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CounText

CounText is a digital tool that allows the user to enter any county in the US, and the program will output three demographically similar counties

CounText Screenshot.jpg

THE CHALLENGE

How might we help journalists avoid tunnel vision and understand how relevant their data is when compared to other demographically similar areas?

USER: 
Sarah: a local journalist based in Chicago, reports primarily on health & public safety including COVID coverage

Wants to...

  • Find story ideas faster

  • Understand COVID on a local level

  • Beat competitive outlets to uncovering new stories

Challenges...

  • Time management

  • Decreasing resource

Use Case

Sarah is looking for more stories to cover concerning COVID on a local level in the Chicago area. She wants to follow the numbers to see if they change with Omicron surges happening nationally - and why they do or do not.

Sarah is able to look at Cook County’s data and compare it to “demographically similar” locales. She is able to identify patterns, inconsistencies, and outliers in the data. This can help her to uncover stories and issues that wouldn’t be identified otherwise. 

For example: Logically, two demographically similar areas should have similar COVID case rates. If they don’t, what is causing that difference?

My role:

  • Product manager for a team of engineers

  • UX Researcher

  • UX Designer

  • Content Strategist

  • Copywriter

How we did it:

We began the process by interviewing journalists and understanding the pain points they experience when reporting with data on a local level. Once we validated the need for context surrounding data, we began another round of interviews to determine what “statistically similar” means to journalists.

 

From there, we built the brand, voice, design, and user flow of CounText, while iterating through many rounds of feedback. 

 

Using an API, CounText ​​receives requests sent by the frontend and fetches county data from the US Census Report, applying a k-nearest neighbors approach and returning it to the React interface. The data sets used (determined through user testing) are population, income level, education level, average home price, and racial and ethnic diversity.

 

Once the front and back end were set up, we completed another round of user testing to ensure a logical user flow and clear value proposition.

Skills gained:

  • Agile development

  • Mockup design

  • Communicating with stakeholders

  • User testing

  • Constant iterating

  • APIs 

  • Wireframing

  • Basic HTML & CSS

  • Data analysis & research

  • Brand strategy

  • Copywriting

Presentation:
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