Bayes Hack 2016 |
Department of Housing and Urban Development,
Presented by Bloomberg and What Works Cities.

How can data help communities thrive?

Housing inequality is present in cities across the United States, rendering low income families unable to obtain affordable housing. Lack of fair housing opportunities is just one of the problems communities face: many people also lack access to transportation and services within the community. Both communities and residents suffer when specific populations cannot utilize all the resources communities have to offer. National and local data sets have been created by initiatives that are addressing the gaps between residents in communities.

Help cities enhance their use of data and evidence to uncover new ways to revitalize neighborhoods and improve the lives of residents. Leverage federal and local open data to identify disparities in access to resource, services, and housing that communities need to thrive. Interactive informative tools that show current trends, or tools to illustrate federal and local spending or regulatory changes, have the potential to reinvent the way communities come together and grow.



Resources

  • The Bayes Impact starter kit, an exploration of this prompt's key datasets.
  • Brand new, and compiled specifically for Bayes Hack: DataSF just released a series of new datasets related to housing equity and neighborhood quality of life!
  • Also brand new HUD Affirmatively Furthering Fair Housing data reatures six community asset indicators: Neighborhood School Proficiency, Poverty, Labor Market Engagement, Job Accessibility, Health Hazards Exposure, and Transit Access.
  • The Opportunity Project combines additional federal and local open data to determine access to opportunity at neighborhood granularity.
  • White House Community-Based Initiatives Data features datasets from dozens of community empowerment projects.
  • There are also a number of local data portals that might be useful for particular urban areas. For example: San Francisco, CA; Kansas City, MO; Seattle, WA; and Tacoma, WA.
  • Yelp reviews offer an organic gauge of neighborhood quality of life that is hard to capture via formal federal data collection. The Yelp API is broadly useful here, though rate limited, but the Yelp Dataset Challenge offers cleaned data in bulk for select cities (including Pittsburgh, Charlotte, Urbana-Champaign, Phoenix, Las Vegas, and Madison), where neighborhood-by-neighbohood geofencing can help you evaluate community well-being.