Crash statistics differences from 2011 to 2012
I posted this as a series of tweets on Friday night.
Writing about cities
Crash statistics differences from 2011 to 2012
I posted this as a series of tweets on Friday night.
A really smart person could come up with a way to measure day-to-day bike friendliness based on how well cities adhere to standards that keep roads clear of obstructions that further frustrate the commute, like construction projects that squeeze bikes and cars together.
I work at home. There are some days when I only leave my house to get milk from the Mexican grocery store at the end of my block (which makes awesome burritos). That means I ride my bike half as much as people who commute to work. on their bikes. Today I had a bunch of errands to run: drop off stuff, buy stuff, take pictures of stuff for my blog, Grid Chicago.
It was a very frustrating experience. I don’t need to go into details about how I was harassed by people who the state so graciously awarded a license to drive. But it happened. And it happens a hundred times a day to people cycle commuting in Chicago. I got to thinking about “bike friendly” cities. Is there a way to incorporate driver attitudes in there? I tweeted:
Later I had the idea to use some very simple but objective measurements to create a new bike friendliness metric. It would help ensure that “Silver” (a ranking the League of American Bicyclists [LAB] uses) in one city means the same as “Silver”. It can expand from here but basically it works like this:
Stop me if this has already been done.
I created a spreadsheet that lists the top 10 populous cities in the United States. I then added 10 more cities: Austin, Boston, Davis, Madison, Minneapolis, Portland, San Francisco, Seattle, and Washington, D.C. In the next column I listed their bike commute share from the American Community Survey 2006-2010 5-year estimates. I calculated the standard deviation and mean of these shares and then in another column used Apple Numbers’s STANDARDIZE function:
The STANDARDIZE function returns a normalized value from a distribution characterized by a given mean and standard deviation.
I think that’s what I want. And the output is close to what I expected. I then found the LAB ranking for each city and found the variance of each ranking to see how far apart each city within one ranking was from another city in the same ranking. The results were interesting: the higher the ranking, the more variance there was.
Hurricane Sandy prompted a lot of New Yorkers to bike. It made headlines, even. Photo by Doug Gordon.
I wanted to add another metric of bike friendliness, and that’s density. To me, a higher density of people would mean a higher density of places to go (shop, eat, learn, enjoy) and friends and family would be closer, too. Or the possibility of meeting new people nearby would be higher. Yeah, I’m making a lot of assumptions here. So I applied the STANDARDIZE function there as well. I added this number to the previous STANDARDIZE result and that became the city’s score.
So, in this new, weird ranking system, the most bicycle friendly cities are…drum roll please…
Remember, I said above that any author of a list should spend at least a day cycling in each city. I’ve starred the cities where I’ve done that – I’ve cycled in 5 cities for at least a day.
I only calculated 20 cities. Ideally I’d calculate it for the top 50 most populous cities AND for every city that’s been ranked by LAB.
LAB cities list (PDF). My spreadsheet (XLS).
For an article I’m writing for Architect’s Newspaper about the Chicago Forward CDOT Action Agenda, I wanted to know about traffic injuries and fatalities in the United States, but compared to the Netherlands and Denmark and other places with a Vision Zero campaign (to have 0 traffic deaths each year).
I already knew the OECD had a good statistics database and web application. With a few clicks, I can quickly get a table of traffic injuries (casualties) listing just the countries I want. I can easily select the years I want, too.
In one more click the web application will show a time animated bar chart. A feature I’d like to see added is dividing the figure (in this case traffic injuries) by the population. Check out the video to see what it looks like. The United States looks to be in terrible shape, but our country has several times more residents.
I had trouble downloading and opening the CSV file of the data table I created. The XLS file was damaged, also. The built-in Mac OS X Archive Utility app couldn’t open the .gz file, but I used The Unarchiver app successfully.
My calculations, based on data from OECD (national population and traffic fatalities), Illinois Department of Transportation (IDOT), and the American Community Survey:
Fatalities per 100,000 in 2009
Chicago’s fatality rate per 100,000 citizens in 2009 was 16.75 (473 deaths on the roads). The fatality rate dropped in 2010: just 11.65 deaths per 100,000 residents (315 deaths on the roads; the population also decreased).
Updated September 28, 2012, to add the United Kingdom.
UPDATE: I added data from years 2005-2007 to complement existing 2008-2009 data in Table 1 as well as a visual representation. I have also added data from the 3-year estimates to Table 2.
UPDATE 01/20/11: Added the most recent 3-year estimate that the Census Bureau released in January 2011 to Table 2.
In September 2009, I wrote about “what the Census tells us about bicycle commuting” and a couple of days ago I compared Chicago to Minneapolis and St. Paul.
I want to update readers on the changes between the 1-year estimate data reported in that article (from 2008) and the most recent 1-year estimate data (from 2009). Percentages represent workers in the City of Chicago aged 16 and older riding bicycles to work.
Table 1 – Bicycling to work, 16 and older, 1-year estimates
|2005||0.7%||+/-0.1||0.9% of 621,537||+/-0.2||0.4% of 541,013||+/-0.1|
|2006||0.9%||+/-0.2||1.2% of 645,903||+/-0.3||0.7% of 563,219||+/-0.2|
|2007||1.1%||+/-0.2||1.4% of 656,288||+/-0.3||0.7% of 574,645||+/-0.2|
|2008||1.0%||+/-0.2||1.5% of 657,101||+/-0.3||0.5% of 603,640||+/-0.2|
|2009||1.1%||+/-0.2||1.8% of 651,394||+/-0.3||0.4% of 620,350||+/-0.1|
View graph of Table 1.Â MOE = margin of error, in percentage points.
We should be concerned about the possibleÂ decrease in the percentage of women riding bicycles to work, especially as the population size increased. The margin of error also decreased, thus suggesting an improvement in the accuracy of the data.Â There have already been many discussions (mine, others) as to why it is important to encourage women to ride bicycles and also what the woman cycling rate tells us about our cities and policies. If the decrease continues we must discover the causes.
But Table 1 doesn’t tell the full story.
As Matt points out in the comments below, the number of surveys returned for 1-year estimates is smaller than that from the Decennial Census. Therefore, I took a look at the two 3-year estimates available, each having a larger sample size than the 1-year estimates (see Table 2). The data below seem to show the opposite change than seen in Table 1: that the number of women bicycling to work has increased. The crux of our quandary is sample size. The sample size is the number of peopleÂ who are asked, “How did this person usually get to work LAST WEEK?”
Table 2 – Bicycling to work, 16 and older, 3-year estimates
|Click header for data source||2005-2007||2006-2008||2007-2009|
|Total workers||1,203,063||1,230,809 (+2.31%)||1,291,709 (+4.71%)|
|Males bicycling to work||7,549||9,014 (+19.41%)||11,014 (+18.16%)|
|Females bicycling to work||3,474||3,741 (+7.69%)||3,542 (-5.62%)|
The number of discrete females who bike to work has decreased in the most recent survey (2007-2009) while the total number of workers 16 and older has increased, giving females bicycling to work a smaller share than the previous survey (2006-2008). We must be careful to also note the margin of error for females bicycling to work is Â±499.
Matt suggested that sustainable transportation advocates “push for higher sampling” to reduce “data noise” and increase the accuracy of how this data represents actual conditions. I agree – I’d also like more data on all trips, and not just those made to go to work. Household travel surveys attempt to reveal more information about a region’s transportation.
One of the two overall goals of the Bike 2015 Plan is “to increase bicycle use, so that 5 percent of all trips less than five miles are by bicycle.” Unfortunately, the Plan doesn’t provide baseline data for this metric, but we can make some inferences (there will probably be no data for this in 2015, either). The CMAP Household Travel Survey summary from 2008 says that the mean trip distance (for all trips) for Cook County households is 4.38 miles (under five miles). The same survey says that for all trips, 1.3% were taken by bike. These can be our metrics. *See below for men/women breakdown. Note that no data for “all trips” exists for the City of Chicago.
We will not achieve the Bike 2015 Plan goal unless we do something about the conditions that promote and increase bicycling. Achieving the goals in the Bike 2015 Plan is not one group or agency’s responsibility. The Plan should be seen as a manifestation of what can and should be done for bicycling in Chicago and we all have a duty to promote its objectives.
Please leave a comment below for why you think the rate of women who bike to work has stayed flat and decreased, or what you think we can do to change this. Does it have to do with the urban environment, or are the reasons closer to home?
*The same survey also said: Cook County males used the bike for 1.9% of all trips. Cook County females used the bike for 0.8% of all trips.
Table 1 data comes from the 1-year estimates from the American Community survey, table S0801, Commuting Characteristics by Sex for the City of Chicago (permalink), which is a summary table of data in table B08006.Â Table 2 data directly from American Community Survey tableÂ B08006.
Being a “webmaster” (should we retire that word?) is a lot of fun. There are so many tools that make being a webmaster easy and enjoyable.
The best tool ever – statistics! Also known as analytics. With statistics, you get to learn what words and phrases people searched for and found your website.
Day after day, the same keywords show up in my statistics:
David Bryne, Luann Hamilton, Randy Neufeld, and Jacky Grimshaw (in the back) were the panelists at “Cities, Bicycles and the Future of Getting Around: A Special Urban Sustainability Forum with David Byrne.”