Data-driven neighborhood-characterization method using publicly available census data¶

Element · All elements
Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

A data-driven method for characterizing neighborhoods that leverages publicly available census data and allows researchers to consider many neighborhood characteristics simultaneously. In Chicago it produced a set of five neighborhood groupings that the authors describe as "relatively easy to describe and are easily understood by those familiar with Chicago". The method is offered as an example for cities, school districts, and policy makers.

Design Implications¶

Context¶

Requirements¶

• Publicly available census data covering the neighborhood characteristics of interest

Constraints¶

• The groupings reflect the local city's segregation patterns and inequities, so they are specific to the place analyzed

Target Learners¶

• Policy makers and school district researchers studying early childhood education access

Target Learning Goals¶

• Characterizing neighborhoods to inform equitable allocation of pre-k services and resources

Claims¶

• Students in census tracts with unaffordable housing, low home ownership, or little green space have higher odds of grade retention, even with similar achievement [+W]
• Within the same neighborhoods and schools, boys and Black students are more likely to be overage for grade, a proxy for prior retention [+W]

Related Elements¶

Examples¶

• Apply the neighborhood-centered grouping approach in other cities for research, policy decisions, and service allocation

Key Sources¶

• A Neighborhood-Centered Approach to Understanding Pre-K Access in Chicago. (2024). Start Early. https://www.startearly.org/