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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.

Something new in Salt Lake City transit

This is the fourth year in a row I’ve visited my mom in Salt Lake City and there’s a new transit line to gawk at. Three years ago it was the FrontRunner North commuter line between SLC and Ogden. Two years ago it was two new light rail lines (with new Siemens S70 vehicles). Last year was FrontRunner South to Provo (where my brother lives), and this year it’s the S-Line streetcar line.

On Wednesday, on my way back to SLC from Provo, I took a bus from my brother’s office to the Provo FrontRunner station, then the FrontRunner train to Murray station, where I switched to TRAX to ride up to Central Pointe station where the streetcar line terminates. A test vehicle was stopped at the single-track platform.

I wanted to see the route, the stops it makes, the station design, and the adjacent biking and walking path so I started walking up and down and across the blocks to check it out. I ran into two train several times while UTA staff tested them and made the video above.

Android versus iOS: my Chicago Bike Laws experience

Chicago Bike Laws: dooring info

Screenshot of Chicago Bike Laws, highlighting the dooring law.

For all this talk that more people use Android, and Android has the biggest and still-growing chunk of the smartphone market (in the United States and the world), I’m not seeing that reflected in how many downloads there are of my two apps.

Chicago Bike Laws is a free app that has been available for Android (download) since November 4 and on iOS (download) since November 9, a 5-day difference. Yet the iOS version has had more installations (58) since then while people have downloaded the Android version 32 times (but 5 people removed it).

What gives? The app is exactly the same for both platforms.

Are Chicago bicyclists more likely to have iPhones? I don’t do any platform-specific promotion so you can count that out.

(The experience I’ve had with Chicago Bike Guide activity is different because the Android version came online over a year later and has always been a paid app – Android apps cannot switch between free and paid while iOS apps can. By the way, the Chicago Bike Guide is free for iOS right now and half-off for Android. The comparison is that the adoption rate is much slower for the Android version.)

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

You can have your free parking when I get my free cappuccino

Kudos to this Chicago developer and their architect for blending the parking garage into the building. I still dislike that it’s visibly a parking garage. 

My friend Payton Chung has some very dry urban planner humor. Which I absolutely love. He wrote about parking minimums in Washington, D.C., and the current proposed zoning change that would reduce them (and included a reference to Chicago’s parking “podiums”). The best part is below:

Drivers’ inability to find free parking spaces outside their offices is no more deserving of a public policy response than my inability to find a free cappuccino waiting outside my office.

Free parking makes the world go round, doesn’t it.