Category: Chicago

How Chicagoans commute map: An interview with the cartographer

January 24, 2014 / / 0 Comments
Chicago Commute Map by Transitized

A screenshot of the map showing Lakeview and the Brown, Red, Purple and Purple Line Express stations.

Shaun Jacobsen blogs at Transitized.com and yesterday published the How Chicagoans Commute map. I emailed him to get some more insight on why he made it, how, and what insights it tells about Chicago and transit. The map color-symbolizes census tracts based on the simple majority commuting transportation mode.

What got you started on it?

It was your post about the Census data and breaking it down by ZIP code to show people how many homes have cars. I’ve used that method a few times. The method of looking up each case each time it came up took too long, so this kind of puts it in one place.

What story did you want to tell?

I wanted to demonstrate that many households in the city don’t have any cars at all, and these residents need to be planned for as well. What I really liked was how the north side transit lines stuck out. Those clearly have an impact on how people commute, but I wonder what the cause is. Are the Red and Brown Lines really good lines (in people’s opinions) so they take them, or are people deciding to live closer to the lines because they want to use it (because they work downtown, for example)?

The reason I decided to post the map on Thursday was because while I was writing the story about a proposed development in Uptown and I wanted  information on how many people had cars around that development. As the map shows, almost all of Uptown is transit-commuting, and a lot of us don’t even own any cars.

What data and tools did you use?

I first used the Chicago Data Portal to grab the census tract boundaries. Then I grabbed all of the census data for B08141 (“means of transportation to work by number of vehicles available”) and DP04 (“selected housing characteristics”) for each tract and combined it using the tract ID and Excel’s VLOOKUP formula.

Read the rest of this interview on Web Map Academy.

Wayfinding signs at Van Buren Street Metra station are incomplete

January 13, 2014 /
New RTA interagency transfer signage near Van Buren Street Metra Electric station

“B” marks a new bus boarding area near the Van Buren Street Metra Electric station.

The Regional Transportation Authority has spent $2 million to improve wayfinding between CTA, Metra, and Pace train stations and bus stops in a needed effort to connect newbies and long-time residents to their next transfer.

Some of the signs need to show better information, though. The RTA installed signs at the Van Buren Street Metra Electric station at Michigan Avenue that create “bus loading groups,” similar to bus bays at suburban park & rides.

It works like this: you come across the nearest bus stop – I happened upon boarding area B – hoping to find the route you need. Instead, though, that route stops at boarding area A. The sign at boarding area B points you in the direction of A and from where you stand you can see a sign that identifies A.

RTA’s signs have two issues. First, they don’t tell you that boarding area C is across the street – unless you inspect the small map – and instead point you in the direction of A (from B). If you walk in the direction of the arrow from boarding area B you will not run into boarding area C or a sign that tells you where to cross the street in order to access C.

The first issue creates the second problem: by reading and relying upon the sign’s text you can’t know at which boarding area, A or C, you should board a bus route that stops at both boarding areas. (Those who also study the maps on another side of the sign will have better luck.) That’s because the same route operates in both directions and if you’re not familiar with the route, you won’t know which direction takes you towards your destination.

New RTA interagency transfer signage near Van Buren Street Metra Electric station

Both boarding areas A and C will get you on the 3, 4, J14, and 26, but only the map on the other side tells you which direction they go. Also, while the arrow points in the direction of boarding areas A and C, only the map tells you that A is across the street.

The fix seems an easy one. First, point the arrows on A and B across the street instead of north or south towards B or A, and add an intermediary sign along the walking path that communicates that “boarding area C is across the street.” Then, update the signs to indicate which direction the bus routes are going so that travelers are assured they need to visit C across the street for King Drive buses going towards Bronzeville or A for King Drive buses going toward Streeterville.

The RTA has installed other signage in this program at 95th and Western (CTA & Pace), Joliet Union Station (Metra & Pace), and Davis Station in Evanston (CTA, Metra, & Pace).

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). 

Divvy isn’t a real bike

January 2, 2014 / / 0 Comments

Riding on Divvy in the snow.

Divvy, for the first time in its short, seven-month existence, shut down today at 12 PM on account of the weather and keeping members and workers – who move bikes, shovel snow, and drive vans around town – safe.

Every news media in town reported on the shutdown. Chicagoist, ABC 7, NBC 5, FOX 32, Chicago Tribune, Sun-Times – you name it they had it.

But nothing has been published, except parts of this story from DNAinfo Chicago, that discusses how bicycling – whether on Divvy or your own bike – is very difficult in Chicago winters because of the poor coordination between the Streets & Sanitation and Transportation departments’ snow removal efforts, and the slow pace at which CDOT gets around to removing snow from the protected bike lanes. (I was quoted, alongside someone I recommended the author get in touch with, and we have differing views on the matter.)

In winter the protected bike lanes are the only bikeable kind of bike lane as conventional bike lanes become snow storage areas because plows can’t reach further right when there are parked cars (to avoid knocking off car mirrors).

This problem is not unique to Chicago and other cities have solved it. The cold isn’t why people in Chicago stop biking: it’s that snow and ice make it even more difficult in a region with little, separated (meaning safe and desirable) cycling infrastructure. There are climes with similar and worse winters where a large portion of people who bike in the summer keep biking in the winter. Places like Boulder, Minneapolis, Montréal, and Copenhagen.

A well-plowed, separated bike lane in a Copenhagen winter. Stranded? Put your bike on the back of a taxi (their buses don’t allow bikes).

I think it’s good that news media have recognized Divvy’s position as a transit system in the area, which they do by holding it to the same, weird standard they do Chicago Transit Authority and Metra, and posting about it frequently. When the CTA or Divvy has some marginal or perceived issue with its finances or service, an article gets written. But when it comes to bicycle infrastructure they give the city a pass where it doesn’t deserve one.

The media cares about Divvy, but it doesn’t care about bicycling. It might be the 11,000 Divvy members (more than Active Transportation Alliance or The Chainlink), however, that gets the city to kick up its bike lane snow removal efforts up a notch and I anxiously await that day.