Category: Data

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

The 2012 Chicago bike crash data is in

November 17, 2013 / / 4 Comments

Crash statistics differences from 2011 to 2012

I posted this as a series of tweets on Friday night.

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. 

How many cars are in Rogers Park?

November 10, 2013 / / 4 Comments

There are a gazillion cars in Rogers Park, and there’s no place to park them. That’s the declaration you would gather if you listen to “Lakefront Car Tower” (a parking garage) proponents, including the 49th ward alderman, Joe Moore.

The parking problem is so bad in Rogers Park that a parking garage at Sherwin Avenue and Sheridan Road that would provide less than 100 overnight parking spaces to the public was actually sent from Asphaltia, the god of car parks. It’s so bad that “[m]any car owners find themselves stuck in their home at night” – yes, the alderman really published that on his website – because they find a parking space on Friday night and can’t move the car until Monday morning. The horror of using your feet, pedals, the bus, the train, car sharing, paratransit, or a Segway!

(I’d love to get into parking pricing policy now, but I’ll just leave you with this: of course there is going to be a demand problem when the supply of publicly-owned on-street parking costs $0 per year.)

This post is actually a tutorial on how to use United States Census data to find how many cars are in the neighborhood of Rogers Park, not a laugh about Asphaltia’s teachings.

Let’s begin! Continue reading

On Active Transportation Alliance’s transportation summit

November 3, 2013 / / 0 Comments

Active Transportation Alliance invited Eric Hanns and I to speak about “using data for advocacy” at their first annual transportation summit held after a member meeting two Saturdays ago. My and Eric’s talks were complementary and centered around the data tool I built and which Eric and the other volunteers in the 46th Ward participatory budgeting program used to prioritize and market infrastructure projects in Uptown.

The tool in question is the Chicago Crash Browser I made last year and improved this year to load data faster, with great help from the Smart Chicago Collaborative and several members of the OpenGov Hack Night group I cherish.

Click or tap a spot in Chicago to retrieve the number of bicyclist-car and pedestrian-car crashes within 150 feet. With this information, the PB volunteers could show the alderman how important it was for him to support bike and pedestrian infrastructure projects in the ward, and to persuade ward voters to fund these projects.

Find more information about the four other summit “breakout groups” on Active Trans’s website. Eric and I prepared a “Using Data for Advocacy: Making the Case with Compelling Facts” handout which you can download as a PDF or see on our Google Doc. I’ve conveniently listed the links from the handout below but if you want more pointed advice on where to look for specific data, or get an answer to questions you have but don’t grok the context of each of these tools, leave me a comment.