Tag: Chicago

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). 

Why do speeding crashes in Chicago lead to worse injuries?

November 21, 2013 / / 6 Comments

Don’t git behind me. Photo by Richard Masoner. 

A discussion about Chicagoans’ proclivity for tailgating (on a post about speed cameras) prompted me to look at the prevalence of this in causing crashes. I looked at the three-year period of 2010-2012 first, mainly so the numbers wouldn’t be so large, and left this information in a comment. But considering the prerequisites* for a crash to be reported in this dataset, and my desire to compare two multi-year periods, I switched my analysis to the single four-year period 2009-2012.

2009-2012

Total crashes: 318,193. Total fatalities: 554 people.

Tailgating crashes

62,080 crashes, 19.53% of all crash types

Tailgating crashes, injuries breakdown:

  • Killed: .0012 (this represents the number of deaths per crash). 75 people died in these crashes, representing 13.54% of all deaths.
  • Incapacitating injuries: 8.53% (the average distribution of people’s injuries in all tailgating crashes)
  • Non-Incapacitating: 46.32%
  • Possible injury: 45.15%

The share of all crash types that are tailgating has increased steadily from 18.11% in 2009 to 20.79% in 2012.

Speeding crashes

10,339 crashes, 3.24% of all crash types

Speeding injuries:

  • Killed: .0118 (this represents the number of deaths per crash). 122 people died in these crashes, representing 22.02% of all deaths.
  • Incapacitating injuries: 15.55% (the average distribution of people’s injuries in all speeding crashes)
  • Non-Incapacitating: 51.95%
  • Possible injury: 32.50%

The share of all crash types that are tailgating has decreased slightly from 3.72% in 2009 to 3.02% in 2012. While speeding leads to fewer crashes, it leads to a greater incidence of death and serious injury. The probability of a speeding crash leading to at least one death seems to stay steady through the period while the probability of seeing a person with an incapacitating injury versus a different kind of injury varies more, but not so much in a range that overlaps the rates for tailgating crashes.

A future comparison at injuries should look at the top crash causes for death and serious injury.

N/A and Unable to determine crashes

237,729 crashes, 74.71% of all crash types

N/A and unable to determine injuries:

  • Killed: .0013 (this represents the number of deaths per crash). 305 people died in these crashes, representing 55.05% of all deaths.
  • Incapacitating injuries: 9.38% (the average distribution of people’s injuries in all N/A crashes)
  • Non-Incapacitating: 48.26%
  • Possible injury: 42.35%

Notes

Updated December 4, 2013

I updated the wording on how to interpret these numbers. For example, previously for “killed” there was a percentage saying this number represented the amount of crashes that had at least one death. This wasn’t accurate: the same number represents a rate of deaths per crash of that type. Injury percentages represent the distribution of injury types experienced by all the people injured in crashes of that type.

Reliability

Analyzing crash causes is not very reliable as 45.60% of the reported crashes in 2012 had “N/A” or “unable to determine” listed as the primary cause! The third and fourth most frequently ascribed causes were the two tailgating codes (described below). There are some crashes that had the one of these two causes in the secondary cause field but I haven’t calculated that.

Cause code descriptions

Each crash has two cause codes. For tailgating crashes I searched for reports where “failing to reduce speed to avoid crash” or “following too closely” in either the primary or secondary cause field (it’s possible that a report had both of these causes ascribed). For speeding crashes I searched for “speed excessive for conditions” or “exceeding speed limit” in either the primary or secondary cause fields.

Prerequisites

This data excludes crashes where there was no injury or no property damage greater than $500 (2005 to 2008) and $1,500 (2009 to 2012). You cannot compare the two datasets when you want to see a share of all crashes because the number of “all crashes” will be underreported in the second dataset.

Queries

These are some of the MySQL queries I used to get the data out of my own crash database (I’m figuring out ways to make it public, using a shared login). “Cause 1 code” indicates the primary cause of the crash according to the police officer’s judgement. “Cause 2 code” indicates the secondary cause of the crash according to the police officer’s judgement.

1. Crash cause reliability: SELECt count(casenumber), sum(`Total killed`), `Cause2`, `Cause 2 code` FROM `CrashExtract_Chicago` WHERE year = 12 GROUP BY `Cause 2 code`  ORDER BY cast(`Cause 2 code` as signed)

2. Speeding crashes: SELECT count(casenumber), sum(`Total killed`), sum(`totalInjuries`), sum(`A injuries`), sum(`B injuries`), sum(`C injuries`) FROM `CrashExtract_Chicago` WHERE (`Cause 1 code` = 1 OR `Cause 1 code` = 27 OR `Cause 2 code` = 1 or `Cause 2 code` = 27) AND year > 8

3. Tailgating crashes: SELECT count(casenumber), sum(`Total killed`), sum(`totalInjuries`), sum(`A injuries`), sum(`B injuries`), sum(`C injuries`) FROM `CrashExtract_Chicago` WHERE (`Cause 1 code` = 3 OR `Cause 1 code` = 28 OR `Cause 2 code` = 3 or `Cause 2 code` = 28) AND year > 8

4. N/A and Unable to determine crashes: SELECT count(casenumber), sum(`Total killed`), sum(`totalInjuries`), sum(`A injuries`), sum(`B injuries`), sum(`C injuries`) FROM `CrashExtract_Chicago` WHERE (`Cause 1 code` = 18 OR `Cause 1 code` = 99) AND year > 8

Stop locking your bike at the Clybourn Metra station overnight

November 11, 2013 / / 6 Comments

Existing bike parking at the Clybourn Metra station

This is a resolution.

WHEREAS, I love GIS.

WHEREAS, I was reading this blog post on the Azavea company blog about bike theft prediction and trends in Philadelphia.

WHEREAS, I analyzed bike theft location in Chicago in 2012 and the Clybourn Metra station emerged as the most frequent Metra theft location.

WHEREAS, I searched the Chicago Stolen Bike Registry for “clybourn” and several thefts have been reported to the registry in 2013.

WHEREAS, I believe the Chicago Police Department still doesn’t allow searching of their database for bike thefts thus leaving the CSBR as the premier source of data.

WHEREAS, I am watching this show called The Bletchley Circle wherein a group of four fictional women who cracked codes in World War II are solving a murder mystery in 1950s London.

BE IT RESOLVED that you should not leave your bicycle parked at the Clybourn Metra station overnight as it is a terrible place to leave a bicycle parked. Why? No one is around most of the time to socially secure your bicycle.

New bike parking at the Clybourn Metra station

This is a great place to get your bike stolen. In the dark. Overnight. With no one around to see it happen. 

What Complete Streets means to DOTs: the case of widening Harrison Street

September 5, 2013 / / 28 Comments

What Harrison Street looks like in 2013, replete with additional lanes and no “bicycle ways”. 

The Chicago and Illinois Departments of Transportation completed a project in 2012 to rebuild the Congress Parkway bridge over the Chicago River and build a new interchange with Lower Wacker Drive. It also rebuilt the intersections of Harrison/Wacker and Harrison/Wells.

Harrison prior to the project had two striped travel lanes (four effective travel lanes) but now has six travel lanes (including two new turn lanes). Bicycle accommodations were not made and people who want to walk across the street at Wacker and Wells must now encounter a variety of pedestrian unfriendly elements:  they must use actuated signals (waiting for a long time), cross long distances or two roadways to reach the other side, avoid drivers in the right-turn channelized lane, and wait in expressway interchange-style islands. Additionally, Wells Street was widened and all corner radii were enlarged to speed automobile traffic and presumably to better accommodate large trucks.

That is how IDOT interprets its “complete streets” law (which took effect on July 1, 2007) and how CDOT interprets its “complete streets” policy (decreed by Mayor Daley in 2006). The full text of the Illinois law, known as Public Act 095-0665, is below:

AN ACT concerning roads.

Be it enacted by the People of the State of Illinois,
represented in the General Assembly:

Section 5. The Illinois Highway Code is amended by adding
Section 4-220 as follows:

(605 ILCS 5/4-220 new)
Sec. 4-220. Bicycle and pedestrian ways.
(a) Bicycle and pedestrian ways shall be given full
consideration in the planning and development of
transportation facilities, including the incorporation of such
ways into State plans and programs.
(b) In or within one mile of an urban area, bicycle and
pedestrian ways shall be established in conjunction with the
construction, reconstruction, or other change of any State
transportation facility except:
(1) in pavement resurfacing projects that do not widen
the existing traveled way or do not provide stabilized
shoulders; or
(2) where approved by the Secretary of Transportation
based upon documented safety issues, excessive cost or
absence of need.
(c) Bicycle and pedestrian ways may be included in pavement
resurfacing projects when local support is evident or bicycling
and walking accommodations can be added within the overall
scope of the original roadwork.
(d) The Department shall establish design and construction
standards for bicycle and pedestrian ways. Beginning July 1,
2007, this Section shall apply to planning and training
purposes only. Beginning July 1, 2008, this Section shall apply
to construction projects.

Section 99. Effective date. This Act takes effect July 1,
2007.

Here is the case: a “bicycle way” should have been incorporated into the Harrison/Congress/Wells modification.

Here is the evidence:

  1. The project location is a transportation facility in the State
  2. The project location is in or within one mile of an urban area.
  3. The project widened an existing traveled way, from 52 feet (two marked travel lanes, four effective travel lanes) to approximately 64 feet (six marked travel lanes).
  4. Local support for bicycle and pedestrian ways is evident; see the “Streets for Cycling Plan 2020” planning process and the addition of a concrete deck (to reduce bicycling slippage) on the sides of the Harrison Street bridge over the Chicago River approaching the project location.
  5. The project was constructed after July 1, 2008.

The missing piece of evidence, though, is whether or not the Secretary of Transportation, based upon documented safety issues, excessive cost or absence of need, made an exception for this project.

The Chicago “complete streets” policy is less specific than the Illinois “complete streets” law, printed below:

The safety and convenience of all users of the transportation system including pedestrians, bicyclists, transit users, freight, and motor vehicle drivers shall be accommodated and balanced in all types of transportation and development projects and through all phases of a project so that even the most vulnerable – children, elderly, and persons with disabilities – can travel safely within the public right of way.

One of the examples CDOT gives on how this policy can be implemented is “Reclaim street space for other uses through the use of ‘road diets’ e.g., convert 4-lane roadway to 3-lane roadway with marked bike lanes” – they accomplished the opposite on Harrison Street.

In a 2010 traffic count, 16,800 cars were counted here, an amount handled by roads with fewer lanes and less than the amount in CDOT’s guidelines for implementing road diets and narrowing a road from 4 lanes to 2, yet in 2012, the agencies increased capacity.

Before: An aerial view from November 7, 2007. Image from Google Earth’s historical imagery feature. These two images represent the same zoom and area so you can compare the land changes from before to after the infrastructure modification. 

After: An aerial view from April 4, 2013. Image from Google Earth. Notice the additional lanes, roadway width, land taken south of Harrison Street, and the widened intersection at Wells with increased curb radius. 

TV shows can’t fool me with their inaccurate train portrayals

January 14, 2013 / / 14 Comments

I have an idea. I have a TV show that takes place in New York City. I need to film a scene on the subway. So I use the closest subway… Los Angeles Metro.

Oh, and I’ll place “NYC Subway” signs on the walls (replete with graffiti).

No one will see the red stripes all over the place indicating this is the Red Line.

When you live in those cities, or you’re just enough of a railfan to see the difference, it becomes annoying and makes you despise the TV show you like.

On this particular show, they show footage actually taken in New York City to show the subway entrance. Some stock footage I guess.

That show was “Don’t Trust the B**** in Apt. 23“. The other filmed product that got it all wrong was “The Bourne Legacy”. It partially takes place in some bastardization of Chicago. In this movie, which stars Jeremy Renner instead of Matt Damon, the director depicted the Chicago ‘L’ while showing footage of a New York City elevated train. How could one tell? Nowhere in Chicago are there two parallel tracks, with one above the other. Nor are the elevated tracks that high above the street, nor do they use curved elevated columns. This happens about 50 minutes in. Immediately before this fake scene is shown, you’ll see aerial footage of the real Chicago ‘L’. This lasts for 4 seconds.

Real Chicago ‘L’.

Stand-in Chicago ‘L’. 

I don’t want to call this “disingenuous” (but I think it is) and TV show producers aren’t required to film exactly where they portray; these “stand ins” are probably for budgetary reasons. I don’t think it harms a city’s brand or image. I just get annoyed: the show becomes less believable. Maybe I know too much about cities.

Film crews get tax breaks in lots of cities and states in the United States and Canada. If I were the city’s film office manager, or the city’s lobbyist or brand manager, I’d want it to be portrayed accurately.