Always Scared

2023 Winner

Entry type: Single project

Country/area: United States

Publishing organisation: Streetsblog

Organisation size: Small

Publication date: 2022-05-24

Language: English

Authors: Jesse Coburn

Biography:

Jesse Coburn is Streetsblog’s investigative reporter. His reporting on transportation issues in New York City has been recognized by the Sigma Awards, the Silurians Press Club, the Online Journalism Awards, and other organizations. Coburn previously was a reporter for Newsday, and before that he was an assistant editor at ARCH+. He has also written for the New York Times, Harper’s, Foreign Policy, the Baltimore Sun, and other publications.

Prize committee’s comments:

This investigation into dangerous accidents near New York City schools pulled in data from nearly one million car crashes over more than five years, logging every collision within 250 feet of a city-run public school. The findings not only revealed the dangerous journey to school each day by thousands of children, but also that the streets near schools serving poor students and students of color are seeing even more accidents. All of this despite millions spent by the city to make school streets safe. What’s stunning about the work here is the size of the outlet – Streetsblog – a small, nonprofit, with just one person on the byline. Yet Streetsblog leveraged the power of available public data and invested in a deep dive into understanding what was putting children in harm’s way and told a compelling story along the way.

Project description:

In this six month investigation into traffic violence around schools, Streetsblog built a spatial database of nearly one million car crashes in New York City, which revealed alarming rates of crashes and injuries outside city schools, especially those serving majority poor students and students of color. Through extensive reporting across the five boroughs, the story also showed how the city’s efforts to reduce the dangers posed by cars outside schools have been insufficient, leaving children vulnerable in places where they have no choice but to be.

Impact reached:

Following the release of this story:

The city Department of Transportation announced plans to redesign streets outside schools in poor communities of color to make them safer.
The governor of New York cited the story’s findings when signing a bill to expand a school-zone speed camera program in New York City.
The mayor of New York City and the city’s top transportation official said the city must do more to keep children safe from cars outside schools.
Local lawmakers expressed outrage about the story’s findings and called on the city to do more to protect children from cars outside schools.

Techniques/technologies used:

This story is built on an analysis of more than a dozen datasets and other data sources published by New York City, most on its open data portal. Using PostgreSQL, PostGIS, QGIS, and Excel, Coburn merged these disparate data sources into a novel database that listed every car crash near a New York City public school since July 2015—excluding the first 15 months of the pandemic, when city students were largely learning remotely—as well as information on the enrollment and demographics of each school. This yielded startling insights into the dangers that children face navigating city streets on their way to and from school every day.

To begin, Coburn acquired and cleaned datasets on: car crashes in the city; the locations, enrollment and demographics of more than 1,000 city schools across six school years; and school calendars, which Coburn had to scrape from PDFs. He then wrote a 70-line Postgres query to join all this data into a single database. For every car crash in the city since July 2015, excluding the early pandemic period, the database indicates whether the crash occurred within 250 feet of a school, whether it occurred during student arrival and departure hours, and how many poor, non-white, and total students were enrolled in each school at the time.

The database required extensive cleaning before it could be analyzed. For example, Coburn had to manually reclassify crashes that occurred near schools but posed no threat to pedestrians or cyclists, such as those on grade-separated highways. Finalizing the data took weeks.

Once finished, this database led to previously unknown discoveries about the extent of traffic violence around schools, including that there are 57% more crashes and 25% more injuries per mile on school streets than other city streets at 8 a.m. on school days, and that injury rates from car crashes are 43% higher outside majority Black or brown schools than majority-white schools.

Coburn also created interactive maps for the story using Tableau.

Context about the project:

New York City agencies were hostile to Streetsblog’s efforts to examine the threat that cars pose to children outside city-run schools. The city departments of transportation and education declined to make any officials available for interviews or to answer many questions for the story. The Department of Education even kicked Streetsblog out of a press conference instead of answering basic questions about the dangers children face walking to and from city schools (link: https://nyc.streetsblog.org/2022/09/29/department-of-education-kicks-streetsblog-out-of-media-event-instead-of-answering-question-on-school-street-safety/).

Streetsblog is a small non-profit newsroom. This was the largest project Streetsblog has even undertaken.

What can other journalists learn from this project?

This story demonstrates that seemingly unrelated datasets can contain important, newsworthy stories when joined together. By looking at the nexus of traffic violence and education, Streetsblog uncovered previously unknown dangers that cars pose to children outside their schools.

The story also shows how injustice can hide in even commonplace activities. Upon first glance, there may appear to be little of journalistic interest about the daily routine of children walking to and from school. Closer scrutiny showed the topic was beset by problems and inequities that deserved public attention.