Better Neighborhoods, Better Cities: Integrating Information and Action at the Neighborhood Level | NYU Tandon School of Engineering

Better Neighborhoods, Better Cities: Integrating Information and Action at the Neighborhood Level

Community,
Urban


Project Sponsor:

Tim Tompkins, NYU Marron Sustaining Places Initiative


Authors

Haoning Li, Yuancheng Xu, Haodong Ren


Research Question

How can neighborhood sentiment, government actions and outcomes be integrated into a single place-based performance measure that informs and improves local services? The project builds a prototype Neighborhood Dashboard, using New York City as a testing ground for a template that could reorient service delivery in municipalities everywhere.


Background

It has been said that what gets measured gets done, and that how we organize information is how we organize institutions. Over 50 years ago, Jane Jacobs wrote about the frustration of city government silos and the fragmentation of services at the neighborhood level. A key challenge for government, residents, philanthropy and nonprofits alike is linking the right indicators of neighborhood sentiment (what residents feel needs addressing), government actions (inputs and outputs), and outcomes (results).

The project builds on a prior capstone, "Augmenting and Accessing Neighborhood Data to Enhance Neighborhood Services," and draws on past successes such as NYPD's COMPSTAT in the 1990s and DOT's Vision Zero in the 2010s.


Methodology

The project involves:

  • Building on the prior capstone's data work to prototype a Neighborhood Dashboard.
  • Engaging from the start with experts from government and the field to identify relevant data.
  • Shaping a beta neighborhood and place-based performance metric that links sentiment, government inputs and outputs, and outcomes.

Deliverables

  • Interactive data dashboard
  • Academic paper
  • Data visualizations
  • Policy memo
  • Policy recommendations
  • Project website

Data Sources

  • Datasets will be identified with government and field experts and build on data from a prior capstone.