Category: Bicycling

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:

 

 

Smartphones replace cars. Cars become smartphones.

January 6, 2014 / / 10 Comments

Teens’ smartphone use means they don’t want to drive. Car makers’ solution? Turn cars into smartphones.

The Los Angeles Times reported in March 2013, along with many other outlets, that “fewer 16-year-olds are rushing to get their driver’s licenses today than 30 years ago as smartphones and computers keep adolescents connected to one another.”

Smartphones maintain friendships more than any car can. According to Microsoft researcher Danah Boyd, who’s been interviewing hundreds of teenagers, “Teens aren’t addicted to social media. They’re addicted to each other.” (Plus not every teen needs a car if their friends have one. Where’s Uber for friends? That, or transit or safe cycle infrastructure, would help solve the “I need a ride to work at the mall” issue.)

Driving is on the decline as more people choose to take transit, bike, walk, or work from home (and not unemployment).

intel cars with bicycle parts

Marketing images from Intel’s blog post about cars becoming smartphones.

What’s a car maker to do?

The first thing a car maker does to fight this (losing) battle is to turn the car into a smartphone. It’s definitely in Intel’s interest, and that’s why they’re promoting the story, but Chevrolet will soon be integrating National Public Radio – better known as NPR – as an in-dash app. It will use the car’s location to find the nearest NPR affiliate. Yeah, my smartphone already does that.

The second thing they do is to market the product differently. Cars? They’re not stuck in traffic*, they’re an accessory to your bicycle. Two of the images used in Intel’s blog post feature bicycles in some way. The first shows a bicycle helmet sitting on a car dashboard. The second shows how everyone who works at a proposed Land Rover dealership is apparently going to bike there, given all the bikes parked at an adjacent shelter.

The new place to put your smartphone when you take the train.

* I’m looking at you, Nissan marketing staff. Your commercial for the Rogue that shows the mini SUV driving atop a train full of commuters in order to bypass road congestion (and got a lot of flack) is more ridiculous than Cadillac’s commercial showing a car blowing the doors of other cars, while their drivers look on in disbelief, in order to advertise the 400+ horsepower it has (completely impractical for driving in the urban area the commercial showcases).

Chicago Crash Browser, miraculously, has 2012 bicycle and pedestrian crash data

January 4, 2014 / / 0 Comments

Screenshot shows that you can choose your own search radius. When researching, be sure to copy the permalink so you can revisit your results. 

I’ve upgraded the Chicago Crash Browser, my web application that gives you some basic crash and injury statitics for bicyclist and pedestrian crashes anywhere in Chicago, to include 2012 data. It took the Illinois Department of Transportation eight months to compile the data and it took me four months to finally get around to uploading it into my database. While I spent that time, I made some improvements to the usability of the app and output more information. Since the last major changes I made (back in February 2013) I’ve gained two code contributors (Richard and Robert) making this my first communal project on GitHub.

I know that it’s been used as part of research in the 46th Ward participatory budgeting process for 2013, and by residents in the 26th Ward to show Alderman Maldonado the problem intersections in the Humboldt Park area. Transitized recently included pedestrian crash stats obtained from the Crash Browser in a blog post about pedestrianizing Michigan Avenue in Streeterville.

The first change I made was adding another zoom level, number 19, so you can get closer to the data. I made some changes to count how many people were injured and total them. You can now choose your search distance in multiples of 50 feet between 50 and 200, inclusive. As is typical, I get sidetracked when I notice errors on the map. Thankfully I just fire up JOSM and correct them so the next person that looks at the map sees the correction. Future changes I want to make include upgrading to the latest jQuery, LeafletJS, and Leaflet plugins. I’d also like to migrate to Bootstrap to improve styling and add responsive design so it works better on small screens.

Sign up for the newsletter where I’ll send a couple emails each year describing new changes (I’ve so far only published one newsletter).

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).