Process snapshot of a community data project on blocked bike lanes in New York City — a collaboration between Melissa De la Cruz, Researcher at Bits and Atoms and Crystal Penalosa, Community Engineer at Stae
Context: What’s the Data Dilemma?
As streets are increasingly congested with passenger vehicles, freight trucks, bikes, and new shared vehicles, whose safety and efficiency takes priority? Should parcels get priority over pedestrians and cyclists? NYC’s open data portal has limited data on blocked bike lanes, as the issue is an ephemeral one — it’s a real-time status that changes faster than a typical 311 issue gets assigned and addressed. On the other hand, Twitter has a community and culture of people posting thousands of photos and locations of blocked bike lanes across the country. Our experiment is to unite disparate datasets (official city data and community-generated data, like Tweets) to gain a fresh perspective on this persistent problem of contested street space.
What is the common data we have for how our streets are used?
- 450K daily bike trips (more than doubled 2005–2015)
- 40% growth in daily Citi Bike use since 2015
- 100+ miles of protected bike lanes (2007–2017)
- NYC is the 2nd most traffic-jammed city in the world (91-hour avg. in traffic annually)
- 26 cyclists killed and 4,104 injured by motorists in 2017
- 1000+ blocked bike lane Tweets in NYC/SF/DC since July 2018
- 12 Vision Zero Priority Bicycle Districts
- 51% of purchases are online nationally (48% in 2014)
- 5% increase in USPS package delivery in 2017
What do we want to find out?
- What blocked-bike-lane hot spots (patterns in geography, temporality, and street typology) can we identify?
- How might pulling in alternative data sources (Tweets, traffic cameras, freight vehicle routes) help round out the picture for the City and community/safety advocates to more effectively manage our streets and design better policies?
- What percentage of the time are loading zones occupied with freight delivery vehicles vs. left vacant or underutilized?
- How might a better understanding of freight loading patterns better inform curb regulation policies and planning?
Here are a few initial insights:
- Street infrastructure is not equipped to handle the increase of reliance on the bike lane for non-bike commuting purposes
- Enforcement is limited, NYPD response time is about 2 hours per 311 request on blocked bike lanes. (Only .01% of requests have resulted in summons)
- Freight complaints tend to cluster in the Lower East Side. One additional layer to look at is the dependence of that neighborhood for freight and how businesses play a role in that
And here are our first data visualizations:
You can explore the data directly here: nyc-ny.municipal.systems
- Add curb asset data to the map using Coord’s Surveyor tool for streets in the Lower East Side most frequently blocked
- Experiment with gathering data via street cameras using a machine-vision algorithm (inspired by Alex Bell)
- Research how driver training, policies, or organizational culture play a role in which freight companies are more or less likely to block bike lanes
- How does the programming of autonomous vehicles factor into use of bike lanes?