Assessing Community Well-being through Open Data and Social Media

Project Lead: Shelly Farnham, Third Place Technologies

DSSG Fellows: Jordan Bates, Ryan Burns, Jenny Ho, Yue Zhou

ALVA Students: Avery Glass, Jennifer Nino

eScience Data Scientist Mentors: Bernease Herman, Bill Howe

Our DSSG Fellows and ALVA students paired with Third Place Technologies to create neighborhood community report pages in the context of a hyperlocal, crowd-sourced community network. The objective was to help neighborhood communities better understand the factors that impact community well-being, and how they as a neighborhood compare with other neighborhoods on these factors. This helps them set the agenda for what to prioritize in promoting their well-being. A key aspect of this project is to explore novel ways to leverage diverse social media and open data sources to dynamically asses community-level well-being, in order to a) enable early identification of emerging social issues warranting a collective response, and to b) automatically identify and recommend the local community hubs best positioned to coordinate a community response.

During the Data Science For Social Good program, the group accomplished the following:

1) Collecting and processing diverse hyperlocal social media (e.g., Twitter, Facebook, Instagram, Yelp) and open data sources (e.g., Census data, crime data) to develop community well-being measures.

2) Representing these metrics to end users (neighborhood community members) in neighborhood report pages, which included visualizations that represent neighborhood well-being across neighborhoods.

Example report page for International District neighborhood

To read more about the project, view our other post on working in an interdisciplinary group.