Nobody lives here: The nearly 5 million Census Blocks with zero population
A Block is the smallest area unit used by the U.S. Census Bureau for tabulating statistics. As of the 2010 census, the United States consists of 11,078,300 Census Blocks. Of them, 4,871,270 blocks totaling 4.61 million square kilometers were reported to have no population living inside them. Despite having a population of more than 310 million people, 47 percent of the USA remains unoccupied.
Green shading indicates unoccupied Census Blocks. A single inhabitant is enough to omit a block from shading
Quick update: If you’re the kind of map lover who cares about cartographic accuracy, check out the new version which fixes the Gulf of California. If you save this map for your own projects, please use this one instead.
The map tends to highlight two types of areas:
- places where human habitation is physically restrictive or impossible, and
- places where human habitation is prohibited by social or legal convention.
Water features such lakes, rivers, swamps and floodplains are revealed as places where it is hard for people to live. In addition, the mountains and deserts of the West, with their hostility to human survival, remain largely void of permanent population.
Of the places where settlement is prohibited, the most apparent are wilderness protection and recreational areas (such as national and state parks) and military bases. At the national and regional scales, these places appear as large green tracts surrounded by otherwise populated countryside.
At the local level, city and county parks emerge in contrast to their developed urban and suburban surroundings. At this scale, even major roads such as highways and interstates stretch like ribbons across the landscape.
Commercial and industrial areas are also likely to be green on this map. The local shopping mall, an office park, a warehouse district or a factory may have their own Census Blocks. But if people don’t live there, they will be considered “uninhabited”. So it should be noted that just because a block is unoccupied, that does not mean it is undeveloped.
Perhaps the two most notable anomalies on the map occur in Maine and the Dakotas. Northern Maine is conspicuously uninhabited. Despite being one of the earliest regions in North America to be settled by Europeans, the population there remains so low that large portions of the state’s interior have yet to be politically organized.
In the Dakotas, the border between North and South appears to be unexpectedly stark. Geographic phenomena typically do not respect artificial human boundaries. Throughout the rest of the map, state lines are often difficult to distinguish. But in the Dakotas, northern South Dakota is quite distinct from southern North Dakota. This is especially surprising considering that the county-level population density on both sides of the border is about the same at less than 10 people per square mile.
Update: On a more detailed examination of those two states, I’m convinced the contrast here is due to differences in the sizes of the blocks. North Dakota’s blocks are more consistently small (StDev of 3.3) while South Dakota’s are more varied (StDev of 9.28). West of the Missouri River, South Dakota’s blocks are substantially larger than those in ND, so a single inhabitant can appear to take up more space. Between the states, this provides a good lesson in how changing the size and shape of a geographic unit can alter perceptions of the landscape.
Finally, the differences between the eastern and western halves of the contiguous 48 states are particularly stark to me. In the east, with its larger population, unpopulated places are more likely to stand out on the map. In the west, the opposite is true. There, population centers stand out against the wilderness.
Ultimately, I made this map to show a different side of the United States. Human geographers spend so much time thinking about where people are. I thought I might bring some new insight by showing where they are not, adding contrast and context to the typical displays of the country’s population geography.
I’m sure I’ve all but scratched the surface of insight available from examining this map. There’s a lot of data here. What trends and patterns do you see?
- The Gulf of California is missing from this version. I guess it got filled in while doing touch ups. Oops. There’s a link to a corrected map at the top of the post.
- Some islands may be missing if they were not a part of the waterbody data sets I used.
Creative Commons Attribution-NonCommercial-ShareAlike
Block geography and population data from U.S. Census Bureau
Water body geography from National Hydrology Dataset and Natural Earth
Made with Tilemill
USGS National Atlas Equal Area Projection
The problem with being so dependent on processing revenue is twofold: First, the profit margins aren’t great. Competition and the commoditization of payment pipes have driven down the fees payments companies charge merchants, while the cut paid out to credit card companies has remained largely the same. Secondly, the public markets today would likely be forced to value Square at a lower revenue multiple than if it made revenue from software sales, which typically come with better profit margins.
It’s not that the margins aren’t great, it’s that a merchant acquirer can only take so much off the top before either the merchants walk or Visa/MC/Amex lock you out. One of the great misconceptions of this investment cycle has been to treat processing volume as revenues (see: Uber, AirBnb, Square). But revenue isn’t the money a processor handles, it’s the money it handles *on which it has a claim.* Payment volumes make for misleading, but exciting, headlines, which is exactly why they’re used so often.
Amazon Fire TV: Meh, nothing to see here
Amazon has finally launched their set top box and I am disappointed.
It is similar to the Apple TV, the Roku, and has some things in common with the Chromecast. It shares the same weakness as all of these devices; a complete lack of understanding on the primary role of television in the lives of televisions heaviest users.
Most of the time spent watching television is not people deliberately choosing programs to watch and then watching them. It’s not binge viewing of mini series. Most of the time spent watching in the US is what we’ll call companionship viewing or background viewing. People flip on the TV choose a channel and then just watch what comes. They do this for hours and hours a day.
Yet another solution to program menuing and program selection is not interesting.
What would be interesting is a way of better monetizing background viewing. Amazon did not do that. Someday someone will.
I basically agree with this article. I will add the caveat that if the workplace always drains out exactly at 5PM you have a staff that is paying more attention to the clock from 4PM on. I think this suggests they are not so passionate about what they are working on.
Ever find yourself bragging about working a 60, 80 or even 100 hour work week or publicly complaining on social media about that incessantly heavy workload of yours? This is not a badge of honor. I…
My understanding is they basically looked at the doppler shift from the expected frequency of the pings which provided a direction vector at each ping (or technically two vectors).
Based on analysis of satellite data, Prime Minister Najib Razak said there was no longer any doubt that the plane flew south into remote waters and could not have landed safely.
Two Observations from Watching the Plane Search Coverage
I often keep the news on in the background while I am working. I have noticed a few things from seeing too much MH370 coverage.
One: Technological Illiteracy
We’re in this era of technology but just because you use twitter, have an iPhone, and blog about tech doesn’t mean you are technologically savvy, it just means you are a tech consumer. I can’t count how many times the issue of why no one called from their cell phones has come up. Or why the plane doesn’t have GPS (it does). Or why the data recorders don’t stream (ever pay an iridium bill?). Or why we just can’t look on Google Earth.
Two: The media is just starting to realize we don’t know shit about the deep ocean.
No one has ever well mapped the bottom of most of the ocean. Of all the oceans in the world we know the least about the Southern Ocean. Our models of winds and currents mid ocean are crude at best. The number of DSVs (deep sea vehicles) that can go down to 8,000 feet on this planet is tiny. Most US Navy subs can’t go much past 1,500-2,000 feet (See the USS Thresher).
If the plane went down near the latitude of the latest satellite photos (44S) we will likely never find the crash site. At some point some flotsam from the crash will show up somewhere. This will only allows us to finally dismiss the “it landed in Asia and the plane is being refitted as a weapon” meme.
I still suspect mechanical problems. It won’t be a simple problem. It won’t be a single problem. This is a very complex system, it can fail in very complex and unpredictable ways. Hopefully someday we’ll know (but it will take some luck).
As an Australian-led search for a missing Malaysia Airlines passenger jet swings into action in the southern Indian Ocean, reports have emerged of a possible sighting of MH370 thousands of kilometres away in the Maldives.
Where is MH370?
I’ve been thinking about where MH 370 went. Here’s a recent map showing where the plane may have gone down.
These two arcs are not flight paths. They are locations where the aircraft could have been at 8:11 AM. The satellite ping defined a circle, the plane was somewhere on that circle at that point in time. The maximum range of the aircraft limited that circle to the arc on the map (I do not know why the two arcs are not contiguous).
Not everywhere on that arc is an equally likely location. For instance since the arc is limited by the maximum range of the plane the extremes of the arcs (up by the ‘stans and deep in the southern ocean) could only be reached if the plane flew directly to those points at maximum efficiency. So if you were to generate a probability density for where on those arcs the plane was at 8:11 you would find the highest likelihood closes to the last known location.
So we’ll start with a 50% chance it went north and a 50% chance it went south. You’re probably going to end up with some kind of normal distribution of probability along each arc.
Now let’s hone the probability further. If the plane was flying outside its optimum envelope of altitude and speed the range would be greatly limited. There’s little radar coverage on the southern arc so there’s no reason to assume if the plane was down there it could not be anywhere on the southern arc. Since the plane was not detected on the northern arc, and there is substantial radar coverage on that arc we can hypothesise that if the plane was on the northern arc it was flying low. Let’s say that cuts the range in half. So instead of the northern arc reaching all the way up to the ‘stans it now ends somewhere in Burma.
On the southern arc there are no obvious other factors, so let’s say the normal distribution holds.
On the northern arc the more time an aircraft spends in range of radar the more likely it is to be seen. The other factors will depend on whose airspace you are talking about. India and China have a much more robust air traffic control systems. But as we have previously largely discounted the far reaches of the northern arc (based on they are on the far end of the probability distribution) combined with the more robust radar systems I think we can basically eliminate anything north or west of the top of Burma. So what’s left on the northern arc. a tiny bit of Laos, a tiny bit of northern Vietnam and a lot of Chinese airspace. If it’s on the northern arc it’s near there. There’s a lot of dense jungle there, if that’s where it crashed it is not surprising it has not yet been spotted.
But as much of the northern arc has been eliminated chances are it’s on the southern arc.