Tag Archives: Sacramento

A Study on Regional Governments


I’m finally finished with the regional governments project that I’ve been working on for something like six weeks and that has kept me from writing in that time.  In the future I may want to use more accurate measures and publish this, so I’m going to practice by presenting this as scientific research.  So here goes.

Introduction

I wanted to study the possibility of creating regional governments in the United States for three reasons.  First, because I agree with The Charter of the New Urbanism when it states that “The metropolitan region is a fundamental economic unit of the contemporary world. Governmental cooperation, public policy, physical planning, and economic strategies must reflect this new reality.” Also, I feel that the lines that divide government designations in America are arbitrary at best and, in many cases, don’t reflect reality on the ground.

It is interesting to compare government designations in Europe and America.  If you look at a map of Europe, you will notice that none of the boundaries are straight lines.  This is because the boundaries do a much better job of reflecting things like topography and real cultural divides.

The US, on the other hand, was drawn up for ease of division by immigrants who considered it to be essentially uninhabited.  Many counties, particularly in the Midwest, are just boxes laid out along the survey lines created by Jefferson, regardless of the topography on the ground.  There is only one state in the US that doesn’t have a straight line for a boundary, and that is Hawaii.  I don’t believe that this is advantageous.  Take my hometown of Pittsburgh, for example.  Pittsburgh is within the state of Pennsylvania, which it shares with Philadelphia.  This is about all the two cities share.  Their economies, populations, ethnic groups, and cultures are very divergent.  When the two cities are thrown into competition, often for State funds, Pittsburgh, with its lower population and generally higher standard of living, often does not get as much as Philadelphia.  According to Pittsburgh Quarterly, “It is often lamented that the Pennsylvania legislature tilts to the east, favoring Philadelphia over Pittsburgh.”  It would be advantageous to Pittsburgh to not be as closely linked to Philadelphia, which it in reality has little relation to.  At the same time, there are areas in Ohio, West Virginia and Maryland that are closer to Pittsburgh than to any major city in their respective states, and would do well to be involved with the politics of that city.  At a lower level, there are people who live just outside of Alleghany County, where Pittsburgh is, so that they don’t have to pay the higher taxes in that county, yet they still use Alleghany County roads and services without paying their fair share.  I believe that, along with state borders, county borders should be amended to reflect the reality on the ground of how central city services are used.

This brings me to my third point: I don’t think, in many parts of the country, that county governments serve a needful purpose in the way that they did in the past.  When the country was made up of many small, independent towns, counties worked to unite them in common purpose.  Now, in our metropolitan world, counties are often used as tax havens or otherwise don’t serve their original purpose.  Rhode Island, Connecticut and Massachusetts have done away with their county governments, streamlining political processes and ironically creating “small government” in some of the most liberal states in the Union.

But how does one define a region?  For an answer, I turned to Christopher Alexander, as I often do, who, in A Pattern Language, encourages us to, “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.”  With that in mind, I began my research.

Methods

I relied heavily on Wikipedia and Google for this research, which is why it isn’t publishable in its current state.  To begin, I set benchmarks for regions.  I wanted to make three maps to visualize regions of different sizes, so I decided that, so as to be in line with Alexander’s requirements, I would have one map with a minimum population of 2 million per region, another map with a minimum of 5 million per region, and a third with a minimum of 8 million.  Then I got a list of cities in the US with a population of over 100,000.

I went through every county in the country and measured the distance from the county seat to the nearest city of over 100,000, marking them on a map with a different color to designate different cities.  I used Google Maps’ walking distance feature because I felt that it would do a better job of reflecting topography than simple as-the-crow-flies measurement, while at the same time being more accurate than the car distances since cars are expected to travel on highways over large distances, which may be faster but not as direct.  Also, I wanted to measure it as if some sort of catastrophe happened and people were unable to use cars, thus being forced to walk.

After finding out which counties were closest to what cities, I counted up the population of the counties that were marked for a given city based on the most recent data on Wikipedia.  Some of this information was as recent as 2009 estimates, while some was as old as the 2000 census.  Hopefully when the new census comes out I can redo this project with better results.  If the population of the area was below the population benchmark that I had set, then the city was removed from the list and the counties were remeasured and marked for the next closest city.  I then mapped them out on large national maps.

Results

This work generated three maps with corresponding lists of cities and the populations of the regions based on these cities.

This first map is based on regions with a minimum population of 2 million, with the following cities anchoring the regions and their given regional populations, from highest to lowest population of the central city:

  1. New York City, NY (10,861,700)
  2. Los Angeles, CA (11.624,092)
  3. Chicago, IL (7,312,584)
  4. Houston, TX (5,807,864)
  5. Phoenix, AZ (6,662,822)
  6. Philadelphia, PA (7,398,857)
  7. San Antonio, TX (3,836,400)
  8. San Diego, CA (3,322,432)
  9. Dallas, TX (3,742,720)
  10. San Jose, CA (3,329,396)
  11. Detroit, MI (2,384,057)
  12. San Francisco, CA (4,318,813)
  13. Jacksonville, FL (3,040,268)
  14. Indianapolis, IN (3,652,091)
  15. Austin, TX (2,079,499)
  16. Columbus, OH (3,736,506)
  17. Fort Worth, TX (3,586,057)
  18. Charlotte, NC (3,878,660)
  19. Memphis, TN (2,502,573)
  20. Boston, MA (6,831,829)
  21. Baltimore, MD (4,243,534)
  22. El Paso, TX (2,874,140)
  23. Seattle, WA (7,070,662) (This includes both Alaska and Hawaii, as will be explained below)
  24. Denver, CO (5,896,137)
  25. Nashville, TN (2,909,035)
  26. Milwaukee, WI (3,184,691)
  27. Washington, DC (3,031,043)
  28. Louisville, KY (2,949,715)
  29. Portland, OR (4,607,152)
  30. Oklahoma City, OK (2,542,568)
  31. Atlanta, GA (6,151,488)
  32. Kansas City, MO (3,814,650)
  33. Fresno, CA (3,032,183)
  34. Sacramento, CA (5,691,903)
  35. Omaha, NE (2,506,874)
  36. Miami, FL (2,785,746)
  37. Cleveland, OH (2,249,989)
  38. Raleigh, NC (2,252,861)
  39. Tulsa, OK (2,843,868)
  40. Minneapolis, MN (4,490,267)
  41. St. Louis, MO (5,069,109)
  42. Tampa, FL (5,049,680)
  43. Santa Ana (Orange County), CA (3,121,251)
  44. New Orleans, LA (2,534,949)
  45. Cincinnati, OH (3,472,024)
  46. Pittsburgh, PA (4,470,907)
  47. Riverside, CA (2,088,322)
  48. Toledo, OH (2,019,458)
  49. St. Paul, MN (2,573,057)
  50. Buffalo, NY (2,782,734)
  51. Greensboro, NC (2,678,241)
  52. Madison, WI (2,070,908)
  53. Orlando, FL (3,625,795)
  54. Birmingham, AL (2,896,134)
  55. Baton Rouge, LA (2,841,516)
  56. Arlington, VA (2,615,764)
  57. Akron, OH (2,307,186)
  58. Montgomery, AL (3,057,149)
  59. Richmond, VA (3,725,124)
  60. Shreveport, LA (2,146,547)
  61. Des Moines, IA (2,092,903)
  62. Augusta, GA (3,286,871)
  63. Grand Rapids, MI (2,311,561)
  64. Little Rock, AR (2,377,037)
  65. Knoxville, TN (3,215,185)
  66. Fort Lauderdale, FL (3,511,282)
  67. Salt Lake City, UT (4,773,812)
  68. San Bernardino, CA (3,454,754)
  69. Fayetteville, NC (2,047,029)
  70. Aurora, IL (3,986,086)
  71. Springfield, MA (3,183,813)
  72. Paterson, NJ (2,285,085)
  73. Syracuse, NY (2,641,398)
  74. Bridgeport, CT (3,876,777)
  75. Warren, MI (2,393,541)
  76. Elizabeth, NJ (4,235,727)
  77. Lansing, MI (2,543,980)
  78. Manchester, NH (3,289,238)
  79. Allentown, PA (2,559,796)

This second map shows regions with a minimum population of 5 million.  They are listed below in the same manner that they were previously.

  1. New York City, NY (15,100,008)
  2. Los Angeles, CA (15,562,860)
  3. Chicago, IL (12,213,121)
  4. Houston, TX (6,568,198)
  5. Phoenix, AZ (8,490,543)
  6. Philadelphia, PA (10,845,050)
  7. San Antonio, TX (5,398,906)
  8. Dallas, TX (5,916,711)
  9. San Jose, CA (9,241,701)
  10. Detroit, MI (8,757,618)
  11. Indianapolis, IN (7,153,419)
  12. Columbus, OH (7,066,082)
  13. Fort Worth, TX (6,422,682)
  14. Charlotte, NC (9,064,119)
  15. Memphis, TN (5,335,220)
  16. Boston, MA (9,275,561)
  17. Seattle, WA (8,755,217)
  18. Denver, CO (10,039,895)
  19. Nashville, TN (5,631,919)
  20. Milwaukee, WI (6,167,922)
  21. Washington, DC (11,269,595)
  22. Portland, OR (5,263,530)
  23. Atlanta, GA (11,943,974)
  24. Kansas City (9,015,985)
  25. Sacramento, CA (6,370,171)
  26. Miami, FL (6,297,028)
  27. Cleveland, OH (6,605,216)
  28. Raleigh, NC (6,911,460)
  29. Minneapolis, MN (8,704,527)
  30. St. Louis, MO (5,438,438)
  31. Tampa, FL (11,235,143)
  32. New Orleans, LA (6,317,469)
  33. Pittsburgh, PA (5,274,967)
  34. Riverside, CA (9,002,191)
  35. Springfield, MA (5,943,610)
  36. Paterson, NJ (6,595,744)

This final map is for regions with a minimum population of 8 million, based on the following cities.

  1. New York City, NY (24,002,264)
  2. Los Angeles, CA (15,562,860)
  3. Chicago, IL (17,521,680)
  4. Houston, TX (14,995,203)
  5. Phoenix, AZ (8,493,518)
  6. Philadelphia, PA (11,376,896)
  7. Dallas, TX (12,594,912)
  8. San Jose, CA (15,669,851)
  9. Detroit, MI (10,047,016)
  10. Indianapolis, IN (14,442,659)
  11. Charlotte, NC (14,787,271)
  12. Memphis, TN (9,796,539)
  13. Boston, MA (12,318,503)
  14. Seattle, WA (13,493,324)
  15. Denver, CO (10,100,944)
  16. Washington, DC (13,875,208)
  17. Atlanta, GA (15,249,097)
  18. Kansas City, MO (10,585,310)
  19. Cleveland, OH (14,161,269)
  20. Minneapolis, MN (9,192,555)
  21. Tampa, FL (17,532,171)
  22. Riverside, CA (9,044,828)

Discussion

There are a number of inferences that can be made from these findings.  The first that I would like to discuss is that, despite using county data, there are still a lot of straight line boundaries.  This is going to be the case as long as counties have boundaries as arbitrary as states.  A more thorough and accurate analysis would include a municipality-by-municipality, rather than county-by-county, analysis, but that would take more time than I am willing to put into this project at this juncture.  The arbitrary straight lines on the map can lead to some unusual results.  For instance, Grand Junction, CO, the county seat of Mesa County, is closer to Salt Lake City and to Denver on the first map, while most of the rest of the counties on the border follow the state line, leaving Mesa County jutting awkwardly into Denver’s region.

Another odd effect is what happens when water transportation is a factor.  Google’s walking directions take regular ferry service into account, so areas such as San Francisco Bay, Puget Sound, Lake Michigan and Massachusetts Bay have many more connections than areas such as Chesapeake Bay.  While in all reality the residents of Northampton County, VA may be more willing to ride a boat to Virginia Beach than to walk to Philadelphia, this isn’t taken into consideration here.  Rivers with infrequent bridges, or at least bridges lacking in pedestrian walkways, also pose a problem.  There are many counties in Arkansas, for instance, that are much closer to Memphis, TN than to Little Rock; however, the lack of bridges and regular ferry service across the Mississippi River made it so that the Google analysis gave many more areas to Little Rock.  Also, Google’s directions from Honolulu to the Mainland included “Kayak across the Pacific Ocean,” and no matter where you wanted the final destination to be, it went through Seattle, thus making Hawaii, as far as this discussion goes, a part of Seattle.

Another issue is the methodology used in selecting which cities would anchor areas.  After having attempted this analysis before with a top-down approach and being unsuccessful, I tried a bottom-up approach, starting with the smallest cities on my list and moving up.  This creates some situations that are somewhat awkward; for instance, Newark, NJ is much more of a population center than either Elizabeth of Paterson, NJ, yet it didn’t make the cut.  Tampa, FL, is another example; it is more likely that Jacksonville and Miami would split the state, rather than Tampa eliminating both of them.  I may in the future consider another top-down approach to see how the results differ.

There is also the fact that this search was limited to cities in the US.  If we were to do a more complete analysis, we would include neighboring countries and, time permitting, the whole world.  There are certainly cities in Alaska, for instance, that are much closer to Vancouver and even Victoria than they are to Seattle.  However, for the purposes of this study, it made sense to limit the scope to the United States.

The last problem with the model is the fact that I set minimum benchmarks.  This worked very well for the first map, which only has two regions exceeding Alexander’s limit of 10 million people, and those not by much.  However, when we get to a minimum of 8 million, nearly all of the regions exceed the limit.  It may be better to next time set a maximum number and split regions in two as they exceed that limit.

These weaknesses being established, there are a few recommendations that I would like to make after doing this research.  First of all, all counties should have one county seat.  There are a number of existing counties that have two seats, and even a few counties that have no seat.  Counties with more than one seat should settle on one and move on, while counties with no seat should either establish one or be dissolved.  Second, if counties are to exist, then all cities should lie within one.  I feel that there is a little bit of leeway in here for state capitals, such as Carson City, NV, which are just following the example of our nation’s capital, but most of the 39 independent cities in Virginia, for example, shouldn’t be independent.  Many of these cities are even the seats of the counties that they are not a part of!  Unless a city has the same boundaries as its county, like Miami and Boston, they should not function independently.  Counties should also be contiguous.  There are a few counties in Louisiana and Kentucky where changing river course or other events have cut certain parts of a county off from the rest of it.  These areas should become part of another, adjacent county.

Also, I will again refer to Christopher Alexander’s A Pattern Language: “Decentralize city governments in a way that gives local control to communities of 5,000 to 10,000 persons. As nearly as possible, use natural geographic and historical boundaries to mark these communities. Give each community the power to initiate, decide, and execute the affairs that concern it closely: land use, housing, maintenance, streets, parks, police, schooling, welfare, neighborhood services.”  While there is a lot in there, Alexander does seem to set 5,000 persons as a baseline for a functional community.  With that being understood, I propose that any counties under 5,000 in population be dissolved.  If this were done, the country would have 292 fewer administrative units to deal with.  The interesting thing is that most of these counties that would go away are not in the sparsely-populated regions of the Rocky Mountains, as I had supposed; they are in the Plains States, where counties were created arbitrarily after Jefferson’s survey and without any sort of requirements for a population to support them.  These counties have no reason to be there, and their citizens would be better off being a part of a real, larger community.

With these suggestions being made, there are still many things that we can learn from these maps.  I personally prefer the first map and think that it could be a good basis for establishing regional governments and possibly eliminating county governments, particularly in the East and in California, where the population is the most dense.  To properly follow the borders of these regions, state borders would also have to be amended.  In this process, States which don’t have significant population centers would be eliminated, including Alaska, Delaware, Hawaii, Idaho, Kansas, Maine, Mississippi, Montana, North Dakota, New Mexico, Nevada, Rhode Island, South Carolina, South Dakota, Vermont, West Virginia and Wyoming.

The second map, with the much fewer and larger regions, might not be as good for establishing regional governments, but may be more useful for realigning state boundaries to better reflect reality.  If this were the plan, then county governments would probably still be needed, but only if they conform to the requirements stated above.  The last map, with the fewest and very largest areas, might not function either as regions or states, but may be one example of how the country might be equitably divided if it were to break up into small countries.  It is interesting to compare this map to others of how the US could potentially break up, as seen here.

Finally, it should be remembered that mere numbers are not what links people to a city or a region.  Few people would ever say that San Jose is the heart of the Bay Area, despite it being considerably bigger than San Francisco.  The only way you would really be able to truly find a dividing line between New York and Boston would be to go door to door through Connecticut and ask people if they are Yankees or Redsox fans.  The only true way to establish a regional identity is through years of tradition and cultural association with an area.  In the words of Lewis Mumford from his epic The City in History, “Contrary to the convictions of census statisticians, it is art, culture, and political purpose, not numbers, that define a city.”

City Council approves cars on K Street


Ben Adler brings us this story on Sacramento‘s recent decision to reopen the K Street Pedestrian Mall to cars.  K Street, as with many similar projects across America, died when it was closed off to vehicle traffic, losing shoppers and gaining criminals.  This is actually a great decision and will hopefully reinvigorate the area.  Even though I don’t use a car myself, I and many other New Urbanists realize that cars still play a very important role in cities.  Most people still travel by car, and if you don’t allow cars at all then you don’t allow a large segment of your population.  Cars are good for retail because it allows more people to see street signs and to take part in commerce.  It also puts more eyes on the street, reducing crime.  Parked cars along streets add to pedestrian safety, creating somewhat of a wall between pedestrians and fast-moving cars.  The problem comes when people plan for cars only, allowing them to go at lethal speeds, providing too much parking, and diminishing the pedestrian scale of a place, making it boring and dangerous for people walking or biking.  Good communities need to plan both for cars and for people, and to give precedence to people, because as long as they are able, the cars will still come.

New numbers prove smart growth reduces CO2, cost-effectively


Kaid Benfield of the National Resource Defense Council‘s Switchboard brings us this amazing article on recent studies on the effectiveness of TOD.  He says that instead of just focusing on cap and trade, powerplants and fuel efficiency, we also need to take land use into consideration.  California’s new law, SB375, which encourages cities to cut down on CO2 emissions and sprawl, may be used as a model for national legislation.  He says the main reason for this is that the Center for Clean Air Policy has released a new report showing that TOD and other smart growth policies can significantly reduce greenhouse emissions and, at the same time, allow governments to save money.  It found that smart growth policies can reduce American’s need to drive by 10%.  By 2030, this would be the equivalent of removing 30 million cars.  It gives a few examples of how this can reduce costs.  From his article:

Sacramento projects savings of 7.2 MMT of CO2 by 2050, while saving $9 billion in infrastructure costs and $380 million in annual consumer fuel costs, yielding a net economic benefit of almost $200 per ton of CO2 saved.

Portland, Oregon‘s investments in bicycle infrastructure will reduce emissions by 0.7 MMT of CO2, with net economic benefits of more than $1,000 per ton CO2 saved.

At the state level, Georgia could save more than $400 billion over 30 years, while saving 18 MMT of CO2 with strategic investments in transit, freight and travel demand management (e.g., four day work weeks, telecommuting, carpooling).  In Atlanta, the Atlantic Station redevelopment project is reducing residents’ need to drive by more than 30 percent [note: much more, according to data that I have seen; the authors are being conservative], which would cut 0.6 MMT of CO2 over 50 years, and generate $30 million per year in much-needed local tax revenue.

This report just confirms what TOD advocates have been saying for years: transit-oriented development is more cost effective, more environmentally friendly, and generally more interesting to live in than suburbs.  I hope that this indeed does influence national policy and that we can turn this country in a better direction.

Bob Shallit: Developer proposes urban village on CSUS campus


This article by Bob Shallit outlines Sacramento State‘s plans to build an urban village on land that is now mostly devoted to parking (I think the image of the triumph of pedestrian development over automobile development is just so sweet).  I think this is a particularly great project because universities are something that new urbanism is not terribly adept at.  As far as the transect goes, a college campus would in most cases be considered a district and not a part of the more mixed use transect zones.  But a univeristy can be integrated into the larger urban fabric of a community to create good urbanism, as has been done at many of our country’s older, more urban and more prodigious universities.  BYU Students for New Urbanism actually helped with a proposition to build an urban village near BYU’s campus, but it was rejected by students who thought that apartments above stores lead to slums.  This is a strange mindset, considering that some of the best places in the world, like the Champs-Élysées and Madison Avenue, fit this description perfectly.  I hope that other universities take a look at what Sacramento State is doing and learn from it.