There exists a huge gentrification issue right now in Bay Area communities such as East Palo Alto, San Francisco, and Berkeley - newcomers are forcing out longtime residents and changing the character and culture of these communities. We wanted to approach this issue by informing users of the potential for gentrification in their own neighborhoods, and empower them to write about what they feel that their community represents. We hope that Divercity allows users to grapple with this ethical issue with a broadened understanding of communities across the U.S.

What it does

We built a web app that uses machine learning to estimate the gentrification index of a city given educational attainment and household income, and then displays the metrics to the user, along with an interactive comment feed.

How we built it

For the machine learning, we used a logistic regression model to learn how demographic features predict gentrification index. We focused on the features of educational attainment and household income because these are the ones that are most impactful in research. For the web app, we used JavaScript, HTML/CSS.

Challenges we ran into

A challenge we ran into is how complex and intersectional the problem of gentrification is. There are so many factors that affect cities and the that live in them. We recognize that it is impossible to sum up a city with a few features, and our model is a prediction rather than a prescription.

Accomplishments that we're proud of

We’re proud of how much effort we put into making the webapp user-friendly and intuitive. We’re also proud that our model is functional given how complex of an issue gentrification is to understand and quantize.

What's next for Divercity

We’re looking to develop more comprehensive algorithms and collect more data. We’re like to use more features and perhaps deeper networks if we can access more data. In addition, it would be awesome to better use Divercity to spread awareness about other overlooked issues similar to gentrification in cities.

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