Tuesday, April 15, 2014
The Importance of Car-sharing
Addendum: check out this post on Atlantic Cities by Paul Supawanich, which deals with another side of the equation.
Lyft's expansion into Hampton Roads is creating consternation at the State House. In the past few years, there have been numerous car-sharing innovations, from Zipcar, Lyft, and Uber to websites that allow users to more easily share rides in the same direction. These services are crucial to the future of urbanism in the US.
But some urbanists have been lukewarm. Fundamentally, they say, shared cars are still cars. These critics are missing the bigger picture. Car-sharing services allow vastly more people to give up car ownership. One recent study suggested that Zipcar and other car-sharing services replace 32 private cars with one shared car and have resulted in as many as 500,000 fewer new car sales since their appearance. Each of those private cars was taking up space in the city and resulting in more traffic.
Shared cars take all expenses of car ownership and boil them down to a trip cost. This is a critical change. Car ownership forces high fixed expenditures that become sunk costs for the owners. They've already shelled out for the car, so they might as well drive it. The car reorients its owner's brain towards living and shopping in places that were conveniently laid out for cars, rather than for people.
Think of it this way. Here's all the trips that a hypothetical family living in a relatively walkable urban area might make in 3 months. We'll say... central Winchester. The height of the bar represents the marginal value of having a car for each trip and the trips are organized from most to least valuable.
Once the accumulated value of the trips on the left is enough to justify owning a car, suddenly housing and shopping decisions are made based on the car. Since you're already driving to work every day, you might as well live in a place where you can only get to work via car. And your company might as well build an office that you can only get to via car. And your city might as well design its infrastructure around the fact that none of those employees are driving. And transit options disappear without customers. And now, suddenly, the marginal value of owning the car is extremely high for every commute, every errand, and even every optional trip. Indeed, you risk being unemployed and living in social isolation without it.
The secret of the shared car's ability to reduce vehicle trips lies in the marginal (meaning additional) value of the trips that people are currently taking. Every time you think about going somewhere, you have decisions to make. Do you drive to work or bike today (this is called "mode choice")? Do you run up to the grocery to get an extra box of pancake mix or do you wait until 3 when you'll be at the dentist already (this is called "trip-chaining")?
Some of those trips are extremely valuable - eg running your wife to the hospital when she goes into labor or (to use a less extreme example) being able to visit your cousins in a rural area - so much so that they force you to own a car. While there are lots of costs associated with driving, the biggest ones are fixed (or "capital") costs - the price of the car and its depreciation, the maintenance and upkeep of the vehicle, insurance and taxes, etc.
The costs people weigh when deciding what mode to use are variable costs - often just gasoline. Even these costs are usually underestimated. Maintenance from additional mileage doesn't factor in for most people. And buying gas is such a habit that few people actually sit down before each trip and calculate the cost of the trip.
All that to say, the real financial benefits of not driving only come when one can avoid even owning a car. And the really economically correct choices are made only when all auto-related costs are boiled down into a trip-by-trip cost that the driver can weigh against the value of the trip. Car sharing schemes allow people who are already close to being able to live without a car to finally give up car ownership.
Thursday, April 3, 2014
"Elected" Representatives
I've been exploring the 2013 House of Delegates election results a bit lately. Here's a funny result of the two-party system and our efficiently gerrymandered districts. In orange are the 59 districts whose representative's did not face either a primary challenger or a serious opponent in the general election. Of those 59, 42 received 90% or more of the vote in their district. Not many elected officials in developed countries can boast vote totals like that. The 5th District's Israel D. O'Quinn takes the cake with no primary and 98.7% of the general election vote, just 1.3% short of the percentage captured by Kim Jong Un in his most recent election.
Thursday, March 27, 2014
Revised Development Projections
I've been working on a revised version of the development projection images that I did a while back. I wrote a long post about it on Stat Chat that has the full images and some commentary on them. Here I wanted to explain a little more of the technical background on how I made them.
How Is This Constructed?
The model starts with a random value in a Poisson distribution that simulates the likelihood of development. This distribution captures the fact that some parcels will develop seemingly at random. As different circumstances change, more and more areas will develop until finally the vast majority of land in an urban area will develop. After that, some undeveloped plots will remain for a while and a few will simply never develop.
The values in this raster are then raised by adding a score based on the driving time to major employment centers. This is because most development is and has been automobile-driven. The shape of an urban area is highly predictable based on the driving time to an employment center. Lastly, I reduced the likelihood of development based on the slope of the terrain and subtracted all national and state parks, wetlands, military bases, conservation easements, and local parks that were in some way preserved. I then split this model up into planning district commissions because they roughly encompass metro areas that expand outward from a core.
The density that I used was the same density of development in the district that I was adjusting. So if the developed areas of a planning district had a density of 1000 persons per acre, that's the density I used for the predicted new residents also. These densities are likely to be off because of several factors. They could be too high because, while areas have been gaining population over the last 50 years, the existing population has also been decentralizing and new residents are likely to buy the lowest density homes on the periphery. On the other hand, they could be too low because, as areas grow in size, they become denser and each new person's marginal amount of developed area is a little smaller. Additionally, the trend in recent years has swung the other way, as I pointed out earlier in the post. For lack of a better model, I considered it a wash and stuck with the existing density.
Cities and driving distances:
Slope:
Conserved land:
Growth likelihood raster and planning district boundaries. Some counties are shared by planning districts - I had to assign these to one district and deduct their population numbers from the other.
I'd be very interested to hear of ways you think this could be improved or additional factors that could be added in. One I thought of was buffering around the Chesapeake Bay and other bodies of water to account for the attraction of living by the water. Another is doing a more comprehensive service area just around the roads to get more of the development aligned with roads rather than "speckled" about. The problem with that is that new developments come with new roads and you don't know where they're going to be.
Wednesday, March 19, 2014
MSA's and Commuting
New post on Statchat looking at the dependence of rural counties on nearby urban areas.
Percent of workers commuting to the Washington, DC area:
Percent of workers commuting to the Washington, DC area:
Sunday, March 16, 2014
Wednesday, March 12, 2014
In Which I Acquire a New Toy
Namely - Tom Gross's cartogram script for ArcGIS.
Virginia counties distorted to make area correspond to population:
Where you from?
Why do Hampton Roads and Northern VA seem so different from the rest of Virginia? At the macro level, it's pretty obvious - the influence of the Federal government in DC and the military in Hampton Roads. But it's even more obvious on this map, which shows the percentage of the population born in Virginia. Blue means born here, red means born elsewhere:
Percentages are high in Hampton Roads - especially around military bases. But almost the entire northern Virginia metro area is in the red. Fewer than 30% of NoVA residents in most areas were born in Virginia.
Here's another map showing the percentage of the population born outside the U.S. See the Weldon Cooper Center's post on immigration for more on this subject.
A few quick regressions on excel shows a pretty strong link between places that have more bachelor's degrees, more people born outside VA, and a higher income. It's not a particularly new revelation - the more education and earning power a person acquires, the more geographically mobile they become.
Percentages are high in Hampton Roads - especially around military bases. But almost the entire northern Virginia metro area is in the red. Fewer than 30% of NoVA residents in most areas were born in Virginia.
Here's another map showing the percentage of the population born outside the U.S. See the Weldon Cooper Center's post on immigration for more on this subject.
A few quick regressions on excel shows a pretty strong link between places that have more bachelor's degrees, more people born outside VA, and a higher income. It's not a particularly new revelation - the more education and earning power a person acquires, the more geographically mobile they become.
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