Category: Chicago

CDOT misses the lesson on open data transparency

Publishing the wrong measurement as a PDF isn’t transparency.

The Chicago Department of Transportation released the first progress report to its Chicago Forward Action Agenda in October, two and a half years after the plan – the first of its kind – was published. I’ve spent an inordinate amount of time reading it and putting off a review. Why? It’s been a difficult to compare the original and update documents. The update is extremely light on specifics and details for the many goals in the Action Agenda, which should have organizational (like record keeping and efficiency improvements) and public impacts (like figuring out which intersections have the most crashes). I’ll publish my in-depth review this week.

Aside from missing specifics and details, the update presents information differently and is missing status updates for the three to five “performance measures” in each chapter. It was difficult to understand CDOT’s reporter progress without holding the original and update side-by-side. I think listing the original action item, the progress symbol, and then a status update would have been an easier way to read the document.

The update measures some action items differently than originally called for, and the way pothole repair was presented, a problem for people bicycling and driving, caught my analytical eye.

CDOT states a pothole-filling performance measure of the percentage, which it desires to be increased, “patched or fixed within 72 hours of being reported” but the average, according to the website Chicago Potholes, which tracks the city’s open data, is 101 days*. The update doesn’t necessarily explain why, writing “the 72 hour goal for filling potholes is not always feasible due to asphalt plant schedules” and nothing related to the performance measure.

As originally written, the only way to note the performance would be to list the percentage of potholes filled within the goal time, at the beginning and in the update. This performance measure has a complementary action item – an online dashboard – which could have provided the answer, but didn’t.

CDOT published that dashboard this summer as a series of six PDF files that update daily and you can hardly call it useful.

Publishing PDF files in the day and age of open government data – popular with President Obama and Mayor Rahm Emanuel – is unacceptable. Even if they are accessible – meaning you can copy/paste the text – they are poor outlets for data given the nationally-renowned civic innovation changes that Emanuel has succeeded in establishing.

There’s another problem: the dashboard file for pothole tracking doesn’t track the time it takes to close a pothole request, nor the number of pothole requests that are patched within 72 hours. It simply tells the number completed yesterday, the year to date, and the number of unpatched requests. (I’ve posted the pothole-tracking file to Scribd because the dashboard [PDF] doesn’t work in Safari; I also notified city staff to this problem which they acknowledged over three weeks ago.)

The “Chicago Works For You” website reports a different metric, that of the number of requests made each day, distributed by ward.

I discussed the proposed dashboard with former commissioner Gabe Klein over two years ago. He said he wanted to create a dashboard of projects “we’re working on that’s updated once a week.” Given Klein’s high professional accessibility to myself, John Greenfield and other reporters, I’ll give him and CDOT a pass for not doing this. But Klein also said, “I’m really big on transparency and good communication. When I left [Washington,] D.C. our [Freedom of Information Act Requests] were dramatically lowered.”

I’ll consider the pothole performance measure and action item “in need of major progress.”

* For stats geeks, the median is 86 and standard deviation is ±84.

Why do speeding crashes in Chicago lead to worse injuries?

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

That wasn’t a joyride on Lake Shore Drive

Video starts at Ohio Street (you can see the W Hotel after the curve at Ontario Street); the camera holder and driver speak with expletives.

Craig Newman at the Sun Times is wrong about the person in this video, who was filmed riding a Divvy bike-share bike along the jersey barrier on northbound Lake Shore Drive. He blogged today:

All excellent questions. But let’s maybe simplify and throw a warning sticker on the bikes: NO RIDING ON EXPRESSWAYS

And yes, I am a consistent bike commuter who enjoys the benefits and routinely laments stupidity, four-wheeled, two-wheeled and on foot we all have to fight through daily. But come on. Lake Shore Drive?

This person didn’t want to be cycling there. There are several ways one could make the mistake of riding a bike on this roadway. And once you’re on, you’re on for good until the next exit (which in this Divvy rider’s case is 1/4 mile north from where the video was shot).

She might have known there was something called the Lake Shore Path (as some people call it) or the Lakefront Trail – she couldn’t remember which. She didn’t see any “Route X” signs, or “Interstate Y” signs.

She saw a road that looks like so many others. It’s called a drive, not an expressway (it doesn’t meet those technical standards). She most likely entered from Lower Wacker (which connects to Michigan Avenue, where many people ride Divvy against Alderman Reilly’s desire) and went up the center, northbound ramp to Lake Shore Drive.

Stony Island Avenue in Chicago. The only difference between this and Lake Shore Drive is the more frequent stopping (unless there’s congestion on LSD) and the shopping. Photo by Jeff Zoline.

It can be easily mistaken for a typical road, looking similar to the stroads near wherever she lives. Like Stony Island, Cicero, Columbus, Archer, in Chicago, or any countless “major street” in the suburbs. Maybe she comes from Roscoe Village, where Western Avenue goes over Belmont, or Bridgeport/Brighton Park, where Ashland Avenue goes over Pershing Avenue. Or some other city where regular roads cross other regular roads at different grades.


View Larger Map

Local photographer Brent Knepper tweeted that he made the mistake before.

We have a problem with our design such that the highway didn’t sufficient communicate, “No really, you shouldn’t bike here”. On the contrary, we have roads that should be shouting, “Hey, you really should be biking here!”

Maybe that’s why Netherlands makes it perfectly clear with red pavement.

Believe me, not even Casey Neistat would ride up here intentionally.

Updated with a better guess of where she entered Lake Shore Drive.

Stop locking your bike at the Clybourn Metra station overnight

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.