Inspectors General

A project to collect reports from the offices of Inspectors General across the federal government.

Done so far:

• Department of Defense
• Department of Energy
• General Services Administration
• US Postal Service
• Department of Homeland Security, which includes:
  • Secret Service
  • Federal Emergency Management Service (FEMA)
  • Transportation Security Administration (TSA)
  • Immigration and Customs Enforcement (ICE)
  • Customs and Border Protection (CBP)
  • Citizenship and Immigration Services (CIS)
  • Coast Guard
  • (and others)
• Office of Personnel Management (OPM)
• Environmental Protection Agency (EPA)
• Department of Justice
  • Federal Bureau of Investigation (FBI)
  • United States Marshals Service (USMS)
  • Drug Enforcement Administration (DEA)
  • Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF)
  • (and others)
• Amtrak
• Export-Import Bank

Thanks to Matt Rumsey for compiling a spreadsheet of IG offices. The left-hand column tracks who's completed what IG. A yellow highlight means it's a high priority.

Eric Mill has written a piece explaining what the project is and why it's being done. From it:

What's an inspector general?

Just about every agency in the federal government has an independent unit, usually called the Office of the Inspector General, dedicated to independent oversight. This includes regular audits of the agency's spending, monitoring of active government contractors and investigations into wasteful or corrupt agency practices. They ask tough questions, carry guns and sue people.

Scraping IG reports

Python 3: This project uses Python 3, and is tested on Python 3.4.0. If you don't have Python 3 installed, check out pyenv and pyenv-virtualenvwrapper for easily installing and switching between multiple versions of Python.

Dependencies: You'll need to have pdftotext installed. On Ubuntu, apt-get install poppler-utils. On OS X, install it via MacPorts with port install poppler, or via Homebrew with brew install poppler.

To run an individual IG scraper, just execute its file directly. For example:

./inspectors/usps.py

This will fetch the current year's reports from the Inspector General for the US Postal Service and write them to disk, along with JSON metadata.

If you want to go back further, use --since or --year to specify a year or range:

./inspectors/usps.py --since=2009

If you want to run multiple IG scrapers in a row, use the igs script:

./igs

By default, the igs script runs all scrapers. It takes the following arguments:

• --safe: Limit scrapers to those declared in safe.yml. The idea is for "safe" scrapers to be appropriate for clients who wish to fully automate their report pipeline, without human intervention when new IGs are added, in a stable way.
• --only: Limit scrapers to a comma-separated list of names. For example, --only=opm,epa will run inspectors/opm.py and inspectors/epa.py in turn.

Using the data

Reports are broken up by IG, and by year. So a USPS IG report from 2013 with a scraper-determined ID of no-ar-13-010 will create the following files:

/data/usps/2013/no-ar-13-010/report.json
/data/usps/2013/no-ar-13-010/report.pdf
/data/usps/2013/no-ar-13-010/report.txt

Metadata for a report is at report.json. The original report will be saved at report.pdf (the extension will match the original, it may not be .pdf). The text from the report will be extracted to report.txt.

Common options

Every scraper will accept the following options:

• --year: A YYYY year, only fetch reports from this year.
• --since: A YYYY year, only fetch reports from this year onwards.
• --debug: Print extra output to STDOUT. (Can be quite verbose when downloading.)

Contributing a Scraper

The easiest way is to start by copying scraper.py.template to inspectors/[inspector].py, where "[inspector]" is the filename-friendly handle of the IG office you want to scrape. For example, our scraper for the US Postal Service's IG is usps.py.

The template has a suggested workflow and set of methods, but all your task needs to do is:

• start execution in a run(options) method, and
• call inspector.save_report(report) for every report

This will automatically save reports to disk in the right place, extract text, and avoid re-downloading files. options will be a dict parsed from any included command line flags.

You will also need this line at the bottom:

utils.run(run) if (__name__ == "__main__") else None

It's encouraged to use inspectors.year_range(options) to obtain a range of desired years, and to obey that range during scraping. See an example of creating it and using it.

Scrapers are welcome to use any command line flags they want, except those used by the igs runner. Currently, that's --safe and --only.

Report metadata

The report object must be a dict that contains the following required fields:

• inspector - The handle you chose for the IG. e.g. "usps"
• inspector_url - The IG's primary website URL.
• agency - The handle of the agency the report relates to. This can be the same value as inspector, but it may differ -- some IGs monitor multiple agencies.
• agency_name - The full text name of an agency, e.g. "United States Postal Service"
• report_id - A string usable as an ID for the report.
• title - Title of report.
• url - Link to report.
• published_on - Date of publication, in YYYY-MM-DD format.

You can include any other fields you think worth keeping.

The report_id only needs to be unique within that IG, so you can make it up from other fields. It does need to come out the same every time you run the script. In other words, don't auto-increment a number -- if the IG doesn't give you a unique ID already, append other fields together into a consistent, unique ID.

Finally, err towards errors: have your scraper choke and die on unexpected input. Better to be forced to discover it that way, then for incomplete or inaccurate data to be silently saved.

Public domain

This project is dedicated to the public domain. As spelled out in CONTRIBUTING:

The project is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication.

All contributions to this project will be released under the CC0 dedication. By submitting a pull request, you are agreeing to comply with this waiver of copyright interest.