Gentrification and Market-based Zoning

A few months ago we moved back to Philadelphia from DC. Washington is a very nice place, but we only make a little bit more than the average American household (which makes about $52,000 a year), while the average income in DC is about $90,000, which means that we couldn’t afford anything. For instance, in Philadelphia in 2013, we lived 1.4 miles from the center of town for $1,200 a month. When we moved to DC, we lived 4.5 miles from the center of town for $1,350 a month. There were several reasons why we chose to move back, but largely, it was because


When we came back to Philly, we knew that we wanted to live south of Market Street, and beyond that, we didn’t care too much. We spent several weeks looking all over South Philadelphia and toured a number of apartments including a very nice one in Point Breeze. Several neighborhoods in Philadelphia are experiencing rapid gentrification, but Point Breeze is in many ways at the forefront. If you don’t know what gentrification is, it’s basically when rich(er) people start moving into a poor(er) area, redeveloping it and driving up rents and property taxes, and driving out existing residents. Though this is strictly speaking an economic issue, because minorities in America tend to live in poorer neighborhoods, it often affects them disproportionately.

Planners, architects and developers have a mixed relationship with gentrification, and so do I personally. I mean, I don’t want to hurt poor minorities, but I also want access to affordable housing close to where I work (by the way, we ended up deciding against Point Breeze; we found a cheaper apartment in South Philly). But why do I have to go to neighborhoods like Point Breeze to find affordable housing in the first place? Why can’t I find housing close to my office in Center City? The obvious answer is that because it is too expensive, but why is that the case?

An interesting argument was presented in this article by Kriston Capps of Citylab. Capps points out that tech, a common boogeyman in the discussion of gentrification in San Francisco, is actually not the problem; it’s anti-development/NIMBY residents of rich neighborhoods. These folks hold much more influence in City Hall than their poorer neighbors, so they can get zoning ordinances and other restrictions passed that keep development from happening in their back yards. But people still want to move to San Francisco, developers will still build new housing to provide for them, and since they can’t do it in the rich neighborhoods where they actually want to live, they develop in the closest poor neighborhood. John Mangin, in his article The New Exclusionary Zoning, says, “Don’t blame in-movers or developers for gentrification—they’d rather be in the high-cost neighborhoods. Blame the exclusionary practices of people in the high-cost neighborhoods.” Mangin argues that in addition to zoning, the lengthy approval processes required by many desirable cities increases the cost of building housing, as well as increasing the time to develop it, and privileges large, savvy, politically-connected developers over smaller neighborhood builders.

Capps proposes that development should be expanded in richer neighborhoods, amending zoning laws and making decisions that are best for the city (or the region) and not necessarily for individual neighborhoods. Mangin argues that we need to pursue policies that either increase the supply of housing or decrease the demand for it in poorer neighborhoods. This includes allowing some development (because not allowing any development simply drives up the prices of the existing housing stock until they cost too much for the current residents and are bought up by more affluent move-ins), while at the same time advocating for more development in the high-demand areas where rich NIMBYs are keeping new folks from moving in. Governments could also impose regulations on new development that would help existing residents, such as requiring some of the taxes assessed on new development to go toward investing in the neighborhood; or creating something of a cap-and-trade market for density, where if people want to exclude development from their neighborhood, they have to help pay for it to happen elsewhere.

What is argued by both of these authors is that zoning is out of sync with housing demand, particularly in rich areas, leading to spillover of new move-ins in poorer neighborhoods. So what would it look like if a city’s zoning were rewritten to reflect market demand? Let’s take a look at Philadelphia.

Below we have the existing housing density, in units per acre, of the Philadelphia Metropolitan Statistical Region by census tract. Density is concentrated in the City of Philadelphia, as well as some of it’s suburbs, particularly the string running southwest through Delaware County down to Wilmington, Delaware, and beyond; and running southeast through Camden County, New Jersey. (The land use categories shown correspond to the densities required for different types of transit service, according to the Victoria Transport Policy Institute: 0-2 supports no transit, 2-4 supports regional rail, 4-7 supports minimal local bus, 7-9 supports intermediate local bus, 9-12 supports light rail, 12-15 supports rapid transit, and over 15 supports frequent local bus service)

Density Existing-01

This is our baseline, and lets us know how many housing units already exist in an area. The next thing we need to know about a tract is the median housing value.

Median Value-01

This shows us that housing value is highest along the Main Line; in some of the suburbs of Wilmington; and around New Hope, PA and Moorestown, NJ. Now that we have the median value, we can multiply that by the number of housing units to get an idea of the total value of housing in a tract, which will indicate to us the demand for housing in that area.

Total Value-01

We see a similar pattern to median value, as well as a slight concentration in Center City, Philadelphia (while the median value is a bit lower than in some of the rich suburbs, there are so many more units that the total value is quite high). Now that we have a measurement for the demand, we need to translate that back into housing units. The average value of a home in the United States is $175,700, so we can divide the total value of each tract by that number to get an idea of how many units that area should supply.

New Density-01

What we can see from this is that many of the suburbs, particularly in affluent Montgomery County, are not pulling their weight, and should take on a greater share of the region’s new development. Housing density in Philadelphia remains high; however, taking a look at the change in units per acre reveals some interesting patterns.

Change-01Philly Change-01

Moving from the edge of the region to the center, we see that some of the outermost communities could actually afford to lose a few units per acre. However, by and large, density should increase as you get closer to the city, particularly along the affluent Main Line to the west. At the same time, there are several small cities in the area that are overbuilt, including Pottstown, PA, Norristown, PA, Upper Darby, PA, Darby, PA, Chester, PA, Camden, NJ, Salem, NJ, and Wilmington, DE. As we look at Philadelphia itself, the neighborhoods of Northeast and South Philadelphia should grow modestly, while the more affluent neighborhoods in the northwest should grow more steeply. This is followed by a ring of overbuilt areas in North and West Philadelphia, as well as Point Breeze and parts of South Philly. However, Center City and University City are underdeveloped, and should grow considerably. This underdevelopment is what is fueling the gentrification of areas where the green and the red meet, such as Point Breeze, Mantua, and Kensington.

Percent Change-01Philly Percent Change-01

It is also interesting to look at the percent change, rather than total units per acre, to tell you something about the degree to which these neighborhoods will be affected by change. Some areas, such as Piedmont, DE, and New Hope, PA, would only see a modest change in the absolute numbers, but because their existing densities are so low it may feel like a large shift. Others, such as Center City, would see significant growth in total units per acre, but because their existing density is already high it will not have as significant an impact on the area. And while there was a lot of red on that first map, the area most impacted by a decline in density is actually more limited when you look at the percentage, which the heaviest impacts in Norristown, Darby, Chester, Camden, and North and West Philadelphia.

So, who would support a plan like this and who would oppose it? The most obvious answer is that rich homeowners, both in the city and the suburbs, may not take kindly to a plan like this. They would see this, not entirely inaccurately, as a threat to their property values and their way of life. Mangin advocates several “smaller scale reforms that preserve a space for sub-local [neighborhood] politics while altering, sometimes subtly, the incentives that political actors face and the procedures by which they arrive at decisions,” to try and get richer residents on board as much as possible.

Initially, many residents of poorer neighborhoods might also oppose it, because zoning by demand would mean severely downzoning several poorer areas, which may look to some residents like “benign neglect” or, even worse, the sort of problems that arose with urban renewal and the use of eminent domain in the middle of the last century. This sort of “depletion” or “neighborhood triage” has been strongly opposed by neighborhood groups who want to preserve their communities and see it as a method for removing poor residents to make room for future development. This sort of opposition was seen in the defeat of the “Team Four Plan” in St. Louis (If you want to pay for it, you can read Patrick Cooper-McCann’s recent article on it in the Journal of Planning History here, or if you’re a cheapskate like me you can get the gist of the article by reading his master’s thesis here for free). It would be important to implement this strategy in steps, such that new housing in desirable areas was available early so that anyone who wished to move out of poorer neighborhoods may have an opportunity to do so, while those who wished to stay behind could do so, safe in the knowledge that restrictive zoning would prevent new development from infringing on their community while they would be allowed to stay there as long as they wished.

The people that a plan like this would really be good for would be middle-class people wanting to move in from outside the city or to move up to a more desirable neighborhood. As it is now, the urban middle class is squeezed between neighborhoods they can’t afford and neighborhoods where they are seen as unwanted agents of change and distress. Opening up more development opportunities, both in Center City and in densified suburbs along the Main Line and elsewhere, would provide for more opportunities for affordable urban living without the guilt of hurting those lower down on the economic ladder.

Gentrification is a hard nut to crack, but it’s important to look at it as a problem of restricted housing supply in affluent areas not being able to meet the demand for development, which then spills over into less affluent neighborhoods. Changing our zoning laws to better reflect the demand for housing in desirable neighborhoods would help ameliorate gentrification and allow more options for middle-income families in cities. While efforts like this would face an uphill battle against entrenched interests, bureaucratic roadblocks, and NIMBYism, in the words of John Mangin, “The options are pretty clear: build more, or stand by as low-income and middle-class people get priced out of ever-wider swaths of the country.”

We can make our roads a lot more bike-friendly. Here’s how.

I recently contributed a post to Greater Greater Washington about bicycle safety based on one of the work sessions I attended at StreetsCamp. Check it out here.

Walkable Does Not Necessarily Mean Big

People I talk to about urbanism tend to think that I’m a “city person.” and I can see why they would think that, since I eventually learned to love Philadelphia, live in DC (okay, Arlington, but I would live in DC if I could afford it), and generally disdain suburbs. But people who know me better know that New York or Los Angeles is not my ideal. When I think of a perfect place, the one that made me want to be an urban designer and the one I would like to replicate in my work, I think of Northampton, Massachusetts.


Northampton isn’t big. It’s population is approximately 28,592, and the way that towns are set up in Massachusetts, that number includes a lot of people who live out in the countryside and not “in town.” But even though it isn’t big, Northampton feels urban, because you can walk to anything you would need on a daily basis and could live quite comfortably without owning a car.

There is a strong correlation between a place feeling urban and it having a high Walkscore. I’ve mentioned Walkscore before, but to sum it up, it is a measure of how easily one can reach their everyday needs on foot. It goes from zero to 100, and a score below 50 being car-dependent, 50-69 being somewhat walkable, 70-89 very walkable, and over 90 a walker’s paradise.

Parts of Northampton are walker’s paradises, as were all the neighborhoods in Philadelphia that I lived in and all the neighborhoods in DC where I would live if I could afford it. I decided to look and see where one could find walker’s paradises, so I searched the whole country for apartments with a Walkscore over 90 (the apartments are important because no matter how many shops and restaurants you have, if no one can walk to them from their home, you essentially have a mall). I mapped the results, noting that many places may have an apartment building or two with a Walkscore of 90 while the neighborhood as a whole is below that, and that other places are “true” walker’s paradises, where the entire neighborhood has a Walkscore above 90.

Click to enlarge.

Click to enlarge.

“Now wait a second,” you might be thinking, “New York is the biggest dot!” And that’s true, but New York is so big that it has the most of many things, including walkable neighborhoods. What’s important is that Los Angeles, the second biggest city in the United States, is not the second biggest dot, nor is Chicago, Houston, or any other city larger than the one that actually is second biggest, San Francisco. In fact, I think Houston is the best example of how big and urban/walkable are not the same thing. Houston, despite its population of 2,239,559 and its size of 627.8 square miles, only has three walker’s paradises, none of which are “true” walker’s paradises. This means that in urbanism terms it is not the equivalent of Chicago (population 2,695,598 with 17 walker’s paradises), but of Lawrence, Massachusetts (population 77,657 with three walker’s paradises).

CorrelationIn fact, as the graph shows, population explains about 60% of how walkable a place is. While a large city does allow for more services, it’s size has nothing to do with how those services are laid out, which has a huge impact on how urban a place is. That is why San Francisco (second highest on the graph above) is so walkable, even more so than simple population projections would predict, while Los Angeles (second furthest to the left on the graph) is actually less walkable than one would project a city of its size to be. San Francisco was built around the pedestrian and the streetcar; Los Angeles was built around the automobile.

So small cities, don’t think that you can’t be great urban places just because you’re not very big. Great urbanism comes from putting the pedestrian first, from planning great streets with a mix of housing, working, and services, and from making a pleasant and vibrant environment for people. Make these a priority and you will be urban, regardless of size.

Alexander’s Distribution of Towns

In a recent post, I analyzed what it would look like to apply the first pattern of Christopher Alexander‘s A Pattern Language to North America. This pattern, titled Independent Regions, created 50 new, small nations or regions within what is today the United States and Canada. The next thing I wanted to try was applying Alexander’s second pattern, The Distribution of Towns, to one of those new regions. The pattern reads:

If the population of a region is weighted too far toward small villages, modern civilization can never emerge; but if the population is weighted too far toward big cities, the earth will go to ruin because the population isn’t where it needs to be, to take care of it.


Encourage a birth and death process for towns within the region, which gradually has these effects:
1. The population is evenly distributed in terms of different sizes- example, one town with 1,000,000 people, 10 towns with 100,000 people each, 100 towns with 10,000 people each, and 1000 towns with 1000 people each.
2. These towns are distributed in space in such a way that within each size category the towns are homogeneously distributed all across the region.

This process can be implemented by regional zoning policies, land grants, and incentives which encourage industries to locate according to the dictates of the distribution.

apl2diagramtowns of 1,000,000 – 250 miles apart
towns of 100,000 – 80 miles apart
towns of 10,000 – 25 miles apart
towns of 1,000 – 8 miles apart

However, as discussed in the previous post, not all regions that meet Alexander’s population criteria automatically meet his spacing criteria when you move from Pattern 1 to Pattern 2. Several regions are too large and sparse, while several more are too small and dense.

Click to enlarge.

Click to enlarge.

So to analyze Pattern 2 without worrying about redistributing the population of the regions until they were the right size, I wanted to pick one of the “Goldilocks” regions that is already about the right size. I went with the Washington DC Region.

Click to enlarge.

Click to enlarge.

Now for the distribution part. Let’s say that we have a region, Region A, with capital city City 1 in its center.Diagram1-01Alexander recommends one very large city per region, so we’ll assume that City 1 has a population of about a million and that it’s the only city of it’s scale in the region. Our first step is to identify our cities of 100,000 people. While Alexander recommended making these cities 80 miles apart, I used 72 miles to simplify the math a little bit. So we want to make sure that these cities of 100,000 are 72 miles from City 1, as well as from each other.Diagram2-01Next we want to identify our towns of 10,000. Again, I fudged Alexander’s math a bit, and identified towns that are 24 miles (1/3 of 72) from any larger cities or from each other.Diagram3-01And finally, we want to identify towns of 1,000 people, eight miles from any larger town and from each other.Diagram4-01That’s what it looks like in an ideal world. However, in the real world, we’re not starting with a blank slate, and rather than founding new cities that are perfectly spaced from DC, I wanted to identify existing cities located in the right place. When you do that, it looks like this:

Click to enlarge.

Click to enlarge.

What this technique doesn’t consider, however, is why cities are located where they are: largely, access to resources, and access to transportation. They aren’t distributed evenly across a landscape; they’re bunched up in the places that have access to mineral, agricultural or intellectual wealth, and places that have access to ports, railroads, and highways. Disregarding this truth about cities means that some cities that are not located the right distance away from one another can get hosed. Case in point, Richmond, a city of over 200,000 people at the center of a metro area with over 1.2 million, because it isn’t spaced correctly relative to DC, is relegated to the 10,000 people tier, meaning that a huge population would be resettled to areas like Tappahannock (current population 2,397) or Powhatan (current population 49) which are spaced correctly.

Also, by using Alexander’s spacing suggestions, one does not arrive at his suggestion for how many towns there should be. Based on this distribution, you end up with one town of 1,000,000, eight towns of 100,000, 45 towns of 10,000, and 319 towns of 1,000. That adds up to 2,569,000 people. But in the last pattern, we determined that the Washington DC Region would have a population of 8,917,843. So where are the other 6 million plus people living?

The answer to that actually comes from another pattern, number 5 in Alexander’s book, Lace of Country Streets:

The suburb is an obsolete and contradictory form of human settlement.


In the zone where city and country meet, place country roads at least a mile apart, so that they enclose squares of countryside and farmland at least one square mile in area. Build homesteads along these roads, one lot deep, on lots of at least half an acre, with the square mile of open countryside or farmland behind the houses.

Alexander doesn’t specify how wide this “zone where city and country meet” is, and if we’re got small cities every eight miles across an entire region, it would almost follow that these zones fill in the rest of the space between towns. If we take a square mile, and line it on all four sides with lots that are just over a half acre, we come up with 116 lots per square mile. If we multiply that by the average household size in the US of 2.58 persons per household, we get about 300 people per square mile in the countryside. If you multiply that by the 24,945 square miles covered by our region, you get 7,483,500 people. If you remove some to consider that a considerable portion of our region is the Chesapeake Bay and that some of those 24,945 square miles are already taken up by towns and, therefore, the Lace of Country Streets wouldn’t apply, it basically adds up to the rest of the people we were looking for.

So what we end up with is a pretty even population distribution across the entire region with a bit more concentrated in the many small towns and a lot more concentrated in the few big cities. but is this even distribution good for people or for the environment? I take issue with part of Alexander’s rationale for this pattern:

Two different necessities govern the distribution of population in a region. On the one hand, people are drawn to cities: they are drawn by the growth of civilization, jobs, education, economic growth, information. On the other hand, the region as a social and ecological whole will not be properly maintained unless the people of the region are fairly well spread out across it, living in many different kinds of settlements – farms, villages, towns, and cities – with each settlement taking care of the land around it. Industrial society has so far been following only the first of these necessities. People leave the farms and towns and villages and pack into the cities, leaving vast parts of the region depopulated and undermaintained.

But what does it mean to “take care of” and “maintain” the countryside? It seems to me that the countryside does pretty well without us up in its business. Alexander mentions the ecology of the city and how large cities are bad ecologically, but (a) some of Alexander’s patterns that I will discuss later address that and (b) by pulling people away from the countryside and concentrating them in cities, it allows for the countryside to function as what we need it most for (a carbon sink), and allows people to take advantage of shared walls and transit systems which greatly reduce our per-capita carbon footprint. If it were me, I’d distribute the population more like this:

Click to enlarge.

Click to enlarge.

Making all the big cities even bigger and, most importantly, denser, and leaving only small villages sprinkled throughout the countryside. As long as enough people live out there to grow the food and operate the mines for everyone else, there’s no reason to have everyone flung out across the landscape. The best thing we can do for the countryside really is to leave it alone. As far as getting people access to natural environments, we’ll talk about that soon, when I take a look at Alexander’s 3rd pattern, City Country Fingers.

Best Country in the World: Professional Networking Edition

After I wrote my last post about which countries are the best in the world and three of the top five were in Scandinavia, I got curious about what it might take to move there. From my quick research, it seems that in all the Scandinavian countries, if you want to get a job, you need to (a) speak the language (working on it) and (b) know someone, because companies want to hire natives before they want to try and bring someone in from another country (or at least a country outside of Europe). So I was curious who I might know in Scandinavia, and if that could again help me focus on one language to learn instead of three. Of course I don’t know anyone off the top of my head, but I knew a way to find people I know who might themselves know someone: LinkedIn.

LinkedIn allows you to do a fairly precise search for contacts, including location, industry, and how well you know someone (1st, 2nd, or 3rd-level contact, it’s kind of like Six Degrees of Kevin Bacon). So I searched for 2nd-level contacts living in Denmark, Norway and Sweden, and in the fields of architecture and planning, research, government administration, and non-profits.

I got about 99 results, although I could only look at about 70 of them before hitting LinkedIn’s commercial search limit (not cool, LinkedIn). I then mapped them out, marking how well their field of work matches my interests, and how well I know our mutual contact, since I have several people on my LinkedIn that I’ve met once or have only corresponded with via email.Map-01I have four cities with a decent number of contacts: Copenhagen (København), Oslo, Stockholm, and Trondheim. Of those, the most contacts in a related field are in Copenhagen and Stockholm. But the highest quality contacts, those where I actually know our mutual contact well, are in Copenhagen. So maybe, for now, I’ll focus on learning Danish.

Finding the Best Country in the World

I’m a bit paranoid. Like when I get home, stick my head in the door, look around, and tell my wife, “Yep, no racoons,” I’m only sort of kidding (a racoon did break into my house when I was a kid so that’s not a totally crazy thing to say). One way this paranoia manifests itself is in creating certain contingency plans. For instance, if I had to flee the country where would I go? The easy answer would be the other English-speaking countries (Canada first because it’s close, the UK second because it’s further but I have friends there, New Zealand third because it looked nice in Lord of the Rings and Australia last because I’m sorry but I do not trust your fauna). But what if, say, all the English speaking countries got nuked or something? Or if we find out that global warming only affects them and they get wiped off the face of the earth? Or they all get conquered by North Korea? Well, if that’s the case, it’s time to flee to a non-English speaking country and learn another language. But which one?

At one point I couldn’t decide, and I was studying Danish, Dutch, Irish, Spanish and Swedish on Duolingo all at the same time (yes, I know next to no one speaks Irish, that was just for kicks). Eventually I cut out Irish and Spanish, but also picked up German, so I wasn’t much better off. So I wanted to figure out, if I’m going to have to flee to another country, including non-English speaking countries, which ones should I focus on so that I can actually study their language effectively?

I thought the Human Development Index (HDI) was a good place to start. It measures a country’s well-being based on a combination of several factors, including life expectancy, education and income. Its top five countries are Norway, Australia, Switzerland, the Netherlands, and the United States. However, HDI doesn’t measure everything. In fact, in 2010 they introduced a new index, the Inequality-Adjusted Human Development Index (IHDI) to try and fill in some of the gaps. This new adjustment changes the top five to Norway, Australia, the Netherlands, Switzerland, and Germany, and kicked the US down to 28th, three spots below Greece.

My thought was that the best way to cover as many factors as I could was to combine as many different indices as I could to create one super index that would bring in enough factors to create a truly well-rounded indicator of what the best country was. I combined the IHDI, Democracy Index, Human Poverty Index, Social Progress Index, World Happiness Report, Global Peace Index, Legatum Prosperity Index, Where-to-be-born Index, Satisfaction with Life Index, and OECD Better Life Index to create what I call the Best Country Index.

Index Index2

So the top five are Norway, Sweden, Switzerland, Denmark, and Finland. Well, right now Norwegian isn’t offered on Duolingo, so even if it’s number one I’m not studying it until they finish creating the course for it, so for now I’ll just have to assume it’s somewhere between Danish and Swedish. And I’m not even going to try with Finnish; I had a hard enough time with Irish, and that’s still an Indo-European language, I can only imagine the sort of trouble I would have learning a Finno-Ugric one. So that leaves me (until Duolingo adds Norwegian) with Swedish, German, and Danish. Well, at least I can stop studying Dutch for now.

For those of you who would like a searchable version of the list above, here it is:

  1. Norway, 2.9877
  2. Sweden, 2.9732
  3. Switzerland, 2.9724
  4. Denmark, 2.9702
  5. Finland, 2.8872
  6. Canada, 2.8858
  7. Netherlands, 2.8778
  8. Australia, 2.8257
  9. New Zealand, 2.7938
  10. Austria, 2.7792
  11. Germany, 2.6754
  12. Ireland, 2.6643
  13. Iceland, 2.6580
  14. Belgium, 2.5993
  15. United Kingdom, 2.4850
  16. United States, 2.4315
  17. France, 2.3879
  18. Japan, 2.3759
  19. Spain, 2.3667
  20. Slovenia, 2.2898
  21. Czech Republic, 2.2789
  22. Italy, 2.2484
  23. Costa Rica, 2.2419
  24. Chile, 2.1414
  25. Luxembourg, 2.1216
  26. South Korea, 2.1083
  27. Singapore, 2.0700
  28. Poland, 2.0554
  29. Slovakia, 2.0191
  30. Portugal, 2.0180
  31. Israel, 2.0131
  32. United Arab Emirates, 1.9953
  33. Argentina, 1.9745
  34. Uruguay, 1.9719
  35. Kuwait, 1.9342
  36. Cyprus, 1.9248
  37. Panama, 1.9047
  38. Estonia, 1.8984
  39. Hungary, 1.8743
  40. Croatia, 1.8356
  41. Malaysia, 1.8159
  42. Taiwan, 1.8096
  43. Malta, 1.8054
  44. Greece, 1.7843
  45. Brazil, 1.7838
  46. Trinidad and Tobago, 1.7417
  47. Mauritius, 1.7076
  48. Hong Kong, 1.7074
  49. Mexico, 1.6853
  50. Latvia, 1.6692
  51. Lithuania, 1.6575
  52. Saudi Arabia, 1.6445
  53. Colombia, 1.5608
  54. Romania, 1.5560
  55. Bulgaria, 1.5293
  56. Mongolia, 1.5285
  57. Jamaica, 1.5272
  58. Venezuela, 1.5111
  59. Serbia, 1.4994
  60. Dominican Republic, 1.4682
  61. Thailand, 1.4682
  62. Qatar, 1.4612
  63. Indonesia, 1.4586
  64. Ecuador, 1.3933
  65. Montenegro, 1.3921
  66. Paraguay, 1.3908
  67. Botswana, 1.3800
  68. Peru, 1.3728
  69. El Salvador, 1.3698
  70. Brunei, 1.3360
  71. Bhutan, 1.3356
  72. Philippines, 1.3135
  73. Bahamas, 1.2969
  74. Suriname, 1.2959
  75. Nicaragua, 1.2713
  76. Guatemala, 1.2650
  77. Sri Lanka, 1.2616
  78. Albania, 1.2463
  79. Vietnam, 1.2340
  80. Macedonia, 1.2286
  81. Cuba, 1.2284
  82. Guyana, 1.2281
  83. Bolivia, 1.2224
  84. Namibia, 1.2217
  85. Kazakhstan, 1.2140
  86. Tunisia, 1.2068
  87. Bosnia and Herzegovina, 1.1818
  88. Oman, 1.1790
  89. Turkey, 1.1783
  90. South Africa, 1.1738
  91. Antigua and Barbuda, 1.1625
  92. Belarus, 1.1555
  93. Jordan, 1.1534
  94. East Timor, 1.1433
  95. Moldova, 1.1385
  96. Seychelles, 1.1310
  97. St. Kitts and Nevis, 1.1231
  98. Honduras, 1.1190
  99. Ghana, 1.1158
  100. Liechtenstein, 1.1140
  101. Uzbekistan, 1.0997
  102. Vanuatu, 1.0995
  103. Barbados, 1.0759
  104. Kyrgyzstan, 1.0725
  105. Dominica, 1.0601
  106. Morocco, 1.0518
  107. Belize, 1.0164
  108. Fiji, 1.0143
  109. Ukraine, 1.0137
  110. China, 1.0126
  111. Georgia, 1.0109
  112. Bahrain, 0.9991
  113. Cape Verde, 0.9768
  114. St. Vincent and the Grenadines, 0.9734
  115. Russia, 0.9629
  116. Andorra, 0.9617
  117. India, 0.9617
  118. Armenia, 0.9466
  119. Bangladesh, 0.9446
  120. Zambia, 0.9422
  121. Senegal, 0.9332
  122. Azerbaijan, 0.9238
  123. St. Lucia, 0.9183
  124. Lebanon, 0.8938
  125. Gabon, 0.8898
  126. Nepal, 0.8886
  127. Samoa, 0.8789
  128. Papua New Guinea, 0.8788
  129. Tanzania, 0.8685
  130. Algeria, 0.8633
  131. Cambodia, 0.8305
  132. Maldives, 0.8239
  133. Lesotho, 0.8086
  134. Iran, 0.8053
  135. Laos, 0.7953
  136. Madagascar, 0.7853
  137. Kenya, 0.7636
  138. Benin, 0.7603
  139. Tajikistan, 0.7579
  140. Solomon Islands, 0.7452
  141. Tonga, 0.7371
  142. Sao Tome and Principe, 0.7316
  143. Grenada, 0.7213
  144. Egypt, 0.7050
  145. Palestine, 0.6883
  146. Malawi, 0.6800
  147. Mozambique, 0.6666
  148. Burkina Faso, 0.6468
  149. Congo, 0.6432
  150. Uganda, 0.6267
  151. Mali, 0.6129
  152. Angola, 0.6028
  153. Liberia, 0.5733
  154. Sierra Leone, 0.5716
  155. Libya, 0.5652
  156. Cameroon, 0.5604
  157. Haiti, 0.5543
  158. Gambia, 0.5511
  159. Djibouti, 0.5483
  160. Mauritania, 0.5472
  161. Nigeria, 0.5384
  162. Togo, 0.5369
  163. Swaziland, 0.5051
  164. Pakistan, 0.5007
  165. Rwanda, 0.4921
  166. Comoros, 0.4904
  167. Iraq, 0.4849
  168. Yemen, 0.4505
  169. Kiribati, 0.4407
  170. Turkmenistan, 0.4333
  171. Niger, 0.4307
  172. Syria, 0.4233
  173. Guinea, 0.3683
  174. Ethiopia, 0.3662
  175. Equatorial Guinea, 0.3662
  176. Cote d’Ivoire, 0.3357
  177. Burma, 0.3104
  178. Zimbabwe, 0.2542
  179. Burundi, 0.2502
  180. Guinea-Bissau, 0.2489
  181. Eritrea, 0.1878
  182. Afghanistan, 0.1861
  183. Central African Republic, 0.1709
  184. Chad, 0.1657
  185. Sudan, 0.1458
  186. Democratic Republic of the Congo, 0.0675
  187. Somalia, 0.0205
  188. North Korea, 0.0169

A Study on Regional Governments Part III: Back to North America

I’ve been working on regional governments on and off for several years, and this time I feel like I’ve made some progress. There are a number of reasons for subdividing North America into new regional governments, as I’ve already discussed in Part I and Part II. But the main idea comes from architect and known crazy person Christopher Alexander‘s book A Pattern Language, a book about development and building patterns that goes from the very large to the very small scale. The very first pattern in the book is:

Metropolitan regions will not come to balance until each one is small and autonomous enough to be an independent sphere of culture…


Wherever possible, work toward the evolution of independent regions in the world; each with a population between 2 and 10 million; each with its own natural and geographic boundaries; each with its own economy; each one autonomous and self-governing; each with a seat in a world government, without the intervening power of larger states or countries.

This time I tried to be more official and used a GIS to do my work rather than Google and Wikipedia. In the past I’ve used counties as my basic geographic and population unit, but that was problematic, seeing as Los Angeles County has over 10 million people already. It’s also problematic to go down to the level of cities, towns and places, because then you run into 8 million-strong New York City, and the second you lump in any of its suburbs it puts you over 10 million. This time I went in between and used county subdivisions, which will both divide Los Angeles County up as well as take advantage of the fact that New York City, while being one city, is also five different counties.

I also used information from both the US and Canada. Initially I wanted to do Mexico as well, since there are a number of major cities on the Mexican side of the border that draw people in from the US. However, I figure that the ties between the US and Canada are much stronger and that it would be easier to integrate their populations. Also, the fact that the US and Canada make census data and GIS files fairly easy to obtain while Mexico doesn’t might have something to do with it.

My methodology goes something like this. Let’s say you’ve got Country A with Cities 1, 2, and 3 and a population of 30 million.

Process1City 1 and City 2 are the largest cities in the country, so the country would be divided between these two cities.Process2Region 1’s new population is 18 million and Region 2’s is 12 million. Since Region 1 is the larger of the two and since it is still above 10 million in population, it needs to be divided again. The second largest city in Region 2 is City 3, so new boundaries need to be redrawn between that and the two existing cities.Process3The new Region 3 took 8 million people from Region 1 and 2 million from Region 2, giving all three regions a nice round population of 10 million. At this point we no longer need to subdivide them any further.

Of course, when you do this on real land and using real borders, it doesn’t come out quite as clean. It looks a bit more like this:

Click to enlarge.

Click to enlarge.

TableThe largest metropolitan area in Anglo-America is New York City and the second is Los Angeles, so they became the first two regions. Since they were both over 10 million and New York City was the larger of the two, Chicago was the third region, then Dallas, then Philadelphia, etc.

What is apparent in this exercise is that, since the regions are based on population and not geography, the size of the region correlates to population density. The densest parts of Anglo-America, the northeast seaboard and southern California, have the smallest regions, while the sparsely populated Rocky Mountains and northern Canada have enormous regions. What I haven’t realized in past iterations of this project is that it complicates Alexander’s second pattern, the distribution of towns:

If the population of a region is weighted too far toward small villages, modern civilization can never emerge; but if the population is weighted too far toward big cities, the earth will go to ruin because the population isn’t where it needs to be, to take care of it.


Encourage a birth and death process for towns within the region, which gradually has these effects:
1. The population is evenly distributed in terms of different sizes- example, one town with 1,000,000 people, 10 towns with 100,000 people each, 100 towns with 10,000 people each, and 1000 towns with 1000 people each.
2. These towns are distributed in space in such a way that within each size category the towns are homogeneously distributed all across the region.

This process can be implemented by regional zoning policies, land grants, and incentives which encourage industries to locate according to the dictates of the distribution.

apl2diagramtowns of 1,000,000 – 250 miles apart
towns of 100,000 – 80 miles apart
towns of 10,000 – 25 miles apart
towns of 1,000 – 8 miles apart

The last part of that section, which describes the spacing of towns, leads to a specific size that a nation of a given population should be. I experimented with a few town distribution models and determined that based on a combination of Alexander’s population and town distribution recommendations, a region should not be less than approximately 30,000 square miles, without being more than approximately 130,000 square miles. Several of the regions I’ve created based on population alone are either too large or too small to meet these criteria.

Click to enlarge.

Click to enlarge.

So to comply with Alexander’s recommendations, the small regions would have to have their population distributed more sparsely and their boundaries enlarged to accommodate that lower density, and the large regions would either have to become smaller and more dense, or have their populations concentrated in certain areas while others are left as uninhabited wastes, as is essentially the case in much of the mountain deserts of the western US and the arctic regions of Canada.

While I see the value of Alexander’s argument for smaller governmental units, I find his arguments for the distribution of towns a bit more dubious, especially when it comes to areas that are too dense for his recommendations. I generally feel that the best way to preserve undeveloped land is not to distribute people evenly across it, but to concentrate them all in one area and leave more of the land untouched. That’s why New Yorkers are some of the greenest people on earth; they leave the countryside alone, and are packed dense enough that they don’t have to use cars and take advantage of the energy savings of dense housing, making their environmental impact considerably lower than someone in a lower density area.

That being said, at some point I would like to take one of the Goldilocks regions I’ve created like San Francisco or Pittsburgh and try to create a distribution of towns like what Alexander recommends, just to see what it would look like. But as far as drawing new regions goes, I’m pretty happy with this version, and I don’t see myself redoing this project again.

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