No Extra Data

Using Additional Address and Park Data

By conditioning the location of dots based on physical address and excluding locations with parks or commercial property, the dot map for Charlottesville becomes a more accurate portrayal of the population distribution of the city. However, the City of Charlottesville is unusual in that this data is made publicly available. There are no nationwide datasets for all parks or physical addresses. As a result, the national-level Racial Dot Map does not make these adjustments.

The Data

All of the data displayed on the map are from the 2010 Summary File 1 (SF1) tables from the U.S. Census Bureau. Table P5, “Hispanic or Latino Origin by Race,” was merged with block-level state shapefiles from the National Historical Geographic Information System. Five racial categories were created based on the data in table P5: non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, Hispanic or Latino, and a category for all other racial categories including the multiracial identifications. The sum of all five categories equals the total population.

Methodology

Python was used to read the 50 state and District of Columbia shapefiles (with the merged SF1 data). The GDAL and Shapely libraries were used to read the data and create the point objects. The code retrieves the population data for each census block, creates the appropriate number of geographic points randomly distributed within each census block, and outputs the point information to a database file. The resulting file has x-y coordinates for each point, a quadkey reference to the Google Maps tile system, and a categorical variable for race. The final database file has 308,745,538 observations and is about 21 GB in size. The processing time was about five hours for the entire nation.

The database file was then sorted by quadkey and converted to a .csv format. SAS was able to do this within an hour without crashing.

Processing 2.0.1 for 64-bit Windows was used to create the map tiles. The Java code reads each point from the .csv file and plots a dot on a 512×512 .png map tile using the quadkey reference and x-y coordinates. The racial categorical variable is used to color-code each plotted dot. This process used the default JAVA2D renderer, but other platforms may work better using P2D. Map tiles were created for Google Maps’ zoom levels 4 through 13 to make the final map. A non-color-coded map was also produced to help add more contrast for lightly populated areas. In total, the color-coded and non-color-coded maps contain 1.2 million .png files totaling about 7 GB. Producing all of the map tiles in Processing took about 16 hours for the two maps.

The Google Maps API is used to display the map tiles. Map tiles with zero population are never created using the above method. Therefore, an index was used to tell the map application whether a tile exists in order to prevent 404 errors.

The entire code is up on GitHub and was adapted from code developed by Brandon Martin-Anderson and Peter Richardson in order to account for the racial coding and errors in reading the shapefiles.

Electoral Geography: How Growing Majority-Minority Districts Effect Elections

Voting BallotWhen talking about ELECTORAL GEOGRAPHY and the importance of analyzing the effects of a changing voting population, the 2012 U.S. Census revealed a change that probably does not shock most. ETHNIC groups are on the rise and non-white majority districts are decreasing. MAJORITY-MINORITY districts have the ability to impact REDISTRICTING of voting boundaries every ten years.  The ruling political party of the state conducts the redistricting, and if it can be proven to be done in their favor, it is known as GERRYMANDERING (illegal yet is still happens-Right…I don’t know either…).

Here is an excerpt from a New York Times article that explains how highly populated ETHNIC ENCLAVES can be dealt with and used for political advantage.

“So if Democrats are in charge of the redistricting process in New York in 2020, perhaps they can find a way to squeeze out another Democratic seat or two by splitting up minority voters. And if Republicans are in charge in Texas, perhaps they can avoid giving up as many seats to Democrats by diluting the minority vote in cities like Dallas and Houston.”

Basically, if there are too many minority voters who might have a tendency to vote for a Democrat in the district, they will have more votes than they need to win the district, so why not spread them out over more iffy ones?

Similarly, if a state losses or gains a larger portion of people due to MIGRATION or NATURAL POPULATION INCREASE, the 435 representative seats will need to be REAPPORTIONED (redistributed) across the states.  States such as California, Texas, New York, and Illinois who already have a large number of majority-minority districts, might earn themselves more fighting power on the FEDERAL level.

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Repost from: http://www.nationaljournal.com/congress/since-1982-minority-congressional-districts-have-tripled-graphic-20120413

Since 1982, Minority Congressional Districts Have Tripled—GRAPHIC

By  and David Wasserman

Updated: May 29, 2013 | 9:33 p.m.
April 13, 2012 | 6:54 a.m.

In 1980, the nonwhite share of the U.S. population was 17 percent, and by 1982 there were 35 majority nonwhite districts. In the 2010 census, the nonwhite share of the nation’s population had ballooned to 28 percent, mostly fueled by Latino growth. But over the same time period, the number of nonwhite majority districts has more than tripled, to 106. For the first time ever, a majority of states–26–will contain majority nonwhite districts, in part thanks to new deliberately drawn minority-majority seats in Washington state where Asian-Americans are the largest minority group.