Tag: data

FOIA is great…if you know who and what to ask for

Dooring is dangerous (sometimes deadly) for bicyclists. Where's the data? Image via The Blaze

Dooring is dangerous (sometimes deadly) for bicyclists. Where’s the data? Image via The Blaze

tl;dr: This is the list of all citation types that the Chicago Dept. of Administrative Hearings “administers”.

The Freedom of Information Act is my favorite law because it gives the public – and me – great access to work, information, and data that the public – including me – causes to have created for the purpose of running governments.

FOIA requires public agencies to publish (really, email you) stuff that they make and don’t publish on their own (which is dumb), and reply to you within five days.

All you have to do is ask for it!

BUT: Who do you ask?

AND: What do you ask them for?

This is the hardest thing about submitting a FOIA request.

Lately, my friend and I – more my friend than me – have been trying to obtain data on the number of traffic citations issued to motorists for opening their door into traffic – a.k.a. “dooring”.

It is dangerous everywhere, and in Chicago this is illegal. In Chicago it carries a steep fine. $500 if you don’t hurt a bicyclist, and $1,000 if you do.

My friend FOIA’d the Chicago Police Department. You know, the agency that actually writes the citations. They don’t have bulk records to provide.

Then he FOIA’d the Chicago Department of Transportation, the Illinois Department of Transportation, the Chicago Department of Administrative Hearings, and the Chicago Department of Finance.

Each of these five agencies tells you on their website how to submit a FOIA request. You can also use FOIA Machine to help you find a destination for your request.

None of them have the records either. The “FOIA officer” for the Administrative Hearings department suggested that he contact the Cook County Circuit Court. So that’s what we’re doing.

Oh, and since the Administrative Hearings department doesn’t have this information (even though they have the records of citations for a lot of other traffic violations), I figured I would ask for them for a list of citations that they do have records of.

And here’s the list, all 3,857 citation types. You’ll notice a lot of them don’t have a description, and some of very short and unclear descriptions. Hopefully you can help me fix that!

I can grant you editing access on the Google Doc and we can improve this list with some categorizations, like “building violations” and “vehicle code”.

 

Working with ZIP code data (and alternatives to using sketchy ZIP code data)

1711 North Kimball Avenue, built 1890

This building at 1711 N Kimball no longer receives mail and the local mail carrier would mark it as vacant. After a minimum length of time the address will appear in the United States Postal Service’s vacancy dataset, provided by the federal Department of Housing and Urban Development. Photo: Gabriel X. Michael.

Working with accurate ZIP code data in your geographic publication (website or report) or demographic analysis can be problematic. The most accurate dataset – perhaps the only one that could be called reliably accurate – is one that you purchase from one of the United States Postal Service’s (USPS) authorized resellers. If you want to skip the introduction on what ZIP codes really represent, jump to “ZIP-code related datasets”.

Understanding what ZIP codes are

In other words the post office’s ZIP code data, which they use to deliver mail and not to locate people like your publication or analysis, is not free. It is also, unbeknownst to many, a dataset that lists mail carrier routes. It’s not a boundary or polygon, although many of the authorized resellers transform it into a boundary so buyers can geocode the location of their customers (retail companies might use this for customer tracking and profiling, and petition-creating websites for determining your elected officials).

The Census Bureau has its own issues using ZIP code data. For one, the ZIP code data changes as routes change and as delivery points change. Census boundaries needs to stay somewhat constant to be able to compare geographies over time, and Census tracts stay the same for a period of 10 years (between the decennial surveys).

Understanding that ZIP codes are well known (everybody has one and everybody knows theirs) and that it would be useful to present data on that level, the Bureau created “ZIP Code Tabulation Areas” (ZCTA) for the 2000 Census. They’re a collection of Census tracts that resemble a ZIP code’s area (they also often share the same 5-digit identifiers). The ZCTA and an area representing a ZIP code have a lot of overlap and can share much of the same space. ZCTA data is freely downloadable from the Census Bureau’s TIGER shapefiles website.

There’s a good discussion about what ZIP codes are and aren’t on the GIS StackExchange.

Chicago example of the problem

Here’s a real world example of the kinds of problems that ZIP code data availability and comprehension: Those working on the Chicago Health Atlas have run into this problem where they were using two different datasets: ZCTA from the Census Bureau and ZIP codes as prepared by the City of Chicago and published on their open data portal. Their solution, which is really a stopgap measure and needs further review not just by those involved in the app but by a diverse group of data experts, was to add a disclaimer that they use ZCTAs instead of the USPS’s ZIP code data.

ZIP-code related datasets

Fast forward to why I’m telling you all of this: The U.S. Department of Housing and Urban Development (HUD) has two ZIP-code based datasets that may prove useful to mappers and researchers.

1. ZIP code crosswalk files

This is a collection of eight datasets that link a level of Census geography to ZIP codes (and the reverse). The most useful to me is ZIP to Census tract. This dataset tells you in which ZIP code a Census tract lies (including if it spans multiple ZIP codes). HUD is using data from the USPS to create this.

The dataset is documented well on their website and updated quarterly, going back to 2010. The most recent file comes as a 12 MB Excel spreadsheet.

2. Vacant addresses

The USPS employs thousands of mail carriers to delivery things to the millions of households across the country, and they keep track of when the mail carrier cannot delivery something because no one lives in the apartment or house anymore. The address vacancy data tells you the following characteristics at the Census tract level:

  • total number of addresses the USPS knows about
  • number of addresses on urban routes to which the mail carrier hasn’t been able to delivery for 90 days and longer
  • “no-stat” addresses: undeliverable rural addresses, places under construction, urban addresses unlikely to be active

You must register to download the vacant addresses data and be a governmental entity or non-profit organization*, per the agreement** HUD has with USPS. Learn more and download the vacancy data which they update quarterly.

Tina Fassett Smith is a researcher at DePaul University’s Institute of Housing Studies and reviewed part of this blog post. She stresses to readers to ignore the “no-stat” addresses in the USPS’s vacancy dataset. She said that research by her and her colleagues at the IHS concluded this section of the data is unreliable. Tina also said that the methodology mail carriers use to identify vacant addresses and places under change (construction or demolition) isn’t made public and that mail carriers have an incentive to collect the data instead of being compensated normally. Tina further explained the issues with no-stat.

We have seen instances of a relationship between the number of P.O. boxes (i.e., the presence of a post office) and the number of no-stats in an area. This is one reason we took it off of the IHS Data Portal. We have not found it to be a useful data set for better understanding neighborhoods or housing markets.

The Institute of Housing Studies provides vacancy data on their portal for those who don’t want to bother with the HUD sign-up process to obtain it.

* It appears that HUD doesn’t verify your eligibility.

** This agreement also states that one can only use the vacancy data for the “stated purpose”: “measuring and forecasting neighborhood changes, assessing neighborhood needs, and measuring/assessing the various HUD programs in which Users are involved”.

Who bikes?

Who bikes? pie chart

From April 2011, via Sightlines Daily, using data from John Pucher and Ralph Buehler, who got it from the 2009 National Household Travel Survey.

Contrary to popular convention, the biggest share of bicyclists isn’t yuppies, it’s low income people. In fact, the lowest-earning quarter of Americans make nearly one-third of all bike trips. Among that group, I would expect to find at least some fraction of working poor, students, the unemployed, and retired people of modest means. No doubt there are almost as many reasons to bike as there are cyclists, but it’s clear that bikes are a favored choice among those on a budget.

The big takeaway for me, however, is looking beyond low-income riders. Bicycling is remarkably evenly distributed among the remaining three quartiles. With the exception of the over- represented bottom quartile, bike trips don’t appear to be the province of any one income class more than any other.

Introduction to DIY bike ridership research

A lot of people ask me how many people are out there bicycling.

“Not a lot”, I tell them.

And I explain why: the primary source of data is the American Community Survey, which is a questionnaire that asks people questions about how they got to work in a specific week. (More details on how it does this below.) We don’t have data, except in rare “Household Travel Surveys”, about trips by bike to school, shopping, and social activities.

It’s comparable across the country – you can get this data for any city.

Here’s how:

  1. Visit the “legacy” American FactFinder and select American Community Survey, operated by the United States Census Bureau.
  2. Select 2005-2009 American Community Survey 5-Year Estimates (or the latest 5-year estimate). This is the most accurate data.
  3. In the right-side menu that appears, click on “Enter a table number”.
  4. In the new window, input the table number ” S0801″ (“Commuting Characteristics by Sex”) and submit the form. The new window will close and the other window will go to that table.
  5. Now it’s time to select your geography. In the left-side menu, under “Change…” click on “geography (state, county, place…)”
  6. In the window to change your geography, select “Place” as your “Geographic Type”.
  7. Then select the state.
  8. Then select your city and click “Show Result”.
Notes:
  • This data shows all modes people take to work, who live in that city. It’s highly probable that people are leaving the city to their jobs on these modes. For example, someone who lives in Rogers Park may ride their bike to work in Evanston.
  • The URL is a permanent link to this dataset. Each city has a unique URL. You should save these as bookmarks so you can easily reference the data later.
  • The question on the survey doesn’t allow multiple choices: “People who used more than one means of transportation to get to work each day were asked to report the one used for the longest distance during the work trip”.

Policy insight for Monday, August 1, 2011

This isn’t refined. These are just my notes that I speak from. I may not have spoke about everything written here and I may not have written here everything I spoke about. This is for Moving Design

There was report of cyclist crashing on the Tuff Curb at the on-street bike parking facility in Wicker Park.

Installing the Tuff Curb

experimental projects need reviews. I don’t mean projects that are considered experiments, I mean projects that are new to the people who designed it, and new to the people who will be using it.

we need good data collection.

Did the Kinzie bike lane cause congestion? So what if it did?
We would need data points that were collected using well-known methods, and probably at different times of the day and week. And we’d have to be sure to count cyclists, too.
Then 3, 6, or 12 months later, we’d have to do it again.

What was the change?
Is that a change that meets our goals?

Back to the cyclist crashing on tuff curb, what is the city’s plan to monitor the use (or disuse) of the facility? How will the city collect data on something like this?

Census – not gonna happen in 2020
American Community Survey – 5-year estimates (with data gathered annually) will replace decennial Census.

“Here are a few Streetsblog posts about Census and NYC DOT’s bike counts, and the problems with each. The first post has some stuff about what could be done to improve on them:” (Ben Fried, Editor in Chief, Streetsblog NYC)

http://www.streetsblog.org/2010/04/27/how-many-new-yorkers-bike-each-day/
http://www.streetsblog.org/2010/10/01/did-nyc-bike-commuting-decrease-in-2009-thats-what-the-census-says/
http://www.streetsblog.org/2011/04/13/actually-if-you-build-it-they-will-bike/

Read more policy insights from Steven Vance.