There is a lot of buzz amongst urbanists and demographers about millennials’ preference for urban areas. We’ve found evidence to support this narrative in some areas of Virginia, including indications that they may be staying even after having kids.
But there’s also a lot of talk about baby boomers retiring and moving into cities. Maybe this is happening in other parts of the U.S., but it’s certainly not the case in Virginia. On the contrary, they appear to be heading for the hills. In fact, despite Forbes Magazine naming Virginia its 5th best state to retire in, Virginia does not appear to attract many retirees in general.
Virginia is aging quickly, as can be seen on the map below. From 2000 to 2010, the median age in the Commonwealth rose from 35.7 to 37.5. In some localities, it rose by as many as 5 or 6 years in just that 10-year period. But most of that is due to a gradual decline in birthrates, not older people moving in. From 2000 to 2010, migration accounted for only a slight (1-2%) increase in the population of age groups around retirement age, and that increase was smaller than the state’s overall growth rate.
Recently, I’ve been comparing a number of traits of metropolitan areas based on distance from the core. Here I’m looking at the average densities of each metro area as you travel outwards from the center, calculated using census blocks and 2010 short-form census data. I’ve graphed them in groups of three. Cities with a strong core will have high densities on the left (near the center) that fall off as you travel outwards. Cities whose densities fall off quickly on the right have clearer edges, while those that taper off slowly are more spread out. Click on the graphs to view them full screen.
First are the three major metro areas. Note that the Northern VA graph includes only Virginia census blocks, not the rest of the DC area. Northern VA has the largest population by far, with fairly high densities even several miles into the suburbs. Richmond has the smoothest curve. I used downtown Norfolk as the core for Hampton Roads, but the area’s polycentricity is obvious.
One of the most frequent observations from people who have recently viewed our new Racial Dot Map is the presence of these “little green boxes” scattered throughout the country. The map displays a single dot for every person counted during the 2010 Census and every dot on the map is color-coded by race and ethnicity: non-Hispanic whites = blue; African-Americans = green; Asians = red; Hispanics = orange; and all other races = brown. These peculiar green boxes on the map can be found everywhere and seem oddly out of place:
As our regular readers already know, I’ve been playing around with a lot of dot density maps lately. Today, however, we are releasing something new I think you might enjoy even more.
We decided to rehash Brandon Martin-Anderson’s idea of plotting one dot for every person in the United States, but with an added twist. The new Racial Dot 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 fully interactive so you can zoom into any neighborhood you wish. You can read more about the map and how we created it here.
For the new, interactive, Racial Dot Map project visit HERE.
Our recent post on dot density mapping of U.S., Canadian, and Mexico census data by MIT’s Media Lab got a lot of attention…so we decided to give it a try ourselves, taking a deeper look into census data for Virginia’s major urban centers and smaller cities. All of the dots on the following maps represent one person, as enumerated by the 2010 Census, with a little bit of a twist. Rather than giving everyone a black dot, as MIT’s Media Lab did, we added another layer of data by assigning color dots based on race and ethnicity. The results are quite illuminating…