Methods: Visualization
Fields: Data Science for Social Good, Social Science, Urban Science

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

Example report page for International District neighborhood

Example report page for International District neighborhood

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 dynamicallyasses 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.

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