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FBI Uniform Crime Reports (UCR) is one of the best tools for tracking crime trends in communities nationwide; FBI UCR data for 2014 is now available from the NICAR data library.
Law enforcement agencies around the country voluntarily submit reports to the FBI on what are known as "index" crimes: Murder, nonnegligent manslaughter, forcible rape, robbery, aggravated assault, burglary, larceny-theft, motor-vehicle theft and arson. These crimes are meant to serve as an index for gauging fluctuations in both the overall volume and rate of crime. The data include the number of crimes by agency and by month. Geographic information include region, state, county, city, and metropolitan statistical area (MSA).
UCR data can help you track different crimes in your community, as well as crime rates over time. Peruse IRE tipsheets for help:
Please read the NICAR documentation carefully before using this data, and consult the FBI website for more information.
The most recent reports on alleged campus crime, arrests, discipline and hate crimes reported for 2014 are now available in the NICAR data library. Buy it here.
The Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act is a federal law that requires colleges and universities to disclose certain timely and annual information about campus crime and security policies. All public and private institutions of postsecondary education participating in federal student aid programs under Title IV of the Higher Education Act of 1965 are subject to it. The information contained in each report is based on the calendar year (Jan. 1 through Dec. 31) in which the crime was reported to campus officials. We've compiled and cleaned the data.
Data is available for 2001 through 2014. For more information, check out the NICAR Readme file and the record layouts (.xls format) for the data tables.
The older years of campus crime data (2001 - 2009) are available in DBF format. The newer years (2010 - 2014) are in CSV format.
For the 2010 - 2014 data NICAR combined some of the tables to make the data easier to analyze. Prior years have 16 tables whereas 2014 only has eight: crime, arrests and discipline reports for each location type (e.g. oncampus) are grouped together. Take a look at our sample (.xls format), and read the documentation for more information. We are working on standardizing prior years as well.
You can use the data ​to look at reports over time, by campus. You can also find out how well your local institute of higher education is reporting its crime statistics as required under the Clery Act.
The IRE Resource Center offers tipsheets, stories and audio that will help you cover accountability in higher education:
- "Campus coverage: Sexual assault and other crimes on campus" offers tips for covering crime in higher education. You can also download the audio of the panel, which features Walt Bogdanich, David Donald, Jill Riepenhoff and Samantha Sunne.
- Read and listen to our blog post, "AUDIO: The FERPA exception every reporter should know," which offers tips on reporting around The Family Educational Rights and Privacy Act (FERPA).
- Read Samantha Sunne's story "Sex offenses on campus" and her submission to the 2013 IRE Awards, which was a finalist in the student category.
- In "Accountability Reporting in Higher Education", Kristen Lombardi talks about finding sources and data, and points to a toolkit developed by the Center for Public Integrity for covering campus crime. See the Center's submission to the 2009 IRE Awards, which was a finalist for the FOI Award.
The entire database (all years) costs $25 for IRE members, $75 for all others. To purchase, visit https://www.ire.org/nicar/database-library/databases/doe-campus-crime/ or contact the Data Library staff at (573) 884-7711 or datalib@ire.org.
NICAR’s Small Business Administration (SBA) Disaster Loans data is now updated with loans approved through February 18, 2016.
The disaster loans are the only form of SBA assistance not limited to small businesses; the loans help homeowners, renters, businesses and nonprofit organizations finance their rebuilding after a federally-declared disaster has destroyed their property. The data contains information on the borrower, including name and address (although not necessarily the address of the damaged property); whether or not the damaged property is a home or business; the date and total amount approved for the loan; and the declared disaster that caused the damage.
Journalists use the data to follow money that flows into a community hit by a disaster, to track trends, to identify those who received loans in their community and find starting points to investigate disaster spending. You can also uncover what types of businesses had the most post-disaster loan approvals and determine how much rebuilding is planned.
To order:
You can purchase the entire data set online or by contacting NICAR at datalib@ire.org or 573-884-7711
Disaster loan stats:
Now updated for 2015, the National Bridge Inventory database can help you assess the soundness of bridges in your area. Journalists can use the data to investigate bridges by identifying those with structural problems, or that haven't been recently inspected. Other key fields include average daily traffic and overall sufficiency rating. The records represent information from the most recent inspection for each bridge (which could be several years ago or the current year).
NICAR gets this data from the Federal Highway Administration website and converts some fields to make them easier to use, including converting latitude and longitude to decimal degrees. Previous years of data also are provided in a standard text format, and we include an importing guide for various database managers.
We provide bridge data going back to 2004; you can contact us for previous years going back to 1994.
You can purchase the entire data set online or by contacting NICAR at datalib@ire.org or 573-884-7711
NICAR bridge data helps The Seattle Times do analysis on deadline after bridge collapse
Many US bridges old, risky and rundown (Associated Press): An AP analysis of 607,380 bridges in the most recent federal National Bridge Inventory showed that 65,605 were classified as "structurally deficient" and 20,808 as "fracture critical." Of those, 7,795 were both - a combination of red flags that experts say indicate significant disrepair and similar risk of collapse.
Following the Money: Rural bridges targeted (Milwaukee Journal Sentinel): The Milwaukee Journal Sentinel found that a vast majority of the Wisconsin bridges awarded $15.8 million of construction money in the first wave of federal stimulus funding carry fewer than 1,000 vehicles a day. A dozen of those get less than 100 cars a day. For the story, Poston mined National Bridge Inventory data available at the NICAR database library.
Coverage of collapsed bridges and dam failures: Matt Jacob, The Dallas Morning News, and Alex Richards, Chicago Tribune, discussed the National Bridge Inventory database during the 2014 CAR Conference. Read a summary of their talk or download the audio (IRE members only).
We've updated our simple, one-table database on fatal accidents in the US; the data now has information on every reported accident from 2003 through 2014.
We created the data from the full DOT Fatality Analysis Reporting System database (FARS), which has 19 tables and almost 800 columns of detailed information about fatal car accidents. The simplified version will save you time and give you the essential information you need to report on trends in your state, county or city. Use it to quickly report stories or add depth to deadline reporting.
You can purchase the data and view the documentation here. You can download the data as a .CSV or Microsoft Excel spreadsheet. If you'd like a state slice, just contact us and we'd be happy to provide one.
Please read the documentation before using the data: it contains important information about how to analyze and cite the data accurately.
FARS records only include fatal accidents. If you want to broaden your story to include minor crashes, you can request records from your local police department. In some states, crash records are available on a highway patrol’s website. For example, the Missouri State Highway Patrol website generates downloadable records on Excel spreadsheets based on your selection of date, time, crash severity, crash type, county and city.
If you have questions about the FARS database, please contact NICAR Data Library Director Liz Lucas at liz@ire.org.
How many people die in car accidents?
Sometimes you'll see the answer to that question flashing on a billboard on the highway. It's also in the DOT's Fatality Analysis Reporting System (FARS) database, a census of fatality accidents on public roads in all fifty states and the District of Columbia. You can use the data to find out where accidents occurred, and answer questions such as: How many people were involved? Were they wearing seatbelts? How many hit and runs occurred in my area? Were drugs or alcohol involved? Did the driver have a history of traffic offenses or license suspensions?
The FARS database consists of eighteen tables and more than 500 fields that include a wealth of detail about all the vehicles and persons involved (not only drivers and passengers, but also cyclists and pedestrians), and the conditions that led to the accident (as reported by the police on scene). Crashes involving trucks and trailers are also included.
The NICAR Data Library has updated FARS to include reports through 2014, the most recent year available, going back to 1975, when FARS was first established. IRE members can purchase the data online: https://www.ire.org/nicar/database-library/databases/dot-fatality-analysis-reporting-system-fars/. Non-members should contact the Data Library staff at datalib@ire.org or (573) 884-7711.
Before purchasing, you can read the NICAR-created Readme file and download the layout for the main tables.
You can also get a simplified version of the FARS data in a single table; we'll be releasing the newest data in simplified form very soon.
For story ideas and more, browse the IRE tipsheets:
Contact the Data Library for more information: email us at datalib@ire.org or call (573) 882-1982.
Which businesses in your area are receiving loans backed by the U.S. Small Business Administration (SBA)? What financial institutions are making those loans? How many businesses receiving loans in the past 10 years have stayed open?
These are questions you can answer with the SBA's 7a loan data, now available at NICAR. This update database includes loans approved through November of 2015. Journalists can also use 7a data to explore repayment of SBA loans by local businesses and find out what types of businesses are getting the loans. For example, the most common franchise to receive SBA 7a loans is Subway. The data can also help you investigate how the SBA works with state and local agencies to lend money to small businesses.
The most current data covers loans from 1990 to November 30, 2015. The SBA 7a database includes the name and address of the business getting the loan, franchise and industry codes, the bank lending the money, the amount loaned, and (where applicable) whether the loan was paid in full or charged off. Visit the database page to buy the data or read the documentation.
ABOUT THE 7A PROGRAM
The 7a program is the SBA's most common loan program. It provides loans to small business owners who can't obtain financing through traditional channels. The program operates through private-sector lenders who provide loans that are, in turn, guaranteed by the SBA. The SBA 7a program itself has no funds for direct lending or grants.
DATA FORMAT
The SBA 7a dataset comes in one tab-delimited text file, easily imported or linked in Access and other database managers. If you'd like something different, we'll do our best to help you out. Give us a call at 573-884-7711 or email datalib@ire.org.
GETTING THE DATA FROM THE SBA
Every fall the NICAR Data Library goes through a somewhat lengthy process to acquire the 7a data from the SBA. Although the SBA distributes the data in Excel (a pretty straightforward format compared to what we're used to), the FOIA usually takes months and we often have to appeal a denial of our request for a fee waiver. The SBA normally charges $250 for the data. We've always been granted a waiver, but that doesn't stop the SBA from trying to deny it year after year.
Police typically conduct sober checkpoints around the holidays, especially New Year’s Eve. Add context to your stories about holiday safety and travel by looking to look at trends in fatal crashes where drugs and alcohol were noted as a factor.
We’ll walk you through how to quickly gather statistics and come up with story ideas using our simplified DOT Fatality Analysis Reporting System database (FARS).
[This is the third and final installment of a blog post series on how to use FARS Simplified. Our first post included story ideas on pedestrian and shopper safety, as well as the worst holiday travel times. The second post explained how to look at dangerous winter weather conditions.]
In the example that follows, we’ll use data on the state of California to look at fatal crashes around New Year’s.
1. Create a Pivot Table with “HOLIDAY” as the Row Labels and “COUNT of NICARID” as the Values. This will show you how many fatal crashes took place on each holiday. Filter to narrow your results down to just the holidays or holiday window you’re interested in. For this example, we just want to look at New Year’s. We can see there were 144 fatal crashes on New Year’s Day in California between 2003 and 2013.
2. Now let’s see how many of those involved a driver that might have been under the influence of alcohol*. Double-click on the number 144 to generate a new spreadsheet showing just the New Year’s Day crashes. On this new spreadsheet, find the NICAR_ALCOHOL column and use the Filter to show anything greater than or equal to 1. This will narrow your results down to any time one or more drivers was possibly under the influence of alcohol. We can see that 69 of the 144 New Year’s Day crashes might have involved alcohol. You could run a similar analysis by making a Pivot Table with “NICAR_ALCOHOL” as the Row Labels, “COUNT of NICAR_ALCOHOL” as the Values.
You can also run a similar analysis using NICAR_DRUGS.
* An important note: The NICAR_ALCOHOL and NICAR_DRUGS fields are based on observations of police officers at the scene of the crash. It does not mean the drivers were considered legally drunk or reflect official testing. You’d need to do additional reporting to confirm specific cases
About the database:
Our full DOT Fatality Analysis Reporting System database (FARS) has 19 tables and almost 800 columns of detailed information about fatal car accidents. To save you time, we created a single-table database that gives you the essential information for every fatal crash. Use it to quickly report stories or add depth to deadline reporting. The data covers 2003-2013 (the most recent year available) and comes pre-loaded in Microsoft Excel. Purchase the simplified data set, and you’ll be able to download data for the entire country, just a state, or both.
You can purchase the data and view the documentation here. Please read the documentation before using the data: it contains important information about how to analyze and cite the data accurately.
If you have questions about the FARS database, please contact NICAR Data Library Director Liz Lucas at liz@ire.org.
NICAR is adding a review of data tools to its online resources, courtesy of Stanford journalism students.
Nineteen journalism and computer science students paired up in a new, 10-week data journalism class with the goal of exploring how programming techniques can assist in investigative reporting. The students analyzed documents and data in various formats, from PDFs to messy Excel spreadsheets.
After 10 weeks of experimenting with a myriad of data tools, the team wrote up their experience as a guide for others, covering various techniques from web scraping to mapping to visualization. They discuss pros, cons and opinions on how easy each program or software package is to use.
Check out their review here: https://www.ire.org/nicar/database-library/stanford-review-tools/
Nathaniel Lash
Nathaniel Lash is a data reporter for the Tampa Bay Times. He was part of a team of journalists who worked on "Failure Factories," a series that explored how five Pinellas County schools became some of the worst in Florida.
How would you describe your job?
I work at the Tampa Bay Times as a data reporter, where I develop and use technology to conceive of, report out and publish stories. Right now, I work mostly with members of the investigative team on their data-heavy projects, which involves acquiring, cleaning, connecting and analyzing data, as well as visualizing it for the web.
How did you get started in data journalism?
The summer before my sophomore year at the University of Illinois at Urbana-Champaign, I was lucky enough to attend an IRE Watchdog Workshop, which sparked an awareness of what kinds of records public agencies collect and maintain. It proved useful pretty quickly. That fall, news broke of "possible inaccuracies" in how the university’s law school reported student test scores, the benchmark of a law school’s quality (and rank). While the university’s investigation was ongoing, we requested the raw test scores for students admitted to the law school, and saw that the highly coveted median LSAT scores been inflated for years. Before we could publish — the day after we got the records — the university released findings that exactly matched ours. I’ve taken data journalism pretty seriously since then.
That got me working with data for a bit, but the only reason I was able to get into writing code and working with databases is thanks to Michael Corey and Augie Armendariz, who brought me under their wing at the Center for Investigative Reporting for a summer and taught me how code fits with investigative journalism.
What are your go-to tools and programs when working on a story that involves data?
For any story that involves data, the tool at the top of my list is experts. A lot of the time that means those who work either at the source of the data or as researchers. But another frequently untapped source is the people who deal with the real-world bits of that data all the time: reporters in your newsroom.
As far as technology is concerned, I do most of my data analysis in iPython notebooks, which makes working with the data analysis package pandas a breeze. It also makes on-the-fly visualization a lot simpler — one of my favorite libraries for that is Seaborn, which simplifies the process for a lot of standard statistical visualizations.
For projects like the Failure Factories series, we had to maintain and connect a lot of different datasets. For those kinds of tasks, I usually turn to something like Django, a Python web framework that makes managing, analyzing and publishing data much easier.
Still, as great as some of these tools are, nothing beats Excel when I need an answer from smaller datasets quickly.
"For any story that involves data, the tool at the top of my list is experts."
At what point in the reporting process for “Failure Factories” were you brought on to create the graphic?
Reporters Michael LaForgia, Cara Fitzpatrick and Lisa Gartner, and data specialist Connie Humburg had been requesting documents and records from the state’s department of education and the school district for eight months before I arrived at the Times as an intern in January. About a month after I started, I began switching between contributing analysis on the project and developing graphics until the stories started running in August.
Communication is key when working in a group, so how did you keep all the reporters working on various components of the story on the same page?
We work together! Cara and Lisa, education reporters who ordinarily worked on the other side of the newsroom, physically moved their desks over to the investigative team’s pods, and the data team — the group of data specialists, designers and developers to which I belong — works adjacent to that team. There was a constant back-and-forth between the other reporters and me, as well as the rest of the data team.
This interview has been edited for clarity. Interview by Maggie Angst.
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