Smarter City Health Data: Introducing the 500 Cities Project

Health data at the state and county level is easy to find. But historically, city-level health data has been scarce—until now, thanks to the CDC’s 500 Cities project, funded by the Robert Wood Johnson Foundation.

The CDC drew data from its survey, the Behavioral Risk Factor Surveillance System—BRFSS for short—and released estimates at the city level. The data even drills down to individual census tracts.

LiveStories put together a series of stories based on this new data. Six stories—HoustonNew OrleansSan Diego, Santa Monica, Tulsa, and Wichita—focus on a single census tract in each city. A more complex story, for Cleveland, explores the data throughout all the city’s census tracts.  

What the Data Measures

BRFSS includes 28 measures, divided into three categories: (1) health outcomes, (2) prevention, and (3) unhealthy behaviors. The 500 Cities project compiled these measures for the 497 largest cities in the United States (plus one city each in Vermont, West Virginia, and Wyoming to ensure all 50 states are represented).

These measures can vary considerably from city to city, and among census tracts within a city. Outliers in the data may point to inequities in local health care or access to healthy lifestyles, and can thus help governments and nonprofits prioritize and focus their efforts in response.

How We Approached the Data

When translating a large, complex dataset into a story, one of the biggest challenges involves structure. How do you slice up the data that spans multiple regional levels and multiple categories of health data? And how do you create a story structure that effectively guides readers through the data?

We’ve previously discussed our best practices for presenting data and charts with clarity and focus in this blog post. For these stories, we used filters to limit the amount of data each chart presents at once. The result: each chart presents a clear comparison between regions. Any disparities between census tract, city, and national data should be obvious.

Universal filters, like this one, limit which data the charts on the page display. They add interactivity, and help prevent information overload.

Universal filters, like this one, limit which data the charts on the page display. They add interactivity, and help prevent information overload.

Smaller, smarter stories, linked together

Instead of presenting all the data on a single story page, we split it up into multiple story pages linked together. The Cleveland story, for example, spans four separate story pages—a broad landing page and three more detailed pages—each with its own focus.

The Cleveland data story is actually four stories: a landing page and three sub-pages that each focus on a particular category. A simple navigation menu, created with hyperlinked images, links them together. 

The Cleveland data story is actually four stories: a landing page and three sub-pages that each focus on a particular category. A simple navigation menu, created with hyperlinked images, links them together. 

The result of this approach resembles a fully-formed website. If you are trying to tell a story about a complex dataset, we recommending trying this multi-page approach. The key is knitting your individual story pages together cohesively. Fortunately, it’s easy to link together individual story pages on the LiveStories platform. You can use simple button modules, or add hyperlinks to images. (We use both buttons and image links in the Cleveland story.)

You Can Use These Stories

These stories are just starting points for exploring the treasure trove of the 500 Cities Project. Like other stories we create in-house, partners are free to copy them into their own accounts and personalize them for their own purposes and localities. (Learn how to copy stories here.) We hope the stories will serve you and your communities well.

Additional examples, focused on a single census tract: