Tag: Divvy data

One day left to enter the Divvy Data Challenge

March 10, 2014 / / 0 Comments
Divvy dock post-polar vortex

Divvy bikes have been covered in snow frequently this winter. Photo by Jennifer Davis.

As self-proclaimed Divvy Data Brigade Captain* in Chicago’s #opendata and #opengov community I must tell you that all Divvy Data Challenge submissions are due tomorrow, Tuesday, March 11. Divvy posted:

Help us illustrate the answers to questions such as: Where are riders going? When are they going there? How far do they ride? What are top stations? What interesting usage patterns emerge? What can the data reveal about how Chicago gets around on Divvy?

We’re interested in infographics, maps, images, animations, or websites that can help answer questions and reveal patterns in Divvy usage. We’re looking for entries to tell us something new about these trips and show us what they look like.

I’ve seen a handful of the entries so far, including some to which I’ve contributed, and I’m impressed. When the deadline passes I’ll feature my favorites.

Want to play with the data? You should start with these resources, in order:

  1. Divvy Data Challenge – rules and data download
  2. divvy-munging – download an enhanced version of Divvy’s data, with input from several #ChiHackNight hackers
  3. Bike Sharing Data Hackpad – this is where I’m consolidating all of the links to projects, visualizations, analysis, data, and blog posts.
  4. Divvy Data Google Group – a discussion group with over 25 members
  5. #DivvyData – chat on Twitter

It’s not too late to get started now on a project about the bikes themselves. Nick Bennet has crunched the numbers on the bikes’ activity and posted them to the Divvy Data Google Group. Want to use his data and initial analysis? He said “run with it”.

Share your work ahead of time and leave a comment with a link to your project.

* This title is a play on Christopher Whitaker’s position as Code For America Brigade Captain and all around awesome-doer of keeping track of everything that’s going on in these communities and publishing event write-ups on Smart Chicago Collaborative.

Divvy activity in Wicker Park-Bucktown

February 13, 2014 / / 18 Comments
Divvy Bikes Outside Smoke Daddy

The Divvy bike-share station outside Smoke Daddy on Division Street at Wood Street is the fourth most popular in the Wicker Park & Bucktown neighborhoods. Photo by Daniel Rangel.

This is an analysis of the station use for Divvy bike-share stations in the Wicker Park and Bucktown neighborhoods (they blend together and it’s hard to know if the club or bar you’re going to is one neighborhood or the other).

Numbers represent a discrete trip, from one station to another (or the same station if the trip was greater than 3 minutes, to eliminate “hiccups” where the bike left the dock but didn’t actually go anywhere). Customer means someone who used a 24-hour pass and subscribers are annual members. Gender is self-reported on a member’s DivvyBikes.com user profile.

17 stations listed.

[table id=10 /]

This map of Wicker Park Divvy stations shows a residential service gap among the Damen/Cortland, Ashland/Armitage ( Metra) and North/Wood stations.

This map of Wicker Park Divvy stations shows a residential service gap among the Damen/Cortland, Ashland/Armitage (
Metra) and North/Wood stations.

Based on the popularity of the Ashland/Armitage station, which is right outside the Clybourn Metra station – a very popular train stop – I think there might be a residential service gap near Saint Mary of the Angels School. I recommend a Divvy station at Walsh Park this year because the Bloomingdale Trail will open and terminate there.

Notes

Not all of these stations were online when Divvy launched on June 28, 2013, but I haven’t yet looked into the history to see when each went online. Therefore direct comparisons are not appropriate until you have a trips per day number. Then, seasonality (very cold weather) has its own effect. At the very least, all stations were online by October 29th, with the final addition of the Lincoln Ave & Fullerton Ave (at Halsted) station.

Can someone use “R” to make a time series chart on the entire trips dataset so we can find the best cutoff time to eliminate “hiccups”?

Query used: SELECT count(`trip_id`), usertype, gender FROM `divvy_trips_distances` WHERE (start_station = ‘Claremont Ave & Hirsch St’ or end_station = ‘Claremont Ave & Hirsch St’) AND seconds > 180 GROUP BY `usertype`, gender

Where do Divvy riders go?

February 13, 2014 / / 14 Comments
Divvys

Divvy bikes fit people of almost all sizes. Photo by Mike Travis (mikeybrick).

Divvy released the 2013 trip data on Tuesday for their data challenge, and presented alongside me the data, basic system operations info, and existing visualizations and apps, at a Divvy data-focused Open Gov Hack Night I put together at the weekly meeting. Thank you Chris Whitaker at Smart Chicago Collaborative for writing the meeting recap.

I “ran the numbers” on some selected slices of the data to post on Twitter and they range from the useless to useful! I’m using the hashtag #DivvyData.

  • Average trip distance of members in 2013 is estimated to be slightly shorter than casuals: 1.81 miles versus 1.56 miles – tweet
  • Bike 321 has traveled the furthest: 989 miles. Beat the next bike by 0.2 miles – tweet
  • Women members on average took longer trips (but fewer trips overall) on @DivvyBikes than men in 2013. – tweet
  • The average trip distance of 759,788 trips (by members and casuals) in 2013 is an estimated 1.68 miles. – tweet
  • In 2013, 79.05% of member trips were by men and 20.95% by women. – tweet
  • On average in 2013, 24-hour pass holders (whom I call casuals) made trips 2.5x longer (time wise) than members. – tweet
  • Damen/Pierce Divvy station (outside the Damen Blue Line station) is most popular in Wicker Park-Bucktown – data

And other stats, presented as embedded tweets:

 

 

Developing a method to score Divvy station connectivity

January 4, 2014 / / 2 Comments

A Divvy station at Halsted/Roscoe in Boystown, covered in snow after the system was shutdown for the first time to protect workers and members. Photo by Adam Herstein.

In researching for a new Streetsblog Chicago article I’m writing about Divvy, Chicago’s bike-share system, I wanted to know which stations (really, neighborhoods) had the best connectivity. They are nodes in a network and the bike-share network’s quality is based on how well (a measure of time) and how many ways one can move from node to node.

I read Institute for Transportation Development Policy’s (ITDP) report “The Bike-Share Planning Guide” [PDF] says that one station every 300 meters (984 feet) “should be the basis to ensure mostly uniform coverage”. They also say there should be 10 to 16 stations per square kilometer of the coverage area, which has a more qualitative definition. It’s really up to the system designer, but the report says “the coverage area must be large enough to contain a significant set of users’ origins and destinations”. If you make it too small it won’t meaningfully connect places and “the system will have a lower chance of success because its convenience will be compromised”. (I was inspired to research this after reading coverage of the report in Next City by Nancy Scola.)

Since I don’t yet know the coverage area – I lack the city’s planning guide and geodata – I’ll use two datasets to see if Chicago meets the 300 meters/984 feet standard.

Dataset 1

The first dataset I created was a distance matrix in QGIS that measured the straight-line distance between each station and its eight nearest stations. This means I would cover a station in all directions, N, S, E, W, and NW, NE, SE, and SW. Download first dataset, distance matrix.

Each dataset offers multiple ways to gauge connectivity. The first dataset, using a straight-line distance method, gives me mean, standard deviation, maximum value, and minimum value. I sorted the dataset by mean. A station with the lowest mean has the greatest number of nearby stations; in other words, most of its nearby stations are closer to it than the next station in the list.

Sorting the first dataset by lowest mean gives these top five best-connected stations:

  1. Canal St & Monroe St, a block north of Union Station (191), mean of 903.96 feet among nearest 8 stations
  2. Clinton St & Madison St, outside Presidential Towers and across from Northwestern Train Station (77), 964.19 feet
  3. Canal St & Madison St, outside Northwestern Train Station (174), 972.40
  4. Canal St & Adams St, north side of Union Station’s Great Hall (192), 982.02
  5. State St & Randolph St, outside Walgreens and across from Block 37 (44), 1,04.19

The least-connected stations are:

  1. Prairie Ave & Garfield Blvd (204), where the nearest station is 4,521 feet away (straight-line distance), or 8.8x greater than the best-connected station, and the mean of the nearest 8 stations is 6,366.82 feet (straight-line distance)
  2. California Ave & 21st St (348), 6,255.32
  3. Kedzie Ave & Milwaukee Ave (260), 5,575.30
  4. Ellis Ave & 58th St (328), 5,198.72
  5. Shore Drive & 55th St (247), 5,168.26

Dataset 2

The second dataset I manipulated is based on Alex Soble’s DivvyBrags Chrome extension that uses a distance matrix created by Nick Bennett (here’s the file) that estimates the bicycle route distance between each station and every other station. This means 88,341 rows! Download second dataset, distance by bike – I loaded it into MySQL to use its maths function, but you could probably use python or R.

The two datasets had some overlap (in bold), but only when finding the stations with the lowest connectivity. In the second dataset, using the estimated bicycle route distance, ranking by the number of stations within 2.5 miles, or the distance one can bike in 30 minutes (the fee-free period) at 12 MPH average, the following are the top five best-connected stations:

  1. Ogden Ave & Chicago Ave, 133 stations within 2.5 miles
  2. Green St & Milwaukee Ave, 131
  3. Desplaines St & Kinzie St, 129
  4. (tied) Larrabee St & Kingsbury St and Carpenter St & Huron St, 128
  5. (tied) Clinton St & Lake St and Green St & Randolph St, 125

Notice that none of these stations overlap with the best-connected stations and none are downtown. And the least-connected stations (these stations have the fewest nearby stations) are:

  1. Shore Drive & 55th St, 11 stations within 2.5 miles
  2. (tied) Ellis Ave & 58th St and Lake Park Ave & 56th St, 12
  3. (tied) Kimbark Ave & 53rd St and Blackstone Ave & Hyde Park Blvd and Woodlawn Ave & 55th St, 13
  4. Prairie Ave & Garfield Blvd, 14
  5. Cottage Grove Ave & 51st St, 15

This, the second dataset, gives you a lot more options on devising a complex or weighted scoring system. For example, you could weight certain factors slightly higher than the number of stations accessible within 2.5 miles. Or you could multiply or divide some factors to obtain a different score.

I tried another method on the second dataset – ranking by average instead of nearby station quantity – and came up with a completely different “highest connectivity” list. Stations that appeared in the least-connected stations list showed up as having the lowest average distance from that station to every other station that was 2.5 miles or closer. Here’s that list:

  1. Kimbark Ave & 53rd St – 13 stations within 2.5 miles, 1,961.46 meters average distance to those 13 stations
    Blackstone Ave & Hyde Park Blvd – 13 stations, 2,009.31 meters average
    Woodlawn Ave & 55th St – 13 stations, 2,027.54 meters average
  2. Cottage Grove Ave & 51st St – 15 stations, 2,087.73 meters average
  3. State St & Kinzie St – 101 stations, 2,181.64 meters average
  4. Clark St & Randolph St – 111 stations, 2,195.10 meters average
  5. State St & Wacker Dr – 97 stations, 2,207.10 meters average

Back to 300 meters

The original question was to see if there’s a Divvy station every 300 meters (or 500 meters in outlying areas and areas of lower demand). Nope. Only 34 of 300 stations, 11.3%, have a nearby station no more than 300 meters away. 183 stations have a nearby station no further than 500 meters – 61.0%. (You can duplicate these findings by looking at the second dataset.)

Concluding thoughts

ITDP’s bike-share planning guide says that “residential population density is often used as a proxy to identify those places where there will be greater demand”. Job density and the cluster of amenities should also be used, but for the purposes of my analysis, residential density is an easy datum to grab.

It appears that stations in Woodlawn, Washington Park, and Hyde Park west of the Metra Electric line fare the worst in station connectivity. The 60637 ZIP code (representing those neighborhoods) contains half of the least-connected stations and has a residential density of 10,468.9 people per square mile while 60642, containing 3 of the 7 best-connected stations, has a residential density of 11,025.3 people per square mile. There’s a small difference in density but an enormous difference in station connectivity.

However, I haven’t looked at the number of stations per square mile (again, I don’t know the originally planned coverage area), nor the rise or drop in residential density in adjacent ZIP codes.

There are myriad other factors to consider, as well, including – according to ITDP’s report – current bike mode share, transit and bikeway networks, and major attractions. It recommends using these to create a “demand profile”.

Station density is important for user convenience, “to ensure users can bike and park anywhere” in the coverage area, and to increase market penetration (the number of people who will use the bike-share system). When Divvy and the Chicago Department of Transportation add 175 stations this year – some for infill and others to expand the coverage area – they should explore the areas around and between the stations that were ranked with the lowest connectivity to decrease the average distance to its nearby stations and to increase the number of stations within 2.5 miles (the 12 MPH average, 30-minute riding distance).

N.B. I was going to make a map, but I didn’t feel like spending more time combining the datasets (I needed to get the geographic data from one dataset to the other in order to create a symbolized map). 

Divvy memberships growing at very slow rate

July 24, 2013 / / 36 Comments

Chart showing the progression of annual member sign-ups. 

The day after Divvy – a bike sharing system in Chicago operated by Alta Bicycle Share – started signing up members, enrollment dropped by 80.2%. The next day it dropped by 57.9% and then 55.7% after that. The progression was 732 in the first day, 145 in the second, 61 in the third, and 27 in the fourth day. Since day two, daily enrollment has never exceeded 121 sign-ups in a day.

The Bike to Work Day Rally on Friday, June 14, had almost no impact: there were 6 sign-ups that day, with 6 sign-ups the day before. This was the first time that a station was visible to the public and Divvy staff were out there talking to people and allowing some test rides. The next day, however, there were 12 sign-ups. Even launch day, June 28, was weak, especially given that the new system was given a lot of attention that day and weekend in the press. The Monday after Friday’s launch saw more than launch day.

In a system that has so far focused on a few stations in neighborhoods (like West Town/Wicker Park, South Loop and Lincoln Park), this might not be surprising. Nor is it surprising that memberships were low from the period enrollment opened to the first station being installed – because there was nothing out on the street to catch people’s attention and you had to know about it by being told, online or from a friend.

“What is this?”

I expected, then, that memberships would jump once the system went live, to at least a rate higher than the period when membership was open but there were 0 stations installed. But that hasn’t happened. If the rate of new annual members doesn’t start increasing as stations start increasing, I will be very concerned. Currently, most trips are taken by 24-hour pass holders, and the most popular stations are near the lakefront, telling me that the system is used mostly by (confused) tourists.

Riding a Divvy bike on Dearborn Street.

I could, of course, try to compare us to New York City’s rapid explosion in annual member sign-ups for Citibike, also run by Alta Bicycle Share. I’m sure readers would poke holes in that comparison, rendering any argumentation here useless. Now that there are 75 stations are in place, the rate of post-launch annual membership enrollment should be vastly higher than the rate of pre-launch annual membership enrollment. The period when there were 0 stations had a higher rate of enrollment than the period that followed it during which stations were being installed.

Recap

Pre-launch, days 1-16 of enrollment (with 0 stations)
1,152 memberships, 36.5%, average of 72 members per day

Pre-launch, days 17-30 of enrollment (with 1-68 stations)
320 memberships, 10.1%, average of 22.9 members per day

Subtotal: Pre-launch, 30 days of enrollment (with 0-68 stations)
1,472 memberships, 44.7%, average of 49.1 members per day

Post-launch, days 31-55 of enrollment (with 68-75 stations)
1,685 memberships, 53.4%, average of 67.4 members per day

Total: days 1-55 of enrollment (with 0-75 stations)
3,157 memberships, 100.0%, average of 57.4 members per day

Appendix

View the membership data for yourself (XLS).

P.I was told two weeks ago that marketing for Divvy would soon begin on Chicago Transit Authority bus shelters and “City Information” signs, both advertising infrastructure operated by JCDecaux under its contract with the City of Chicago. I think this will have minimal impact, but it’s definitely worth putting out there.