By KYLE ROWE
Although we may be one of the top cities for bicycling in the nation, planning for bicycles is no simple task in Seattle; political barriers, physical limitations, and competing modes make squeezing bicycle facilities onto arterial streets seem like we are tearing down a bridge. One of the barriers that bicycle planners face is the neighborhood business community, who has repeatedly made their voice heard that they oppose bicycle facilities on their retail streets, such as NE 65th Street.
A lack of data and understanding about the impacts that bicycle facilities have on retail streets has allowed this political barrier and general misunderstanding to persist much longer than necessary. During the public comment period for the Bicycle Master Plan, numerous businesses wrote to the city in opposition to the new facilities planned on their streets. Rightfully so, these businesses are concerned about customers’ access to their storefront. With little data to show, planners are at a loss when trying to show that the proposed changes to the right-of-way will not hurt businesses.
I have attempted to bridge that gap in knowledge by utilizing taxable retail sales data (provided by the Washington State Department of Revenue) to study what occurred in neighborhood business districts when bicycle facilities were added.
In late 2010, the Seattle Department of Transportation completed a road diet on Greenwood Ave N, which included installing bicycle lanes from N 85th St. to N 105th St. The business district in Greenwood is centered around the intersection of Greenwood and 85th, and extends both north and south a few blocks. Taxable retail sales data was gathered from the Greenwood business district, starting in the fourth quarter of 2009 and extending to the fourth quarter of 2012 – the most recent dataset available. To account for variables beyond the street improvements, two comparison datasets were gathered in the same timeframe to act as controls. The first neighborhood comparison was the business district centered at 15th Ave NW and NW 85th St and the second comparison dataset was all businesses in NW Seattle.
The results of this analysis are in the graph below, with the bicycle lane signifying the construction of the road diet. Greenwood performed very similarly to the neighborhood-wide control, while differing slightly from the neighborhood comparison.
The second bicycle project studied was the climbing lane installed on NE 65th St, from NE Ravenna Blvd to 1st Ave NE. Although this project only installed a climbing lane for the hilly portion and shared lane markings elsewhere, the real impact in question is the twelve parking spots removed adjacent to the business district at NE 65th and Latona Ave NE. Again, two separate datasets were gathered to provide constants. The neighborhood comparison was the business district referred to as Tangletown – at Keystone Pl. N and N 56th St and the neighborhood-wide data included all businesses in NW Seattle.
The results of this analysis are in the graph above, again with the bicycle lane signifying the construction of the project and the removal of the parking. Leading up to the construction and just afterwards NE 65th St performed very similar to both controls, however two quarters after the project was finished NE 65th St experienced a 350% increase in sales index, followed by another jump to 400% sales index the following quarter.
Looking at the data, one conclusion can clearly be made, these bicycle projects did not have a negative impact on the business districts in both case studies. This conclusion can be made because in both case studies the business district at the project site performed similarly or better than the controls. You may be thinking, “why can’t we conclude that NE 65th St benefited from the bicycle facility?” This is where retail sales data presents a barrier in analyzing street improvements.
Even though the business district at 65th & Latona experienced a 400% increase in sales index after the project was finished, we cannot assume that this economic success was solely because of bicyclists. One could argue that the economic success likely wasn’t the product of motorists since their access was theoretically reduced, but without mode-split data before and after the project no conclusions can be made to assume which mode was most responsible for the economic change.
The methodology limitation outline above could be overcome by utilizing intercept surveys in conjunction with retail sales data analysis. Utilizing a survey-based methodology, like the recent East Village Shoppers Study in New York and the Polk Street Study in San Francisco, gives researchers a better understanding of how people are accessing the business district, but relies on highly subjective economic data.
Future studies should utilize both methods in conjunction to gain an understanding of how mode-splits change during the same timeframe that taxable retail sales data is collected. Doing this will allow for more accurate conclusions to be made and facilitate understanding and better communication between the bicycle planning and business communities.
The full report can be viewed here.