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.

From ictir2015.org.

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.

Videos of Bicycles Being Cool


I’m taking a little break from the recent string of in-depth research to look at something a little more fun. Recently, I came across this article by Sarah Goodyear about how Salt Lake City will be the first city in America to implement a protected bicycle intersection. The plan is based on Dutch designs, as well as research by Portland-based planner and designer Nick Falbo.

Protected Intersections For Bicyclists from Nick Falbo on Vimeo.

I hope that I’ll be able to see this the next time I’m in Salt Lake (all Mormons seem to end up getting stuck in Salt Lake’s orbit at some point). But also, I was glad to see this video of how a Dutch design could be interpreted for and shared with an American audience. It just made me think of some cool bicycle videos I’ve seen recently and wanted to share. For instance, and speaking of the Dutch, here is a cool video of bicycle traffic in Utrecht.

I almost wish part of this video was shown in real time rather than sped up so that you could see how cyclists interact in real time, and how cycling in the Netherlands is not the sort of extreme, aggressive sport cycling that seems to be the only kind that’s allowed in some parts of America. It is indeed a casual activity for regular people.

Speaking of aggressive sport cycling though, these videos are really cool.

 

 

At least of the videos I’ve seen, these sort of downhill urban mountain bike races seem to only take place in Latin America (although the participants seem to largely be speaking English), but you could imagine them taking place in an Italian hilltown or a Swiss village. I just don’t know where we could do it in America; we rarely build on the kind of slopes where doing this would be any fun to watch, we almost never build with those sort of narrow curves and passageways, and even when we do build on hills it’s mostly taken up by rich people who probably wouldn’t want something like this in their backyard.

I wonder what videos like this do for cycling. I could see them sort of raising awareness that cycling is a viable option, but at the same time I could see it creating the perception that the only people who cycle are these sort of extreme types. Either way though, they’re pretty fun to watch.

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.

Therefore:

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.

Therefore:

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

Streetmix.net: My New Favorite Website


A short one today, but I really wanted to mention a new website I found called Streetmix. It allows you to create street cross sections on the fly, with different options for buildings, sidewalk elements, and travel lanes. For instance, in less than a half hour, I made five different cross sections for different parts of Columbia Pike, a major thoroughfare in Arlington, Virginia. The eastern section of the road has a lot of Modernist housing blocks that are tall and set way back from the street:columbia-pike-at-scott-stBut further west, it has more of a Main Street feel, with tall buildings coming right up to wider sidewalks:columbia-pike-at-walter-reed-drUp until very recently there were plans to put a streetcar line in on Columbia Pike, which were defeated by a zealot who convinced people that a BRT would be cheaper (which it wouldn’t be) and politically feasible (which it isn’t because it would take away a travel lane in each direction and the turn lane, which Arlington isn’t allowed to do as per an agreement with the Commonwealth). Here’s what this section would look like with a streetcar (note that the streetcar lanes are not dedicated and would still allow car traffic):columbia-pike-at-walter-reed-dr-with-streetcarAnd with BRT:columbia-pike-at-walter-reed-dr-with-brtFinally, as Columbia Pike heads further west and into the Bailey’s Crossroads area of Fairfax County, it widens into a strip mall super street:columbia-pike-at-moray-lnAll of this is to say that Streetmix makes it easy to create and compare cross sections of streets on the fly. While I wouldn’t use it for final documents, it gives you something to play around with and to work off of. And for planning nerds like me, it’s just a lot of fun.

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