With the Minnesota Bicycle Summit last week and the National Bike Summit this week, I have been noting a pernicious habit among cycling advocates and friends that I wish I could punch out of everyone: Quoting studies selectively or in ways that simply do not apply.
As cyclists and advocates argue for ongoing funding for alternative transit infrastructure in a difficult budget environment, data helps. With those who already support bicycles, data is a tool to offer them to help argue the position or defend their position to others. To doubters, data can be a means to shift opinion.
But using data badly does no one any favors. It’s easily assaulted by opponents. It makes cycling advocates look stupid.
Some recent examples:
- That damn cycletracks study. I have ranted about this seventy-eleven times at this point. There are major flaws in this study’s data methodology.
- Studies that say more people would ride if more facilities were built. Very often the data collection in these meets appropriate statistical standards. But these really do become a tyranny of the masses — what is popular is not always a good idea. National obesity trends are one example of how what is popular (being sedentary, high fat convenience foods) not necessarily being a good plan. It’s important not to let opinion polling override other forms of science.
- The Baltimore study that says that investing in bicycle infrastructure creates more jobs than highway projects. The data in this study is specific to one metro and one series of projects, and is thus difficult to credibly extend across all projects and metros. However, I am definitely seeing bicycle advocacy groups try to do so.
- I saw someone reference a study the other day from Bristol, England that says that “pedestrians, cycle and public transport users provide as much if not more spending power than car users in town centres.” It’s been re-tweeted a lot by people going to the National Bike Summit.
One issue: This is a UK study. The way UK cities and neighborhoods are built around “High Streets” is completely unlike how most of the United States is built. I’ve lived near a UK High Street, when I was attending school in London. Neighborhoods are built around a core intersection/broadway/circus in which most of the basic needs of life can be procured, and major transit transfers are possible.
There are some junctions within cities that act like high streets in the UK — an intersection like Cleveland and Ford Parkway in Saint Paul comes to mind, where you can get almost all the amenities of life within a short walk of the core intersection. But more often the setup is more like MN65 in Fridley/Blaine — a series of strip-malls along a high-speed state highway corridor. To invest on a High Street model would mean blowing up a lot of America to start from scratch.
I’m sure there are more out there that would just make me snarl to hear cited.
Not all studies based on small geography or populations are of no use. The health study in Madison and Milwaukee has broader applicability, because the controlled factors are such that you can credibly say: We don’t know what the total financial savings would be in THIS metro, but based on the savings in THOSE metros it’s pretty safe to bet it’d be a good chunk of change, eh.
I have seen a number of advocacy groups stick to citing well-controlled data studies and facts and figures that can easily be applied within a region without acrobatics. The Bicycle Alliance of Minnesota is one such organization.
As advocates, we don’t need to try to mutilate data to serve our needs. There are studies and data to support our goals that are credible as they stand, without trying to say “we could be more like Europe!” (which is not a good message with even some moderate Republicans, and is often not realistic based on existing build patterns). There are countless health and environmental benefits. There are social benefits. Infrastructure investment can reduce congestion and thus increase business productivity. Infrastructure can attract educated workforces to urban cores. Citing those factors, and data collected in the United States in broadly applicable studies, is going to have a greater impact on fiscal conservatives and the unconverted than using data dodgily.