Data science for urban equity: Making gentrification an accessible topic for data scientists, policymakers, and the community

TL;DR

This paper discusses a data science project focused on understanding gentrification and urban inequity in Seattle, integrating interdisciplinary perspectives and visualization tools to inform policymakers and communities.

Contribution

It presents a case study of applying data science to analyze gentrification and urban inequality, making these topics more accessible for diverse stakeholders.

Findings

Insights into gentrification patterns in Seattle

Enhanced visualization tools for urban equity issues

Interdisciplinary approach connecting data science with social sciences

Abstract

The University of Washington eScience Institute runs an annual Data Science for Social Good (DSSG) program that selects four projects each year to train students from a wide range of disciplines while helping community members execute social good projects, often with an urban focus. We present observations and deliberations of one such project, the DSSG 2017 'Equitable Futures' project, which investigates the ongoing gentrification process and the increasingly inequitable access to opportunities in Seattle. Similar processes can be observed in many major cities. The project connects issues usually analyzed in the disciplines of the built environment, geography, sociology, economics, social work and city governments with data science methodologies and visualizations.