Network connectivity can be a difficult concept to describe, understand, and -- crucially -- to measure. Traditionally, cities attempting to quantify the usefulness of their bike networks have fallen back on easily-measured attributes like the mileage of bike lanes, or an as-the-crow flies distance to the nearest bike facility.
Though there may be some correlation between "bike friendliness" and these crude measures, they fail to capture the importance of having an interconnected network of comfortable bike routes, in addition to the role that destinations play in connecting people to places. The BNA aims to capture the importance of the interconnectedness of bicycle routes by measuring access to destinations.
There are three steps to the BNA (not counting the process of assembling the necessary input data as described in the import instructions).
- Level of Traffic Stress
- Block-to-block connectivity analysis
- Aggregation of destinations
Level of Traffic Stress (LTS) is a concept first described by Peter Furth, Maaza Mekuria, and Hilary Nixon in a paper published by the Mineta Transportation Institute at San Jose State University. In the paper, they set forth roadway conditions that contribute to cyclist stress. These are things you'd probably expect: high traffic speeds are uncomfortable for bicycling, quiet residential streets are less stressful than busy arterial roadways,etc. The original paper is published here.
One important aspect discussed in the paper is the critical role intersection crossings play in route continuity. It's easy to forget the importance of intersections when looking at a map of bicycle routes because the intersections themselves take virtually no space. Contrary to the visuals, intersections actually have an outsize importance in terms of routes a typical cyclist might feel comfortable following. If you've ever biked down a bucolic residential street and found you had to turn back at the virtual wall of high-speed traffic zooming down the arterial at the end of the block, you understand this concept well.
Another significant topic discussed in the paper is the principle of the "weakest link". This is an assumption that, in the minds of most cyclists, the highest stress level encountered on a route defines the stress level of the entire route. In the words of the authors, "If people will not use links whose stress exceeds their tolerance, several low-stress links cannot compensate for one high-stress link." The implication of this is significant for network planning as the prevailing attitude has been to provide comfortable routes where feasible but accept some compromises in constrained locations (such as busy intersections). This is usually done in the hope that users will stomach a short, uncomfortable segment as long as the rest of the route is pleasant.
The BNA adopts the general concepts of LTS for rating the comfort of possible route options. The lookup tables used as a basis for the BNA are published on Peter Furth's academic website here. There are some deviations from Dr Furth's tables, including for features not covered in his work, such as HAWK signals. We've also made some adjustments at the margins to better fit conditions as we've observed them doing bike network planning around North America.
The main magic of using LTS in the BNA is that it elevates the importance of intersections and insists on a continuous, comfortable route. Fortunately, the BNA is flexible enough to allow you to make your own calls on some of these issues. Has your city already completed an LTS analysis? You can plug it directly into the BNA, essentially skipping this step entirely. Would you prefer to use a more nuanced scheme with five different stress levels? The BNA can accommodate that too.
The core of the BNA measures low-stress connections between "blocks". Under the default settings, a block is a US Census block, which is roughly analogous to a city block. There's no magic to the composition of a block, but coarser areal units such as census block groups are less well-suited for use in the BNA since their large area can paper over connectivity problems occuring within their boundaries.
The process for measuring connectivity can be summarized as follows:
- Associate roads with blocks (i.e. decide which roads belong to which blocks)
- Iterate over each block and identify the shortest route to all other blocks regardless of the stress level
- Iterate over each block and identify the shortest route to all other blocks on only low-stress portions of the network
The results of #2 and #3 are then compared. If a connection is found in #3 but it requires a significant detour compared to the baseline condition in #2 that connection is ignored.
The end result is a matrix of block-to-block connections that identifies which blocks are connected to which other blocks via a low-stress route.
With the block-to-block connectivity established, the BNA then looks at destinations. It starts by measuring the universe of possible destinations around each block regardless of stress, and comparing that to the subset of destinations accessible via a low-stress route. This process can be summarized:
- Associate destinations with blocks
- Iterate over each block and count the number of destinations accessible regardless of stress level
- Iterate over each block and count the number of destinations accessible via a low-stress route
This yields two numbers -- a count of low-stress destinations and a count of all destinations -- that can be compared for each category of destination. As the number of low-stress destinations approaches the total number of destinations, a block's score approaches 100/100. As the number approaches zero, the block's score approaches zero too. The score is ultimately dependent on the total number of destinations around each blocks. Blocks with very few destinations nearby are judged based on how well they connect to those destinations on low-stress routes. For example, a block which is connected to two schools will score higher if there are only two possible schools to connect to (full points) than if there are five possible schools to connect to.
This can be moderated by defining a set thresholds at which points are awarded. In the school example above, one could decide that access to two schools is sufficient to warrant a 100/100 score regardless of how many additional schools are nearby. This is done through an explicit configuration of the school destinations in the configuration file.
Destination categories are also completely flexible. You may decide that the destinations you want to measure access to include coffee shops and movie theaters. As long as data can be supplied for a destination type, it can be incorporated into BNA results.
The last step is combining the scores for all destination categories into a single BNA score. This is accomplished by weights assigned to each destination category. A higher weight means that category's score has a larger impact on the overall score. One caveat to this can result in surprising scores: if no destinations of a given category are identified within reasonable distance of a given block, that category's score is not factored into the overall score. For example, if a block does not have any universities nearby, the university score for that block will be empty and the block's overall score will omit that category from consideration in the overall score. This often results in blocks in outlying areas receiving very high scores because they are surrounded by few destinations, all of which happen to be accessible via a low-stress route. There are ways to soften the effects of this with some post-processing of the results (e.g. manually reducing scores for blocks who don't meet a certain threshold for total number of destinations). As of yet these operations haven't been codified into the pyBNA code base.